Microsoft AI Agent Harness - Simply Explained

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Today's topic is one that almost everyone has heard of, but few can actually explain.

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The AI Agent Harness.

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You've probably seen it in blog posts or conference talks, but what does it actually

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mean?

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Here's the thing.

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Most people think building an AI agent is about writing the perfect prompt, that one

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magical instruction that makes the model do exactly what you want.

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But that's like, thinking a car is just an engine.

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Sure, the engine is important, but without wheels, a steering wheel breaks and a chassis,

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you're not going anywhere.

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By the end of this episode, you'll understand what an agent harness actually is.

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Why it's the next evolution beyond prompt engineering and how Microsoft AI Foundry brings

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this to life for real businesses?

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Grab your coffee and let's dive in.

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Why prompting isn't enough anymore?

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Let's go back to the early days.

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Around 2022 to 2024, working with AI was simple.

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You wrote a prompt and got an answer.

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Need a draft email?

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Write a prompt.

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Need a summary of an article?

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Write a prompt for simple one-shot tasks that worked really well.

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You could ask for something, and the model would give you a reasonable response.

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But then people got more ambitious.

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They started asking these models to do real work.

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Research a topic and write a report, analyze a codebase, and fix a bug or handle a multi-step

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customer support conversation from start to finish.

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And suddenly, one prompt couldn't handle it.

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Here's what happened.

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The model's context window filled up.

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It forgot the earlier instructions, and it started making up facts or giving incomplete

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results.

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The longer the task went on, the more unreliable the output became.

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You've probably experienced this yourself.

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You ask an AI to do something complex.

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And by the end, it's forgotten what you asked it to do in the first place.

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Imagine asking a single person to build an entire house from scratch, all in one sitting,

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without ever checking their work.

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That's what we were asking these models to do.

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The results were exactly what you'd expect.

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A half finished house with missing walls and doors that don't open.

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The solution wasn't a better prompt.

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You could spend hours tweaking the wording, adding more examples, and refining the instructions,

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and it would still fail on long complex tasks because the fundamental problem wasn't

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the prompt.

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It was the lack of a system around it.

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But the model needed wasn't better words, but better support.

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That's where the evolution from prompt engineering to context engineering began.

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The three phases of AI evolution.

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So here's how I see the evolution of AI.

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Three phases.

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Each one shifts the focus for engineers.

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Phase one was prompt engineering that ran from about 2022 to 2024.

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The big question was simple.

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What should I say to the model?

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You'd write one instruction, get one answer, and that was it.

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The whole craft was about wording and tone.

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For basic tasks, that was enough.

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Phase two came next.

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In context engineering from 2024 through 2025, the question changed from what should I say

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to what should the model see.

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Engineers found that what you feed the model matters more than how you ask.

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So they built systems like tool calling, retrieval augmented generation, and the model context

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protocol, MCP.

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These let the model grab the right information on the fly.

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Instead of stuffing everything into one prompt, the model could fetch what it needed when

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it needed it.

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Phase three is where we are now.

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Harness engineering.

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Starting around 2026, the question is, what system do

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I need to build around the model?

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This is where it gets interesting.

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Because reliability, memory, orchestration, and guardrails don't come from the model itself,

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they come from the system you build around it.

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Here it is, the simplest way to think about it.

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An agent equals a model plus a harness.

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The harness is everything else.

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The model does the thinking, the harness does the rest, and that harness is the secret source

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that makes the whole thing work.

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Think of it like this.

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The model is a skilled worker.

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Really talented but limited.

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The harness is the workshop, the tools, the checklist, the supervisor, and the brakes.

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Without the workshop, the worker has nowhere to work.

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Without the tools, they can't build anything.

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Without the checklist, they forget steps.

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Without the supervisor, they make mistakes.

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And without brakes, they burn out.

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The harness provides all that structure.

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So what exactly is inside this harness?

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Let's open it up.

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What a harness actually contains.

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So what goes into a harness?

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Let's walk through the pieces one by one.

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First, the agent loop.

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This is the cycle that keeps the agent going.

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The model thinks then decides to call a tool.

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The tool runs and sends back a result.

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The model looks at that, thinks again, and maybe calls another tool.

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The loop keeps going until the agent reaches its goal.

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Without this loop, you just get a single question and a single answer.

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With it, you have a system that works through problems step by step.

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Next, context management.

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This is critical because models have a limited attention span.

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Their context window can only hold so much before overflow.

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The harness steps in.

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It compresses the conversation, summarizes older messages, and prioritizes what's important.

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Think of it like a filing system for the conversation.

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The harness organizes it so the model only sees what's relevant.

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Without this, long sessions fall apart as the model loses track.

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Then there are tools and skills.

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These are the hands of the agent.

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A model by itself can only generate text.

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It can't read a file, browse the web, run code, or query a database.

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The harness gives it those capabilities.

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File access, web browsing, code execution, database queries, all tools the harness provides.

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The model decides when to use them and the harness makes them work.

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Memory and session persistence is another big piece.

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The harness remembers past sessions.

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So when you come back to an agent tomorrow, it doesn't start from zero.

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It remembers your preferences, your project context, the decisions you made.

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The agent learns over time because the harness keeps that history.

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Guardrails are the safety layer.

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Limits you set on the agent's behavior.

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Think of it as the agent's rule book.

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Maximum steps before it stops.

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Actions it's not allowed to take.

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Human approval gates for high-risk moves like writing files or sending emails.

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Without guardrails, an agent could run forever or do something you didn't intend.

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The harness keeps it in check.

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Finally, orchestration.

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The harness can spin up specialised sub-agents for different tasks.

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One handles research, another handles writing, a third handles verification.

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They work together like a team of specialists on a construction site.

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Each does what it does best.

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And the harness makes sure they don't get in each other's way.

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Now these components aren't just theory.

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Microsoft has built a full platform that puts all of this into practice.

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Enter Microsoft AI Foundry, the Enterprise harness.

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That platform is Microsoft AI Foundry.

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Think of it as a complete workshop for building and running AI agents at scale.

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When I say scale, I mean real scale, not a lab experiment.

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Foundry serves over 70,000 customers today.

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And last quarter alone, it processed 100 trillion tokens.

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That's 100 trillion, not a typo.

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Every day, it powers 2 billion Enterprise search queries.

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This is a production platform handling some of the biggest workloads in the world.

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What Foundry actually does is give you the harness as a managed service.

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You don't build context management, memory, guardrails and coordination from scratch because

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it's all baked in.

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You bring your agent and Foundry provides the workshop around it.

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Now let's talk about identity.

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This is a big deal.

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Every agent in Foundry gets its own Entra agent ID.

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Basically a digital identity.

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Think of it like an employee badge that the reception desk issues.

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The agent can log into services, access data and act on behalf of the organization just like

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a human employee.

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And because every action is tied to that identity, it's fully auditable.

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Why does that matter?

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In an enterprise, you need to know who did what.

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Every action is logged intracable.

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It connects to over 1 400 Enterprise data sources from SharePoint and Dynamics 365 to Salesforce

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and Custom databases.

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If your company uses it, Foundry probably has a connector.

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So the agent isn't guessing or making things up.

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It pulls real data from real systems, which means the answers are grounded in your actual

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business data.

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Then there's memory.

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Foundry includes built-in memory types.

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Procedural memory helps the agent learn from past tasks.

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It gets better over time, like on the job training.

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Session persistence means it remembers previous conversations, so it's not starting from

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zero every time you talk to it.

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It makes interactions feel continuous and intelligent, so how does this actually work?

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Let's break down the components Microsoft provides for building custom harnesses.

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The agent framework, building custom harnesses.

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So how do you actually build a harness?

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Microsoft provides a dedicated SDK called the Microsoft agent framework, which you might

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know by its earlier name, semantic kernel.

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It's been rebuilt, but the idea is the same.

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It lets developers build custom agent harnesses in Python and CSAT.

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The framework has three layers.

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But the bottom is the agent loop, the core reasoning cycle where the model thinks, calls tools,

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gets results, and thinks again.

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On top of that, sit workflows, coordination patterns that manage multiple agents.

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And wrapping everything is the harness layer itself, the shell that holds it all together.

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Microsoft ships five built-in coordination patterns.

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Sequential runs agents one after another in a defined order.

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Concerned runs them all in parallel, handoff passes control from one agent to another,

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based on what's needed.

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Group chat lets multiple agents talk in a shared conversation.

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And then there's magentech, a manager pattern from Microsoft Research here, a supervisor

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agent generates a plan, and then delegates work to specialize sub agents like a project

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manager assigning tasks to team members.

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Each pattern handles a different coordination problem.

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Here's the best part, the framework is extensible.

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You can plug in any model you want from OpenAI, Anthropic, and Google Gemini to Amazon

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Bedrock and even local models on Olamma.

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Same for tools, you connect via OpenAPI, the model context protocol or direct code.

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Some people worry that using Microsoft's framework locks you into their ecosystem.

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That's not true.

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The framework works with whatever stack you're already using.

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The harness layer adds capabilities like file access, code execution, planning, middleware,

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and telemetry.

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So building an agent with this framework means building a complete system that reads files,

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writes code, plans, multi-step tasks, and logs everything for debugging.

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It's more than just a chat interface.

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Here's what that looks like.

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Imagine a coding agent built on this framework.

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You give it a task, say, add a new feature to an existing code base.

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The agent starts by browsing documentation to understand the API.

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Then it writes the code, then it runs tests to make sure nothing broke.

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If a test fails, it reads the error, fixes the bug, and runs the tests again, all within

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the same harness, looping through the think-act check cycle until the job is done.

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Without the harness, you'd manually feed each step.

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With it, the agent handles the whole workflow on its own.

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One popular example of a harness in action is the claw pattern.

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Microsoft has made it available on Foundry, and it's a great demonstration of how these

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pieces come together.

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They're all digital style agents and Hermes.

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Let's talk about claw style agents.

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The name comes from open source projects like OpenClaw, these agents live on your machine,

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wait for instructions, and then run long tasks on their own.

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They're always there ready to jump in when you need them.

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Think of them as a personal assistant that never takes a day off.

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Microsoft's version is called Hermes.

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It runs inside Foundry's hosted agent sandbox and comes with some impressive abilities.

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Hermes has its own file system, its own memory, its own set of tools.

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It can set up its own maintenance routines, cleaning up old files, organizing its workspace.

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And if it realizes it needs a new skill it doesn't have, it can build one on the fly.

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Now here's the thing about claw agents.

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Cloud architects call them pets, not cattle.

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Pets are unique.

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You name them, you care for them, you can't easily replace them.

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Cattle are interchangeable, so if one goes down, you spin up another.

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Claw agents are definitely pets.

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Each one has its own state, history, and learned behaviors.

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That uniqueness makes them powerful, but it also makes recovery and scaling harder.

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If something goes wrong, you can't just throw away the instance and start over because

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you'd lose all that accumulated knowledge.

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Microsoft's solution is something called routines.

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These are scheduled tasks that let the agent wake up, do maintenance work, and then go

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back to sleep.

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Instead of keeping the sandbox running 24/7, which costs money.

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The agent shuts down after a period of inactivity.

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The routine system wakes it up when it's time to do something, like clean up old skills,

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run a backup, or check for new tasks.

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Then it goes back to sleep.

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This saves cost while keeping the agent available when you need it.

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The sandbox isolates each session.

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Your agent's files, memory, and configuration all stay contained in its own environment,

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and the harness handles managing that state across sessions, so you don't have to worry

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about one agent's data leaking into another's.

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Building your own harness from scratch is possible, but most businesses don't need to.

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Foundry gives you a ready-made platform with all these capabilities built in.

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The real question is, what do you want your agent to do?

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Building and collaboration where harnesses meet humans, so you've built your agent given

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it tools, memory, orchestration, and a sandbox.

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Now how do people actually use it?

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Here's where Foundry makes things easy.

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Once your agent is built and deployed, you can publish it to Microsoft 365 Co-Pilot and

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Microsoft Teams with one click, not 10 clicks, not a three-day deployment pipeline, just

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one click.

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And your agent shows up right where people already work.

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In their email, their chat and their documents.

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Foundry supports two deployment modes.

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First, assisting agents.

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These work on your behalf, drafting emails, scheduling meetings, or pulling up documents.

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They act as an extension of you, like having a really fast assistant who does what you ask.

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Second, autopilot agents.

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These are newer and more interesting.

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They act on their own behalf with their own email address and their own identity.

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They don't need you to prompt them every time they take initiative.

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Autopilot agents have a full user account.

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You can add them to a Teams group chat just like you'd add a human coworker.

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They follow the conversation and jump in when they can help tracking open items, answering

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questions about what the team is working on.

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One on-boarding new team members by sending them the right documents and introducing them

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to the right people.

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Here's a concrete example.

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Say you have a "Workstream Manager" agent.

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You add it to your Teams group chat.

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Someone says, "Hey, I need the Q3 report finished by Friday."

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The agent notices this is an action item.

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It creates a task, assigns it to the right person, and adds a reminder for Thursday to check

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on progress.

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Nobody told it to do that.

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It just saw something that needed doing and did it.

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The harness handles all the permissions behind the scenes.

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Only authorized users can interact with the agent in a group chat.

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The agent sees messages, it's allowed to see and ignores the rest.

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It respects the same access controls your human team members do.

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Here's why harness engineering really pays off.

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The agent stops being a chatbot you open in a browser tab.

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It becomes a productive team member.

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It lives where you live, works the way your team works.

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And it does all of that because the harness, the system around the model, handles identity,

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permissions, deployment and integration.

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Without the harness, you'd just have another chat window to check.

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But any powerful tool needs safeguards.

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Let's talk about the guardrails that make all of this safe.

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Responsible AI and governance in the harness.

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Here's the thing.

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Harness engineering isn't just about making agents more capable.

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It's about making them safe and trustworthy.

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An agent that can send emails, modify files and acting group chats is powerful, but that

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power needs controls.

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Think of it like giving an employee keys to the building.

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You want them to do their job, but you also set rules about what they can access and

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when.

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Microsoft's approach is built on the responsible AI standard.

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Six principles guide everything.

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Harness, reliability and safety, privacy and security, inclusiveness, transparency and accountability.

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These aren't just nice ideas written on a poster.

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They are the actual foundation for how the entire platform is designed from the ground

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up.

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The harness is where these principles become real code.

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Guardrails limit what the agent can do.

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Life cycle hooks let you enforce policies at every step of the agent's execution before

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the agent runs.

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After it runs, before it calls a tool, after it gets a result, you can inject your own

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checks at any point.

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You can do that.

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Want to require a human approval before the agent sends an email?

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You can do that too.

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Both of those are straightforward to set up.

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As you as well architected framework includes dedicated guidance for AI workloads.

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It covers everything from data encryption to incident response.

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The idea is that building an AI system isn't fundamentally different from building any

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other enterprise system.

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You still need security, reliability and operational excellence.

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The well architected framework gives you a structured way to think about all of that

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without getting lost.

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Beyond that, there's the cloud adoption framework.

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It provides a four-stage process for AI governance.

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First identify risks.

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What could go wrong?

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Second measure impact.

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How bad would it be?

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Third mitigate with controls.

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What can you put in place to prevent it?

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Fourth operate with monitoring.

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How do you catch problems when they happen?

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It's a practical step-by-step approach that turns abstract concerns into concrete actions

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you can actually take.

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For agent harnesses specifically, this means a few key things.

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Maximum step limits so the agent can't run forever.

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Two restrictions so it can only access what it's supposed to.

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Human approval gates for high-risk actions audit trails so every decision is logged and

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reviewable, content safety filters that catch problematic outputs before anyone sees them.

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Every piece works together to keep your agent on a short leash.

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Microsoft provides built-in content safety and evaluators inside Foundry.

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You can test your agent against a set of criteria before you deploy it.

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Does it stay on topic?

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Does it handle sensitive data properly?

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Does it refuse requests?

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It shouldn't fulfill?

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You can run these evaluations automatically and get a score before you ever publish the

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agent to your team.

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It's a bit where you catch problems early, not after something goes wrong.

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This is the full picture.

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From a simple prompt to a production grade safe autonomous agent, the model does the thinking,

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the harness does everything else, the coordination, the guardrails, the governance.

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Everything that makes it trustworthy.

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So let's recap the transformation.

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We started with one-shot prompting.

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Write a prompt, get an answer.

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That worked for simple tasks.

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Then we moved to context engineering, manage what the model sees, load the right information

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at the right time.

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That worked for more complex tasks.

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And now we've arrived at full harness engineering, build a complete system around the model with

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memory tools, coordination, guardrails and governance.

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Here's the key takeaway.

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An agent is only as reliable as the system around it.

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The model matters absolutely, but the harness is what makes or breaks production.

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You can have the best model in the world, and without a good harness, it will fail on real

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tasks.

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And you can have a decent model with an excellent harness.

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And it will outperform expectations every time.

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Here's your challenge.

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In your next AI project, think beyond the prompt.

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Don't ask what should I say to the model?

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Ask what system do I need to build around it?

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That shift in thinking is what separates demos from production systems.

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In the next episode, we'll dive deeper into one harness component, memory, how agents learn

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over time, how they remember what they've done, and how that changes what they can accomplish.

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Subscribe so you don't miss it.