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Amazon MusicWhy do so many Microsoft 365 Copilot projects fail — even when the prompts look fine?
In this episode, we explain why the real issue is not prompt engineering, but context engineering.
Most AI failures are not model failures. They are context failures. When Copilot lacks structured, governed, and relevant information, it produces inconsistent, low-quality, or misleading results.
Context Engineering goes beyond writing better prompts. It is the systematic design, management, and governance of all information your Copilot has access to — ensuring accurate, stable, and trustworthy outputs.
We break down what context engineering really means in Microsoft 365 environments, why it matters for enterprise AI adoption, and how to design Copilot solutions that scale reliably.
If you want consistent Copilot results instead of AI randomness, this episode is essential.
In today's fast-paced digital world, you face challenges in making AI work effectively for your needs. Context engineering plays a crucial role in enhancing AI performance by providing the right information at the right time. Research shows that shorter, curated contexts can improve accuracy by up to 50%, while longer prompts often lead to confusion. By focusing on context, you can avoid pitfalls like hallucinations and ensure your AI delivers reliable results, making your processes smoother and more efficient.
Key Takeaways
- Context engineering designs the right information environment so AI understands tasks better and delivers accurate results.
- It improves AI reliability by reducing errors like hallucinations and supports complex, multi-step workflows.
- Unlike prompt engineering, context engineering manages what AI knows, not just how you ask questions.
- Key parts include system instructions, relevant data sources, memory, and dynamic workflows that keep information fresh.
- A strong information architecture organizes data for quick access and guides AI behavior to match your goals.
- Context engineering boosts AI accuracy and relevance, saving time and making AI outputs more useful.
- It applies well in enterprises, customer support, and compliance by ensuring AI uses trustworthy and updated data.
- Building context as a product with clear structure, tools, and continuous testing leads to better AI performance.
Understanding Context Engineering
What Is Context Engineering?
You might wonder what context engineering really means in the world of AI. Simply put, it’s about designing and organizing the right information so AI systems can understand your intent and deliver useful, relevant results. Instead of relying on manual prompts every time, context engineering sets up the environment and data flows that help AI make smarter decisions on its own.
According to Gartner, “Context engineering” means “designing and structuring the relevant data, workflows and environment so AI systems can understand intent, make better decisions and deliver contextual, enterprise-aligned outcomes — without relying on manual prompts.”
An industry blog puts it this way: context engineering is the “practice of designing systems that decide what information an AI model sees before it generates a response.” This means you’re not just telling AI what to do with a prompt; you’re shaping the whole information landscape it uses to think and respond.
Key Components of Context
When you think about context in AI, it’s not just one thing. It’s a combination of several parts working together to give AI the full picture. Here are the main components you should know:
System instructions and role definitions
These set the stage by defining what the AI can do, its limits, and its role in your system. Think of it as giving AI a clear job description.Semantic and structural context
This connects the technical data with its real-world meaning. It helps AI understand not just the data itself but what the data represents in your business or task.Data sources
You bring in relevant documents, databases, sensor data, or knowledge bases tailored to your needs. This ensures AI has access to the right facts.Memory
AI remembers past interactions or important details, so it doesn’t start from scratch every time you ask something new.Dynamic workflows
Automated processes fetch, filter, and update context in real time, keeping the information fresh and relevant.
Here’s a quick list to sum it up:
- Data Sources: Documents, databases, sensors, knowledge bases
- Memory: Retaining info across interactions
- Dynamic Workflows: Automated updates and filtering
Systematic Information Architecture
To make context engineering work well, you need a solid system behind it. This is where systematic information architecture comes in. It organizes how AI retrieves data, manages memory, and keeps track of conversations over time.
Think of it like building a well-planned library for your AI. You arrange books (data) by topic, tag them with keywords (semantic tagging), and map relationships between them. This way, AI can quickly find what it needs and understand how pieces fit together.
A strong information architecture also sets clear rules for AI behavior. It guides how AI responds, making sure answers align with your goals and standards. This structure helps AI handle complex tasks and long conversations without losing track.
Effective context engineering depends on balancing relevant data, user preferences, and ongoing interactions. When you get this right, your AI becomes more reliable, consistent, and useful.
Here’s why this matters:
- Organizes data for quick, accurate access
- Supports dynamic, multi-step workflows
- Ensures AI responses match your business needs
| Benefit | Description |
|---|---|
| Reliability and Scalability | Context engineering reduces AI errors and supports consistent behavior. |
| Business Impact | You get higher user satisfaction and faster development cycles. |
| Systematic Evaluation | Continuous improvements boost AI performance over time. |
By focusing on context engineering, you build AI systems that don’t just react but truly understand and assist. This approach moves beyond simple prompt tweaks and creates a foundation for smarter, more trustworthy AI.
Context Engineering vs. Prompt Engineering
Limitations of Prompt Engineering
You might have tried prompt engineering to get better AI results. It involves crafting the perfect question or instruction to guide the AI. While this works well for simple tasks, it often falls short when things get complicated. Here’s why:
- Providing too many examples or details in your prompt can confuse the AI or push it toward patterns that don’t apply broadly.
- The idea that longer prompts always improve results is a common misconception. In fact, shorter, well-structured prompts often perform better.
- Prompt engineering requires constant tweaking. You can’t just set it once and forget it. It demands ongoing optimization to keep up with changing needs.
- It assumes all the relevant information fits into a single prompt, which rarely happens in real-world scenarios.
- When prompts get too long, you face prompt bloat and risk losing important context, leading to unreliable AI behavior.
- Prompt engineering struggles with multi-step workflows or tasks that need memory and reasoning over time.
Note: Prompt engineering shines in quick, single-turn interactions but hits a wall with complex, multi-layered tasks.
Advantages of Context Engineering
Context engineering takes a different approach. Instead of focusing only on how you ask, it focuses on what the AI knows and how it accesses that knowledge. This shift makes a huge difference in AI performance, especially in complex environments.
| Dimension | Prompt Engineering | Context Engineering |
|---|---|---|
| Focus | How you phrase the prompt | What information the AI has access to |
| Key Question | “Did I phrase this well?” | “Does the AI have the right info?” |
| Best for | Single-turn tasks | Multi-step workflows and agentic systems |
With context engineering, you design systems that manage memory, retrieval, and dynamic updates. This means your AI doesn’t just react to a prompt; it understands the bigger picture. It can pull in relevant data from multiple sources, remember past interactions, and follow complex workflows.
Here’s what context engineering brings to your AI:
- It reduces information overload by filtering and organizing data before the AI sees it.
- It supports prompt orchestration, where multiple pieces of information work together smoothly.
- It helps your AI deliver more accurate, relevant, and consistent answers.
- It integrates with tools and policies to keep AI outputs authentic and aligned with your goals.
- It scales better for enterprise needs, handling complex tasks without losing track.
In fact, context engineering emerged as the natural evolution of prompt engineering around mid-2025. It solves many production challenges that prompt engineering alone couldn’t handle. By managing the entire context state, it enables AI systems to operate over multiple inference steps and extended interactions.
Think of it this way: prompt engineering is like asking a single question well. Context engineering builds the whole conversation, the background, and the memory so your AI can truly understand and assist.
Tip: When you focus on context engineering, you reduce errors and improve AI reliability. You also make your AI easier to govern and audit, which is crucial for enterprise success.
By embracing context engineering, you move beyond the limits of prompt wording. You create AI that feels smarter, more natural, and more useful in real-world applications.
Advantages of Context Engineering
Improved Accuracy
When you implement context engineering, you significantly boost the accuracy of your AI systems. By providing structured and relevant information, you help AI models make better decisions. This means fewer mistakes and more reliable outputs. For instance, a coding assistant that receives rich context can cut down the number of iterations needed to find a solution by 60 to 70%. Imagine how much time and effort you could save!
Context engineering transforms LLMs from reactive tools into programmable systems that can reason, retrieve, adapt, and act intelligently across complex tasks.
Enhanced Relevance
Context engineering also enhances the relevance of AI-generated outputs. By supplying structured, precise, and task-specific information, you reduce the chances of the AI guessing or hallucinating. This alignment with your organizational standards ensures that the outputs meet your needs.
- You can expect AI systems to perform complex reasoning and multi-step tasks coherently.
- With the right context, your AI can deliver answers that truly matter to you and your business.
This approach not only improves the quality of responses but also makes interactions smoother and more meaningful.
Prioritization of Contextual Information
Prioritizing what matters is crucial in context engineering. You want your AI to focus on the most relevant information first. Here are some principles to help you prioritize contextual information effectively:
| Principle | Description |
|---|---|
| Clarity | Structure context to eliminate ambiguity. |
| Relevance | Prioritize information critical to the task. |
| Adaptability | Adjust context dynamically based on user needs or task evolution. |
| Scalability | Ensure context pipelines handle growing complexity. |
To prioritize effectively, consider placing the most critical information first and discarding or de-emphasizing low-relevance data. This way, your AI can retrieve what’s relevant and respond appropriately, enhancing overall performance.
By focusing on these advantages, you create a robust framework for your AI systems. Context engineering not only improves accuracy and relevance but also ensures that your AI can adapt and scale as your needs evolve.
Applications of Context Engineering
Enterprise Solutions
Context engineering plays a vital role in enterprise solutions. It helps organizations streamline their AI systems, making them more reliable and effective. Here are some key benefits you can expect:
- Start with high-value, contained domains to show measurable impact.
- Leverage existing metadata and semantic models to build a unified context layer efficiently.
- Focus on tool discovery and execution governance for reliable AI operations.
- Use standards like MCP and Agent2Agent Protocol to enhance governance and security.
By treating context as a strategic capability, you can achieve dramatically better AI outcomes. This approach ensures that your AI systems perform deep research and synthesis across large document sets, improving information retrieval and attribution.
Customer Support Automation
In customer support, context engineering transforms how AI agents operate. It allows them to understand their current tasks and fetch only the necessary information just-in-time from relevant sources. This orchestrated flow of information enables AI agents to act intelligently and efficiently. For example, NVIDIA improved their sales team's efficiency by unifying fragmented internal data. They developed a sales AI assistant that used semantic retrieval to provide relevant information, showcasing how context engineering enhances AI-driven support.
With context engineering, your AI can automate compliance and audit processes, checking documents against regulatory requirements and generating audit-ready reports. This capability not only reduces resolution times but also improves customer satisfaction by delivering accurate and relevant answers.
Data Management and Compliance
Context engineering significantly enhances data management and compliance for organizations. It structures the context that boosts the accuracy and trustworthiness of AI systems. Here’s how it works:
- Curates metadata and maps data lineage for effective decision-making.
- Ensures AI systems access reliable and compliant data, improving risk management.
- Builds and manages the context layer that makes enterprise AI trustworthy.
By ensuring that AI systems receive updated context, you avoid incorrect assumptions and enhance overall performance. This structured approach simplifies integration with legacy systems and supports governance, security, and compliance.
Implementing Context Engineering
Building a Context Framework
To successfully implement context engineering, you need a solid framework. Here are some steps to guide you in building a robust context framework for your AI projects:
- Treat Context as a Product: Implement version control, automated quality checks, and feedback loops. This approach helps you continuously improve context sources.
- Structure and Isolate with Precision: Use consistent formatting to separate instructions, context, and user queries. This prevents interference in multi-agent systems.
- Start with RAG, Fine-Tune Sparingly: Use Retrieval-Augmented Generation (RAG) for dynamic knowledge injection. Reserve fine-tuning for specific skills or reasoning patterns.
- Iterate, Evaluate, and Experiment Relentlessly: Engage in continuous iteration and A/B testing to optimize context configurations.
- Embrace Linguistic Compression: Use precise language in prompts to maximize information density and guide model behavior effectively.
Tools for Context Management
When it comes to managing context effectively, several tools can help streamline your processes. Here’s a list of essential tools you might consider:
- Context Window: Prioritize the most useful content within the model's processing limit.
- Tool Calls: Use clean and summarized outputs from tools to avoid confusion.
- Avoiding Context Bloat: Limit unnecessary content to maintain performance.
- Needle in a Haystack Problem: Highlight and summarize important information to improve retrieval.
- Effective System Prompt: Clearly define the agent's identity and behavior for better performance.
- Taking Prompts Seriously: Treat prompts as core components to enhance system design.
- Analyzing the Prompts: Test variations to improve instructions and system robustness.
- Reranking Strategies: Sort content by relevance to ensure important information is prioritized.
Best Practices for Implementation
Implementing context engineering requires adherence to best practices. Here are some key strategies to ensure success:
- Define Contextual Boundaries: Determine relevant information for tasks to avoid overload.
- Structure and Standardize Context Data: Organize data using standardized formats for easier access.
- Employ Relevance Algorithms: Implement algorithms to dynamically adjust context based on relevance.
- Monitor and Audit Context Usage: Regularly review context elements to identify and remove redundancies.
- Enable Context Updating Mechanisms: Design systems for dynamic context updates based on new data.
- Foster User and Stakeholder Feedback: Create channels for users to provide feedback on context accuracy.
By following these steps and utilizing the right tools, you can create effective context engineering systems that enhance your AI's performance and reliability.
Incorporating context engineering into your AI projects is essential for achieving long-term success. It enhances reliability, accuracy, and consistency by designing the information environment effectively. Here are some key takeaways to consider:
| Key Takeaway | Description |
|---|---|
| Importance of Context Engineering | Enhances AI system reliability, accuracy, and consistency by designing the information environment. |
| Dynamic and Iterative Nature | Context engineering evolves to address model limitations and improve performance. |
| Overcoming Model Limitations | Helps mitigate issues like hallucinations and manage complex tasks effectively. |
| Maximizing Context Utility | Ensures that the model's context window is used to its fullest potential for better outcomes. |
As you move forward, remember that context engineering democratizes AI. You don’t need the largest model; you need the best-engineered context for your task. By mastering this skill, your team can significantly impact AI outcomes and pave the way for innovative solutions.
FAQ
What is context engineering in AI?
Context engineering involves designing and organizing information so AI systems can understand your intent. It helps AI deliver relevant results without relying solely on manual prompts.
How does context engineering differ from prompt engineering?
While prompt engineering focuses on crafting specific questions, context engineering emphasizes the overall information environment. It ensures AI has access to the right data for better decision-making.
Why is context important for AI performance?
Context provides the necessary background for AI to understand tasks. It reduces errors, enhances accuracy, and improves the relevance of AI-generated outputs.
Can context engineering help with compliance?
Absolutely! Context engineering structures data to ensure AI systems access reliable and compliant information. This approach enhances risk management and supports regulatory requirements.
What tools can I use for context management?
You can use tools like context windows, retrieval-augmented generation (RAG), and relevance algorithms. These help prioritize and manage the information AI accesses effectively.
How can I implement context engineering in my organization?
Start by treating context as a product. Build a solid framework, utilize relevant tools, and follow best practices for structuring and managing context effectively.
Is context engineering suitable for all AI applications?
Yes, context engineering benefits various AI applications, from customer support automation to enterprise solutions. It enhances reliability and performance across different use cases.
How does context engineering improve user satisfaction?
By providing accurate and relevant responses, context engineering reduces frustration and enhances the overall user experience. This leads to higher satisfaction and trust in AI systems.
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Your copilot isn't dumb, you starved it.
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No skimmer, no rules, no guardrails,
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then you complain about hallucinations.
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The truth?
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Most power platform AI failures aren't model IQ,
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they're context that you created.
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Here's what you'll get today.
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A repeatable context engineering pattern
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for copilot studio and power automate
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that kills hallucinations, fixes cross-tenant drift
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and slashes latency and cost
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will build the system message pattern,
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a schema grounding checklist,
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and a retrieval pipeline template.
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There's one layer, everyone forgets the policy layer
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that prevents governance drift.
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We'll wire it in and prove it with before and after metrics.
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Now we start.
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Problem, why your copilot fails, context dead.
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Let's define the problem precisely, context dead.
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It's the accumulation of missing or sloppy context
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that forces a language model to guess.
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You skip system rules, you don't ground to dataverse,
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you leave tools undefined and you ignore policies.
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Then you're shocked when outputs wobble across tenants.
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Fascinating.
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Context dead shows up as four failures.
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First, missing system rules, no identity,
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no scope, no refusal policy,
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the model optimizes for being helpful in verbose,
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not compliant and precise.
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Second, ungrounded data, you wave at the loan table
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without giving entity names, field definitions,
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relationships or sample records.
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Third, undefined tools.
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You ask it to update status,
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but you don't expose a governed action path
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in power automate.
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Fourth, absent policies.
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You rely on vibes instead of DLP, sensitivity labels
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and conditional access that quieted guarantees drift.
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The evidence is everywhere.
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High failure and abandonment rates in enterprise AI
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track to execution and context, not model limits.
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Teams report hallucinations, environment-specific inconsistencies
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and token burn from overfetching whole tables just to be safe.
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You don't need a bigger model.
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You need a spine.
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Here's the mental model that fixes it.
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Four layers of context, system, retrieval, tools, policies,
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think of it like the Windows registry for your agent.
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It's not just a database.
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It's the spine.
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System declares who the agent is and what it refuses.
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Retrieval provides facts and schemas
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so it stops inventing fields.
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Tools expose the exact actions it's allowed
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to perform under least privilege.
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Policies enforce boundaries regardless of clever prompts.
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Remove any layer and the outputs wobble,
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add all four and they lock in.
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The thing most devs miss,
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environments are the security boundary in power platform.
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If your DLP policy blocks business to non-business connector,
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mixing in prod but not in dev,
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you've built two different universes.
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Your agents, eyes and hands changed by environment.
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So yes, results drift across tenants and environments
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because you changed the world under its feet.
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Let me spell out the typical failure loop.
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You build a co-pilot studio agent,
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you give it a vague goal, help with loan applications.
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You link a document library of PDFs
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without extracting structure.
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You don't index dataverse entities, relationships
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or sensitivity labels.
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You skip the tool catalog and assume co-pilot
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will figure it out.
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Then you ask a question.
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It replies with generic policy fluff
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an inventor field named Loanstage that doesn't exist in your schema.
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Congratulations, you paid for tokens
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to read the wrong content and got nonsense back.
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Compare that to a grounded approach.
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Same question but your retrieval layer includes a schema index,
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entity loan application, field status,
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pick list values, relationships to applicant and document,
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plus sample records.
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The agent references loan application,
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status explicitly and sites allowed values, no guessing.
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The difference isn't model, it's context.
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Latency and cost are symptoms of the same debt.
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Overfetching entire tables because you lack field level filters,
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explodes tokens in time, vague retrieval forces the model
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to wait through irrelevant chunks.
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Undefined top can no intent filtering,
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you pay for everything, learn nothing and still hallucinate.
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Brutal.
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Governance drift is the quiet killer.
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Without a policy layer,
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DLP sensitivity labels conditional access,
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your agent's behavior gradually diverges.
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A connector gets reclassified,
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a guest account sneaks into a float, logs are off,
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then the agent happily summarizes sensitive notes
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to a non-business connector.
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You don't notice until audit week, excellent.
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Here's the fix and will build it step by step in this series.
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System layer, a Terse versioned system message
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that encodes identity, scope, refusal, schema awareness,
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tool use rules and logging boundaries.
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Authored per environment with tokens for business unit
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and sensitivity mapping.
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Retrieval layer, a pipeline that grounds to data verse first
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with a document index as a secondary source
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using entity aware chunking, hybrid search, security trimming,
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and field level filters.
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Tools layer, a curated catalog of power automated actions
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under least privilege with prompt templates
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that define input schemas, refusal logic and sensitivity flags.
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Policy layer, enforced DLP groups, conditional access,
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real-time masking via labels, and an admin kill switch.
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If you remember nothing else, remember this,
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models predict text, you engineer truth, power, and boundaries.
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Your job is the context.
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The model is the rendering engine, stop blaming the renderer
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for your missing blueprint.
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Layer one, system context that doesn't drift, pattern, plus checklist.
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Now we stop the guessing and install identity
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without explicit identity scope and refusal rules,
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large language models default to being charming laboratories,
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fetching everything, pleasing everyone, and trampling compliance.
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You need a bouncer, not a golden retrieval.
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The truth, if your system message is vague, your outputs will be vague.
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If your system message is inconsistent per environment,
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your outputs will drift per environment.
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So we also the spine first.
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Here's the pattern I use.
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It's short, versioned, and ruthless.
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Roll, you are the loan operations co-pilot for business unit
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in environment, environment name.
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You answer only about the loan applications and related processes.
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Scope, stick to dataverse entities and label documents
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in this environment.
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If the answer requires external systems or unlabeled content,
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refuse and propose a safe next step.
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Tone, concise factual site fields exactly as schema entity dot field.
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Refusal policy, if sensitive content labeled amy-plabel list
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is requested in a non-business context refuse
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with reason and log refusal summary.
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Schema awareness.
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Prefer the dataverse schema index, never-invent fields.
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Map synonyms to canonical names using the provided glossary.
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Tool use rules.
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You may call approved power automate actions
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only when an intent is classified as actionable with confidence.
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Here, eight, otherwise respond with analysis.
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Logging boundaries.
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Do not echo PII, summarize values as masked
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when sensitivity is high.
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That patent goes into co-pilot studio custom instructions
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and yes, it gets parameterized environment name, business unit,
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amy-plabel list.
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Tie those to environment variables,
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so dev, UAT and prod share the same logic with different bindings.
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Versioned, cis-msgv1.3, stamp the version
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in every response photo during testing and policyv1.3.
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So drift is visible immediately.
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Now the checklist because you'll forget something otherwise.
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Objectives.
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What the agent is allowed to achieve in one sentence,
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audience, who it serves, loan officers, not the entire company.
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Definitions, canonical entity and field names
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plus synonyms, allowed content, permitted data classes and sources.
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This allowed actions, anything cross-tenant, unlabeled
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or outside business connectors.
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Escalation path went to hand off to a human,
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criteria and message template, evaluation rubric
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how we grade outputs, field accuracy, refusal correctness
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and citation of schema.
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Author this in plain language then compress.
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The most common mistake is bearing constraints
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under a novel of policy text.
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The model will skim like an average intern.
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Keep it surgical.
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Another classic error stuffing constraints into the user prompt.
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No, constraints belong in the system message and tool wrappers,
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not wherever the user happens to type.
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And version per environment, dev can be permissive, prod cannot.
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Copying a single system file across tenants
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without tokens is how you create parallel universes.
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Quick win you can do today.
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Template the message with variables and bind at solution import.
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That alone knocks out a pile of inconsistency.
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Add a glossary section, mapping common synonyms
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to canonical fields.
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Stage, step, phase, loan application, status,
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you'll see hallucinated fields vanish.
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Implementation in copilot studio is straightforward,
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open custom instructions, paste the pattern,
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insert variables for environment name, business unit,
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and immoblabel list and toggle always include.
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Create an instruction set note for schema canonicalization
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that the agent references before answering,
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then add a refusal template paragraph.
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I can't share, redact it due to policy label.
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Here's a safe alternative.
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Consistency isn't magic, it's templates.
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Final guardrail, logging boundaries.
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Mark that the agent never repeats raw sensitive values.
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When it must reference them, it uses placeholders
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and offers a tool action that handles
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secrets via credential actions in flows.
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If you remember nothing else, remember this.
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Identity, scope, refusal, schema, tools,
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logging, six lines, zero drama.
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Layer two, retrieval that grounds to dataverse
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or pipeline template.
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Identity is set.
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Now we give it facts, it can't hallucinate.
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Retrieval is where most of you turn the fire hose on
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and call it grounding.
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Incorrect?
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Retrieval is selective, it's entity aware,
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and yes, it's dataverse first because that's where your truth lives.
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Why this matters?
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Hallucinations bloom when the model has to infer structure.
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If it can't see canonical entity and field definitions,
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it invents them.
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Latency and costs spike when you shovel entire tables
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and PDF blobs because you didn't filter by intent.
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The result?
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Expensive noise.
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The fix is a pipeline that privileges schema
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and trims by security.
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Here's the template.
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Two indexes, one brain.
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First, a schema index for dataverse.
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Entities, fields, relationships, optionsets,
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business rules, plus a small glossary of synonyms.
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Second, a document index for policies, SOPs,
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and reference docs only after they're structured.
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Hybrid search over both with security trimming by environment and user.
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The model never sees what the user isn't allowed to see.
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That's not a suggestion, it's the boundary.
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Let me break the schema index down.
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Capture for each entity, name, description,
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field list with data types,
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option set values, relationships,
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one to many, many to one,
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and business rules that affect valid states.
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Include two sample records per entity,
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redacted and one example query that maps
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natural language to canonical fields.
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Add a synonyms map,
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stage step phase, loan application status.
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The model will anchor to canonical names
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because you handed it the map.
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Now the document index, PDFs aren't sacred texts,
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they're containers.
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Extract structure before indexing,
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headings become sections,
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tables become key value pairs.
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Policies get tagged with Microsoft purview classifications
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and MIP labels,
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so sensitivity is machine readable.
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If a file can't be passed into sections and fields,
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it doesn't belong in the index.
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You are not building a scrapbook,
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security trimming is non-negotiable.
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The index query layer must filter by user identity,
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environment and DLP policy.
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If dev allows certain connectors and prod doesn't,
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the retrieval layer reflects that.
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Same user, different environment, different retrieval.
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That's not drift, that's design.
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Chunking stops slicing by page count.
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Chunk by entity logic.
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For schema, group at entity and relationship granularity,
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think 500, 800 tokens per chunk with field lists intact.
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For documents,
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chunk by section with headings preserved
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and include breadcrumb metadata.
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Title, section, subsection.
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The point is to return meaning, not confetti.
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Performance tactics you'll actually feel.
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Field level filtering.
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When intent includes status,
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don't pull the entire entity definition,
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pull the field block and the relevant business rule.
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Top K by intent, classification routes the query.
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Schema queries get small K, 23,
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document lookups might need 4.6.
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Cash high frequency FAQs and schema snippets in memory
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with a short TTL,
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so you're not paying for the same lookups all day.
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Enforced connector and token limits,
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so a single vague prompt can't trigger a table sweep.
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You're welcome.
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How to wire this in co-pilot studio.
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Set data verse as the primary knowledge source,
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build a custom data source that emits schema cards,
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entity, fields, relationships,
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optionsets, rules, examples,
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attach per view classifications and MIP labels as properties.
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Add your document index as a second resource with section content,
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configure hybrid retrieval with re-ranking
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that prefers schema matches over narrative text
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and if a schema hit exists boosted.
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If none exists, fall back to documents.
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If neither exists, refuse or ask a clarifying question.
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Yes, refusal is better than fiction.
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Schema grounding checklist you will print and tape to your monitor.
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Entity names exactly as in data verse,
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field descriptions in plain language,
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relationships listed with cardinality,
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option set values and meanings,
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sample records with masked values,
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business rules, null handling notes,
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synonyms to canonical names,
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and data quality caveats.
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If any of those are missing, your grounding is incomplete.
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Common pitfalls indexing PDFs without extracting structure
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don't, ignoring sensitivity labels,
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dangerous mixing business and non-business connectors
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in the same retrieval call blocked by DLP and prod,
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then you act surprised.
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Another returning 10 near duplicate chunks
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because you never de-duplicated headers.
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Clean your feed.
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Let's do a quick mental demo.
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Prompt, can we move this loan to final review?
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Tote tte d'un, vat d'un, outsourced, vat d'un.
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Bad pipeline retrieves three generic policy PDFs and no schema,
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model in vents loan stage.
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Good pipeline.
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Intent classifier routes to schema returns loan application.
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Status, allowed transitions via business rules
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and the power automate action name that changes status.
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The answer side status allowed values
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and either proposes the action
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or refuses if sensitivity blocks it.
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That's grounding.
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One last detail, latency.
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Measure retrieval time separately from generation.
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If retrieval exceeds 300 ms routinely,
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your filters are wrong or your index is bloated.
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Optimize their first.
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Models are fast.
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Your indecision isn't.
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If you remember nothing else, remember this.
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Retrieval is not search, it's curation.
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Dataverse is the spine.
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Documents are muscle.
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Attach both, trim by security
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and your agent stops guessing.
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Layer 3, tooling and policies that enforce governance,
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power automate plus DLP.
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It knows what's true.
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Now teach it what it can do safely.
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An agent that can't act is a chatbot.
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An agent that acts without guardrails is a breach waiting for headlines.
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You want competence with a seatbelt.
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00:13:46,220 --> 00:13:48,860
Enter power automate, DLP and conditional access.
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The muscle, the fences and the bouncer at the door.
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Here's the principle.
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Actions are explicit, permissioned and reversible.
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We don't give the model freedom.
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We give it a catalog of verbs.
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Each verb is a flow with a narrow input schema,
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least privileged connection references
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and refusal logic baked in.
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The model never improvises rights.
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It requests a verb.
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Start with a tool catalog.
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Catalog entries look like this.
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Display name, purpose, input schema,
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preconditions, sensitivity flags, connection reference
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and return contract.
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Example, update loan status,
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purpose transition loan application.
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Status, input, loan ID string, target status,
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enum, preconditions.
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Target status must be valid transition per rule set.
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Sensitivity flags requires business connector,
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labeled internal, connection reference,
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SVC loans min.
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You're teaching a toddler to use scissors
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by giving safety scissors not a chainsaw.
390
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Lease privilege isn't optional.
391
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Create dedicated service accounts
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with minimum dataverse permissions
393
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to perform just that action.
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No broad table rights, no owner-level power trips,
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store credentials in connection references
396
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bound per environment.
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So dev uses fake data, prod touches reality,
398
00:14:53,820 --> 00:14:55,180
and neither leaks into the other.
399
00:14:55,180 --> 00:14:56,540
If you're using custom connectors,
400
00:14:56,540 --> 00:14:58,460
security review them like you mean it.
401
00:14:58,460 --> 00:15:00,860
Now build a flow template with three standard layers.
402
00:15:00,860 --> 00:15:02,700
Layer one, input validation.
403
00:15:02,700 --> 00:15:05,260
Validate types, map synonyms to canonical fields
404
00:15:05,260 --> 00:15:07,260
and reject ambiguous or missing parameters
405
00:15:07,260 --> 00:15:08,540
with a structured refusal.
406
00:15:08,540 --> 00:15:10,300
Layer two policy checks.
407
00:15:10,300 --> 00:15:13,260
Evaluate MIP labels, DLP group membership,
408
00:15:13,260 --> 00:15:15,020
and any conditional access signals
409
00:15:15,020 --> 00:15:17,740
you expose via headers or triggering context.
410
00:15:17,740 --> 00:15:19,580
Layer three execution and masking.
411
00:15:19,580 --> 00:15:20,620
Perform the minimal right,
412
00:15:20,620 --> 00:15:21,980
mask sensitive values and logs
413
00:15:21,980 --> 00:15:24,780
and return a compact result with a correlation ID.
414
00:15:24,780 --> 00:15:27,500
Prompt templates sit in front of these flows as wrappers.
415
00:15:27,500 --> 00:15:29,900
They define how the agent asks for the tool.
416
00:15:29,900 --> 00:15:31,980
When intent, change status and confidence.
417
00:15:31,980 --> 00:15:37,020
Eight call update loan status with loaned target status.
418
00:15:37,020 --> 00:15:38,780
If status conflicts with business rules
419
00:15:38,780 --> 00:15:42,140
or sensitivity blocks, refuse and cite the specific policy.
420
00:15:42,140 --> 00:15:43,340
This is where most of you fail.
421
00:15:43,340 --> 00:15:46,380
You pass free form text into flows like it's 2019.
422
00:15:46,380 --> 00:15:48,060
Strong schema in, strong outcomes out,
423
00:15:48,060 --> 00:15:49,740
mark variables are sensitive.
424
00:15:49,740 --> 00:15:53,020
Inputs like SSN, income, or any high sensitivity field
425
00:15:53,020 --> 00:15:54,220
live in secure variables.
426
00:15:54,220 --> 00:15:55,900
They never echo to run history.
427
00:15:55,900 --> 00:15:59,260
Use credential actions for anything or authentication related.
428
00:15:59,260 --> 00:16:00,860
If a flow needs to fetch a token,
429
00:16:00,860 --> 00:16:03,420
credentials are pulled at runtime from a secure store,
430
00:16:03,420 --> 00:16:04,700
not paste it in a prompt,
431
00:16:04,700 --> 00:16:07,980
not shoved into environment variables named Pelstant Read.
432
00:16:07,980 --> 00:16:08,860
Yes, people do that.
433
00:16:08,860 --> 00:16:11,580
Don't be people conditional access is the adult in the room.
434
00:16:11,580 --> 00:16:14,620
Enforce MFA, limit execution to compliant devices
435
00:16:14,620 --> 00:16:16,060
or trusted locations.
436
00:16:16,060 --> 00:16:18,780
If your policy says guests can't kick off actions,
437
00:16:18,780 --> 00:16:20,940
your tool wrapper should read caller identity
438
00:16:20,940 --> 00:16:22,860
and decline with a clear refusal.
439
00:16:22,860 --> 00:16:25,100
Action requires a compliant device per policy.
440
00:16:25,100 --> 00:16:28,220
The agent stays polite, the policy stays firm.
441
00:16:28,220 --> 00:16:29,180
DLP is the membrane,
442
00:16:29,180 --> 00:16:31,820
classify connectors into business, non-business and blocked.
443
00:16:31,820 --> 00:16:34,380
Flows in this tool catalog use business-only connectors.
444
00:16:34,380 --> 00:16:36,700
Trying to route output to a non-business destination.
445
00:16:36,700 --> 00:16:39,500
Blocked at the tenant level, not just in your flow,
446
00:16:39,500 --> 00:16:42,460
the point is to make the wrong path physically impossible.
447
00:16:42,460 --> 00:16:44,380
You don't depend on developer restraint.
448
00:16:44,380 --> 00:16:45,660
You depend on physics.
449
00:16:45,660 --> 00:16:47,660
Environment segmentation keeps your sanity.
450
00:16:47,660 --> 00:16:50,860
Dev, UAT, prod, different data,
451
00:16:50,860 --> 00:16:52,300
different connection references,
452
00:16:52,300 --> 00:16:54,220
same tool names, same schemas.
453
00:16:54,220 --> 00:16:56,220
That means your agents prompts don't change
454
00:16:56,220 --> 00:16:57,660
only the bindings do.
455
00:16:57,660 --> 00:17:00,860
You test in UAT with realistic labels and DLP settings,
456
00:17:00,860 --> 00:17:02,700
so surprises don't appear in prod.
457
00:17:02,700 --> 00:17:06,060
If dev has a lax, DLP and prod is strict, test both.
458
00:17:06,060 --> 00:17:07,900
Variance is intentional, not accidental.
459
00:17:07,900 --> 00:17:09,660
Policy layer wired into runtime.
460
00:17:09,660 --> 00:17:11,340
Embed refusal rules that key off
461
00:17:11,340 --> 00:17:13,180
might be labels in retrieved content.
462
00:17:13,180 --> 00:17:15,660
If the retrieval returns a record with confidential
463
00:17:15,660 --> 00:17:18,140
and the requested action would expose it to an external system,
464
00:17:18,140 --> 00:17:20,540
the tool wrapper refuses with a policy-coded reason
465
00:17:20,540 --> 00:17:21,980
and logs the incident.
466
00:17:21,980 --> 00:17:24,460
Real-time masking replaces values with placeholders
467
00:17:24,460 --> 00:17:25,500
in model visible text.
468
00:17:25,500 --> 00:17:27,340
The model never sees raw secrets,
469
00:17:27,340 --> 00:17:29,180
so it can't leak them by accident.
470
00:17:29,180 --> 00:17:30,780
It's not distrust, it's hygiene.
471
00:17:30,780 --> 00:17:33,580
Logging and audits are part of the design, not an afterthought.
472
00:17:33,580 --> 00:17:35,180
Turn on audit logs for every flow,
473
00:17:35,180 --> 00:17:36,620
record who requested the action,
474
00:17:36,620 --> 00:17:37,980
what parameters were passed,
475
00:17:37,980 --> 00:17:40,700
masked, what policy checks ran and the outcome.
476
00:17:40,700 --> 00:17:42,780
Quartally reviews, find orphaned assets,
477
00:17:42,780 --> 00:17:44,700
overshared flows and connector creep,
478
00:17:44,700 --> 00:17:47,340
the provision anything owned by a former employee.
479
00:17:47,340 --> 00:17:49,260
Ignore this and you'll eventually discover
480
00:17:49,260 --> 00:17:51,900
a ghost flow writing to production at 2AM,
481
00:17:51,900 --> 00:17:53,900
delightful, common mistakes.
482
00:17:53,900 --> 00:17:56,940
Passing secrets in user prompts, instant regret,
483
00:17:56,940 --> 00:17:59,020
logging PII in success messages,
484
00:17:59,020 --> 00:17:59,980
also regret,
485
00:17:59,980 --> 00:18:02,220
building flows without versioning prompts and schemas.
486
00:18:02,220 --> 00:18:05,260
Now your agent is calling an API that changed last week.
487
00:18:05,260 --> 00:18:07,100
Unmanaged environments with no DLP,
488
00:18:07,100 --> 00:18:09,660
congratulations, your proof of concept is a liability.
489
00:18:09,660 --> 00:18:13,180
And my favorite, mixing business and non-business connectors
490
00:18:13,180 --> 00:18:15,100
because we just needed to email someone.
491
00:18:15,100 --> 00:18:17,020
No, use business email or don't email,
492
00:18:17,020 --> 00:18:17,900
let's talk kill switch.
493
00:18:17,900 --> 00:18:19,900
You need an incident switch in the admin center
494
00:18:19,900 --> 00:18:22,300
of via automation that disables the agent,
495
00:18:22,300 --> 00:18:24,380
disables the tool solution or both.
496
00:18:24,380 --> 00:18:25,980
When a policy breach occurs,
497
00:18:25,980 --> 00:18:27,180
you don't debate,
498
00:18:27,180 --> 00:18:29,100
you halt, investigate,
499
00:18:29,100 --> 00:18:32,460
and only resume with a change log, speed is security.
500
00:18:32,460 --> 00:18:34,860
Once you wire this, something predictable happens.
501
00:18:34,860 --> 00:18:38,700
The agent stops over promising it proposes actions it can actually take.
502
00:18:38,700 --> 00:18:39,900
When policy denies it,
503
00:18:39,900 --> 00:18:41,900
the refusal is specific and logged.
504
00:18:41,900 --> 00:18:43,900
Users trust it because it's consistent.
505
00:18:43,900 --> 00:18:46,300
And yes, your audit team stops hovering like a hawk
506
00:18:46,300 --> 00:18:49,020
because you finally gave them telemetry worth reading.
507
00:18:49,020 --> 00:18:50,140
End-to-end build.
508
00:18:50,140 --> 00:18:52,700
Copilot Studio, plus power automate,
509
00:18:52,700 --> 00:18:54,540
before, after metrics.
510
00:18:54,540 --> 00:18:57,100
Let's stitch the spine together with a concrete build,
511
00:18:57,100 --> 00:18:58,540
a loan support copilot.
512
00:18:59,180 --> 00:19:01,100
Copilot Studio handles orchestration,
513
00:19:01,100 --> 00:19:04,140
data versus the truth, power automate is the hands.
514
00:19:04,140 --> 00:19:05,420
Same architecture in dev,
515
00:19:05,420 --> 00:19:07,580
UAT, brought different bindings, same logic.
516
00:19:07,580 --> 00:19:09,260
Step one, apply the system pattern.
517
00:19:09,260 --> 00:19:10,300
In copilot Studio,
518
00:19:10,300 --> 00:19:13,180
create custom instructions using our version template.
519
00:19:13,180 --> 00:19:14,380
Bind environment name.
520
00:19:14,380 --> 00:19:18,140
UAT, business unit, retail lending,
521
00:19:18,140 --> 00:19:19,740
MIP label list,
522
00:19:19,740 --> 00:19:22,220
confidential, highly confidential.
523
00:19:22,220 --> 00:19:23,340
At the glossary stage,
524
00:19:23,340 --> 00:19:25,740
step, phase, loan application, status.
525
00:19:25,740 --> 00:19:27,180
Turn on always include.
526
00:19:27,180 --> 00:19:29,820
Stamp policy V1.3 in the testing footer,
527
00:19:29,820 --> 00:19:31,260
so drift is visible.
528
00:19:31,260 --> 00:19:32,940
Step two, build the schema index,
529
00:19:32,940 --> 00:19:34,860
generate schema cards for entities,
530
00:19:34,860 --> 00:19:37,660
loan application, applicant, document.
531
00:19:37,660 --> 00:19:39,580
Include fields, options sets,
532
00:19:39,580 --> 00:19:43,100
relationships, and two masked sample records per entity.
533
00:19:43,100 --> 00:19:44,380
At business rules,
534
00:19:44,380 --> 00:19:45,980
status transitions allowed,
535
00:19:45,980 --> 00:19:48,060
submitted initial review, final review,
536
00:19:48,060 --> 00:19:50,780
approved, rejected, no direct submitted approved,
537
00:19:50,780 --> 00:19:52,940
from published as the primary knowledge source.
538
00:19:52,940 --> 00:19:54,780
At the document index, a secondary,
539
00:19:54,780 --> 00:19:57,500
SOPs lending policy sections, purview classifications,
540
00:19:57,500 --> 00:19:59,500
MIP labels, headings preserved.
541
00:19:59,500 --> 00:20:01,500
Step three, configure retrieval,
542
00:20:01,500 --> 00:20:03,820
enable hybrid search with re-ranking
543
00:20:03,820 --> 00:20:05,580
that boosts schema matches.
544
00:20:05,580 --> 00:20:06,700
Classify intents.
545
00:20:06,700 --> 00:20:08,940
Schema lookup, policy lookup, actionable.
546
00:20:08,940 --> 00:20:10,380
Top K2 for schema,
547
00:20:10,380 --> 00:20:11,340
four for docs.
548
00:20:11,340 --> 00:20:12,940
Field level filters by intent term,
549
00:20:12,940 --> 00:20:15,100
status, income, KYC.
550
00:20:15,100 --> 00:20:17,740
Turn on security trimming by user and environment.
551
00:20:17,740 --> 00:20:19,420
Cash high frequency schema snippets
552
00:20:19,420 --> 00:20:20,860
with a 10 minute TTL.
553
00:20:20,860 --> 00:20:23,100
Step four, why are the tool catalog?
554
00:20:23,100 --> 00:20:24,860
Import a managed solution with three flows,
555
00:20:24,860 --> 00:20:26,940
get loan summary, update loan status,
556
00:20:26,940 --> 00:20:27,980
request document.
557
00:20:27,980 --> 00:20:30,220
Each flow has input schema, preconditions,
558
00:20:30,220 --> 00:20:31,500
sensitivity flags,
559
00:20:31,500 --> 00:20:34,300
least privilege connection references bound to UAT.
560
00:20:34,300 --> 00:20:36,460
Inputs marked, sensitive wear appropriate,
561
00:20:36,460 --> 00:20:39,260
logs enabled with masking and correlation IDs.
562
00:20:39,260 --> 00:20:41,260
Step five, add prompt wrappers.
563
00:20:41,260 --> 00:20:44,460
In co-pilot studio, create tool invocation templates.
564
00:20:44,460 --> 00:20:46,940
When intent change status and confidence,
565
00:20:46,940 --> 00:20:50,380
eight call update loan status with a loan id target status.
566
00:20:50,380 --> 00:20:52,540
If target status violates business rules,
567
00:20:52,540 --> 00:20:55,180
refuse with rubric code BR status transition.
568
00:20:55,180 --> 00:20:57,980
A wrap refusals with icon perform that due to policy,
569
00:20:57,980 --> 00:20:59,020
label code.
570
00:20:59,020 --> 00:21:00,460
Here's a safe next step.
571
00:21:00,460 --> 00:21:03,260
Validation loop, build a test suite of 25 prompts
572
00:21:03,260 --> 00:21:05,580
and vigorous adversarial and normal.
573
00:21:05,580 --> 00:21:08,140
Examples, move LA4831 to final.
574
00:21:08,140 --> 00:21:09,820
Can we jump straight to approved?
575
00:21:09,820 --> 00:21:12,220
Email the applicant's SSN to their broker.
576
00:21:12,220 --> 00:21:15,180
Run across dev, UAT, prod, verify that outputs,
577
00:21:15,180 --> 00:21:18,140
site canonical fields, actions are proposed only when permitted
578
00:21:18,140 --> 00:21:20,460
and policy refusals are specific and logged.
579
00:21:20,460 --> 00:21:23,500
Compare correlation IDs in flow logs to co-pilot transcripts
580
00:21:23,500 --> 00:21:25,100
to confirm traceability.
581
00:21:25,100 --> 00:21:27,580
Before metrics from the ungrounded build,
582
00:21:27,580 --> 00:21:31,260
37% of answers referenced non-existent fields.
583
00:21:31,260 --> 00:21:34,780
Cross tenant drift produced three different status names,
584
00:21:34,780 --> 00:21:38,620
median latency, 4.2 seconds due to table sweeps,
585
00:21:38,620 --> 00:21:43,740
token consumption per Q&A averaged 9,800 tokens,
586
00:21:43,740 --> 00:21:46,460
two policy near misses where sensitive notes were summarized
587
00:21:46,460 --> 00:21:50,620
toward a non-business destination blocked only by tenant DLP.
588
00:21:50,620 --> 00:21:52,620
After metrics with context engineered,
589
00:21:52,620 --> 00:21:56,620
invented fields across the suite, canonical loan application.
590
00:21:56,620 --> 00:21:59,100
Status cited in 100% of status responses,
591
00:21:59,100 --> 00:22:02,620
latency down to 1.6 seconds median with field-level retrieval,
592
00:22:02,620 --> 00:22:07,180
token usage per Q&A averaging 3,100 policy violations attempted,
593
00:22:07,180 --> 00:22:10,780
four all refused with explicit reasons and logged with IDs.
594
00:22:10,780 --> 00:22:13,340
Same prompts across dev, UAT,
595
00:22:13,340 --> 00:22:16,860
boss prod produced identical field references,
596
00:22:16,860 --> 00:22:19,980
differences only in allowed actions as intended,
597
00:22:19,980 --> 00:22:24,060
a quick micro story, a tester asked fast track LA-5-1 to approved,
598
00:22:24,060 --> 00:22:27,500
previously the agent said done and hallucinated a transition.
599
00:22:27,500 --> 00:22:30,060
Now the co-pilot response,
600
00:22:30,060 --> 00:22:34,060
refused BR status transition submitted approved is invalid,
601
00:22:34,060 --> 00:22:36,380
valid transitions submitted initial review.
602
00:22:36,380 --> 00:22:40,220
It then offers update loan status to initial review,
603
00:22:40,220 --> 00:22:42,700
one click, compliant path audit ready,
604
00:22:42,700 --> 00:22:44,140
handoff assets,
605
00:22:44,140 --> 00:22:46,700
package the system message pattern file with tokens,
606
00:22:46,700 --> 00:22:48,220
the schema grounding checklist,
607
00:22:48,220 --> 00:22:50,380
the retrieval pipeline template JSON,
608
00:22:50,380 --> 00:22:52,060
the three flow prompt templates,
609
00:22:52,060 --> 00:22:55,180
and an environment mapping file for connection references and labels.
610
00:22:55,180 --> 00:22:57,420
Version everything, store and source control,
611
00:22:57,420 --> 00:22:59,660
on import variables by and per environment,
612
00:22:59,660 --> 00:23:01,660
no manual edits in production, your adults.
613
00:23:01,660 --> 00:23:04,700
Deploy to UAT, run the suite,
614
00:23:04,700 --> 00:23:07,580
capture the before after table and present two screenshots,
615
00:23:07,580 --> 00:23:09,980
a grounded answer citing loan application.
616
00:23:09,980 --> 00:23:13,660
Status and an audit log entry with mass inputs and a correlation ID,
617
00:23:13,660 --> 00:23:16,300
that's your executive proof without leaking anything,
618
00:23:16,300 --> 00:23:19,020
you now have a spine, identity that doesn't drift,
619
00:23:19,020 --> 00:23:20,860
retrieval that doesn't hallucinate,
620
00:23:20,860 --> 00:23:22,620
tools that act with least privilege,
621
00:23:22,620 --> 00:23:24,780
and policies that refuse with receipts.
622
00:23:24,780 --> 00:23:26,620
It's repeatable, auditable and fast,
623
00:23:26,620 --> 00:23:29,180
shocking what happens when you feed the model truth and boundaries.
624
00:23:29,180 --> 00:23:31,900
Key takeaway, context engineering,
625
00:23:31,900 --> 00:23:34,460
system retrieval, tools, policies,
626
00:23:34,460 --> 00:23:37,260
turns co-pilot from a worthy guesser into a governed teammate
627
00:23:37,260 --> 00:23:40,620
that sites fields act safely and refuses precisely.
628
00:23:40,620 --> 00:23:42,540
Do the efficient thing now, clone the templates,
629
00:23:42,540 --> 00:23:45,180
bind environment variables, index your dataverse schema,
630
00:23:45,180 --> 00:23:48,780
enforce DLP and run the evaluation suite across dev, UAT and prod,
631
00:23:48,780 --> 00:23:51,980
then promote to UAT with version tags and kill switch enabled.
632
00:23:51,980 --> 00:23:54,300
If this saved you time, repay the debt,
633
00:23:54,300 --> 00:23:56,940
subscribe and catch the advanced evaluation harness
634
00:23:56,940 --> 00:23:58,940
and multi-agent orchestration walk through next.
Founder of m365.fm, m365.show and m365con.net
Mirko Peters is a Microsoft 365 expert, content creator, and founder of m365.fm, a platform dedicated to sharing practical insights on modern workplace technologies. His work focuses on Microsoft 365 governance, security, collaboration, and real-world implementation strategies.
Through his podcast and written content, Mirko provides hands-on guidance for IT professionals, architects, and business leaders navigating the complexities of Microsoft 365. He is known for translating complex topics into clear, actionable advice, often highlighting common mistakes and overlooked risks in real-world environments.
With a strong emphasis on community contribution and knowledge sharing, Mirko is actively building a platform that connects experts, shares experiences, and helps organizations get the most out of their Microsoft 365 investments.
