The Copilot Assumption That's Holding You Back — Agentic RAG

Before we get into it, quick ask, if you're new here, subscribe to M365FM. We break down Microsoft 365 like this every week, no fluff, just the stuff that actually changes how you work. Okay, here's the assumption. Most people think co-pilot's job is to answer questions. You type something in, it goes and finds a document, it gives you back an answer. Simple. Clean, that's what everyone assumes it does because that's what it looks like it does. That assumption is broken. And it's costing you the real value of the platform because co-pilot isn't a search box anymore. It's becoming something closer to an orchestrator, a system that decides where to look, how many times to look, and what to do once it finds something. Most people using co-pilot right now have no idea that shift already happened. By the end of this episode, you're going to understand a gentec rag well enough to spot it when you see it. Question it when someone pitches it to you and actually use it right instead of just turning it on and hoping. Let's get into why the old assumption held for so long. Why everyone thinks co-pilot is just search? Here's the mental model almost everyone walks in with. It comes from Google. It comes from SharePoint Search. You type a question, something goes and fetches the document and you get an answer back. Done. Transaction complete. And look, that model isn't wrong exactly. It's just incomplete. It's describing one specific architecture and that architecture has a name, retrieval augmented generation. Traditional rag, one shot, one answer. Let me walk you through the loop because once you see it, you'll recognize it everywhere. You ask a question that query gets converted into something called an embedding, basically a mathematical fingerprint of what you're asking. That fingerprint goes and searches a vector database which is just a specialized store of your documents chunked up in index the same way. Whatever chunks come back gets stuffed into a prompt along with your original question. That whole package gets sent to the language model and the model does exactly one thing with it. It generates an answer. That's it. That's the entire loop query, vector database context prompt, LLM output, one pass, front to back and you're done. And here's the thing. This actually works great for a huge chunk of what people ask. What's our PTO policy? What's the Wi-Fi password for the Denver office? Where do I submit an expense report? These are FAQ style questions. One clear question. One document that has the answer, no ambiguity about where to look. Traditional rag handles this beautifully. Fast, cheap, predictable. But here's where most people miss something important. In that entire loop, the language model only gets used once and only for one job, summarizing what got retrieved. It's not deciding anything. It's not choosing which database to check. It's not looking at the first result and thinking, "This isn't enough. Let me try again." It's a passive tool sitting at the end of a pipeline, waiting to be handed context, so it can write a nice paragraph. Think about what that actually means. All the intelligence in the system, the reasoning, the language understanding, the thing that makes an LLM, an LLM, gets used for exactly one narrow task. Summarization. Everything before that point, the actual decision of what to retrieve and where, is hard-coded. A developer wrote that logic in Python months ago and it hasn't changed since. That's fine when the question is simple in the source is obvious. But that setup has a ceiling, and the moment a question doesn't fit neatly into one query, one document, the whole thing starts to wobble. The moment rag breaks. So let's test that ceiling with two questions. First one, what's our remote work policy? Easy. There's a document somewhere in SharePoint called Remote Work Policy, or it's a section in the Employee Handbook. One query, one source, one clean answer. Traditional rag nails this every time. Now try this one. Why did my invoice go up this month? Stop and actually think about what has to happen to answer that well. You need the current invoice. You need last month's invoice to know what changed. You probably need usage data because maybe the increases from higher consumption, not a price change. You might need the contract terms in case there's a rate adjustment clause that kicked in. That's four different sources minimum and none of them live in the same place. Traditional rag can't do that. Not because the model isn't smart enough to reason through it, but because the architecture never gives it the chance. Remember the loop from a minute ago query goes to the vector database. Whatever comes back is what the model gets end of story. There's no step where the system pauses and asks, wait, do I actually have everything I need here? It just looks where it's told to look, gets one batch of chunks and hands them off. This is the actual structural floor and it's worth saying plainly. Single-shot retrieval assumes the question is simple and assumes the source is obvious. That assumption holds for PTO policies. It falls apart completely for anything that requires connecting pieces across systems. And here's what that costs you in practice. It's not just that copilot gets a slightly worse answer. It's one of three things and none of them are good, wrong answer, where it retrieves the invoice but not the usage data and gives you a confident explanation that's flat out incorrect. In complete answer where it grabs part of the picture and presents it like the whole picture so you walk away thinking you understand what happened when you don't or the one that actually damages trust the fastest. I don't know. Copilot tells you it can't find the information even though the answer exists somewhere in your tenant. It's just spread across sources. The single-shot pipeline was never built to check. That last one is the killer because people don't blame the architecture. They blame the tool. They say copilot's not good enough. Copilot doesn't have access to our data. Copilot isn't ready for real work, but the data was there the whole time. The retrieval step just never went looking for it because looking again wasn't part of the design. So here's the question that actually matters. What if the model wasn't locked into look once, wherever you're told and report back? What if instead it could look at the question, decide for itself what it actually needs, go get the first piece, evaluate whether that's enough and if it's not, go look again somewhere else. That's not a hypothetical. That's a different architecture entirely and it's what we're covering next. What a genetic rag actually is. Here's the definition, plain and simple. A genetic rag turns the LLM from a responder into a decision maker. That's the whole shift right there. In the old model, the language model waits for context and writes a summary. In this model, the language model decides what context it even needs. It chooses which data source to query first. It looks at what comes back and decides whether that's enough or whether it needs to go query again somewhere else with a different question. It's not a passive endpoint anymore. It's running the show. There's a name for how this actually works. And it's worth knowing because you'll see it referenced constantly if you read anything technical about this. The React pattern. Reason, then Act, then Reason again. The model thinks through what it needs. Takes an action, an actual retrieval or a tool call. Looks at the result and reasons about that result before deciding its next move. Reason, Act, Reason, Act in a loop until it decides it has enough to answer. Put the two models side by side and the difference is stark. Old model, one shot look up. Ask, retrieve, generate, done, no matter what. New model, an iterative, self-correcting loop where the system can recognize this isn't enough and go back for more on its own without a developer having to hard code that specific path in advance. Now here's why this matters so much specifically for Microsoft 365 and not just as an abstract AI concept. Think about where your organization's actual knowledge lives, policies and project docs in SharePoint, conversations and decisions buried in Teams threads, commitments and context sitting in Outlook, structured business data in Dataverse, maybe a CRM, maybe a ticketing system. None of these were designed to be searched the same way. SharePoint is document-centric. Teams is conversational and time-ordered. Outlook is threaded and personal. Dataverse is structured, relational, nothing like a document at all. A single-shot retrieval system has to pick one of these in advance, hard-coded and hope the answer lives there. That's the whole problem we walked through with the invoice question. The answer wasn't missing. It was just scattered across systems that don't speak the same retrieval language. An agentic system doesn't need that upfront guess. It can look at your question, reason about what kind of information would actually answer it and go check SharePoint first. And if that's thin, pivot to Dataverse. And if it still needs more, pull recent Teams context too. Multiple sources evaluated in sequence, not a single role of the dice on one database. This is the actual architectural leap that Microsoft has been building toward. It's not co-pilot got smarter at writing. It's co-pilot getting the ability to make retrieval decisions the way a person would. If you asked a coworker to go find something and they knew which system to check first and knew to keep looking if the first place came up empty. So now the natural question is, what does this actually look like in practice step by step inside a real co-pilot session? Because the model makes decisions is easy to say and much less obvious to picture. Let's walk through the orchestration loop itself piece by piece and see exactly where the reasoning happens and where the action happens. The orchestration loop, step by step, picture a real question hitting co-pilot, something like, pull together everything we know about the meridian account before my call in an hour. The first thing that happens isn't retrieval, it's query understanding, the system passing what you're actually asking for, not just the words but the intent behind them. Everything we know isn't a search term, it's a scope. Co-pilot has to figure out that scope means recent emails and you open documents maybe a contract maybe notes from the last team's call, that's already more interpretation than a traditional rag system ever attempts. Then comes the part that didn't exist before, planning. This is where the model decides what it needs before it goes looking. Not after, before it's building something like a mental checklist, contract terms probably in SharePoint, recent conversation history, probably an outlook or teams, account status maybe in a connected CRM. The planning step is the model committing to a strategy instead of just firing off one query and hoping. Once that plan exists, you get multi source retrieval and this is where it starts looking genuinely different from anything traditional rag could do. Co-pilot might hit SharePoint first, pull the contract, check it against what it's looking for, then it goes to Graph. Microsoft's connective layer that ties together your emails, calendar, chats and files to pull recent interactions tied to that account. If there's a connector, set up to a line of business system, maybe a CRM holding deal status or support tickets, it goes there next. Three separate systems, three separate retrieval calls, all in service of one question. Now here's the part that actually matters most, the loop. Say the first pass through SharePoint only turns up an old version of the contract. A traditional system would just hand that over and call it done, wrong or not. An agentex system evaluates what it got back. It asks essentially, does this actually answer what I was asked? If the answer is no or even not fully, it doesn't stop. It refines the query, maybe narrows the date range, maybe searches for an amendment instead of the original contract and tries again. It can do this multiple times, adjusting each pass based on what the last one revealed. That loop is the entire ball game. It's fundamentally different from a single vector database lookup, because a single lookup has no concept of good enough. It just returns whatever is closest to the query embedding and moves on. There's no evaluation step, no second attempt, no course correction. One shot, whatever you get is what you get. After the retrieval loop settles, once the system decides it actually has what it needs, you hit tool execution. This might mean running a calculation, checking a status field, formatting data from a structured source into something readable. Then summarization, where the model finally does what people assume it's been doing the whole time, turning everything gathered into a clear readable answer. And last, safety checks verifying the response doesn't expose something it shouldn't, respects permissions, and stays inside whatever governance rules apply to that data. Query understanding, planning, multi source retrieval, tool execution, summarization, safety checks, six stages, not two. This is the react pattern we named earlier actually running. Reason about what's needed, act by retrieving from a source. Reason about whether that's enough, act again if it's not. Every step in that pipeline is either a reasoning moment or an action moment alternating. Until the loop resolves itself, that's the mechanical difference between the two architectures laid out step by step. And once you can see the loop like this, you start to understand why this isn't just a faster or smarter version of search. It's a different category of system entirely, and that difference has consequences way beyond how fast you get an answer. Why this isn't just a technical detail. Now step back from the mechanics for a second, because if you're the person making budget decisions or signing off on rollout, none of what we just walk through matters. If you can't answer one question, so what? Here's the so what? This isn't an engineering nuance. It's not a detail for the IT team to sort out while everyone else keeps working the same way. This changes what co-pilot can actually be trusted to do. Under the old model, co-pilot answers questions. That's the entire job description. You ask, it retrieves, it summarizes, you read the output and decide what to do with it. The human stays in the loop for every single action that follows. Co-pilot hands you information and you're the one who acts on it. Under the new model, co-pilot completes tasks. That helps you find the information you'd need to complete the task. Complets it, pulls the contract, checks the account history, cross references the CRM, drafts the summary, maybe even roots it to the right person, all without you touching each individual step. That's not a faster question answering tool. That's a different category of system, which is really the shift happening here. Co-pilot is moving from a Q&A tool towards something a lot closer to a digital employee, not in some sci-fi sense. Nothing mysterious about it just practically. An employee doesn't wait for you to specify exactly which database to check. You hand them a goal and they figure out the path. That's what the orchestration loop from the last section is actually doing. It's the difference between a tool you operate and a system you delegate to and that word delegate is exactly where this gets uncomfortable for anyone responsible for governance. Because the moment co-pilot can act instead of just answer, a new question shows up that never existed with traditional reg, who's accountable for what it does. If co-pilot answers a question wrong, that's a bad answer, annoying but contained. If co-pilot completes a task wrong, sends the wrong file, pulls data it shouldn't have touched, roots an approval to the wrong person, that's not a bad answer anymore. That's an action with consequences and somebody has to own it. Not the model, a person, a policy, a framework that was actually thought through before the agent started acting on its own. We're going to come back to that governance question directly because it deserves its own real conversation, not a footnote. But before we get there, it's worth grounding all of this in something harder than architecture diagrams, actual performance numbers. Because right now this whole conversation has been conceptual and conceptual arguments are exactly the kind of thing that fall apart. The second someone asks, "Okay, but does it actually work better?" Let's look at what the data says. The performance data nobody is talking about. Let's start with the number that gets people's attention fastest. Agenetic Rags shows 2.8 times higher task completion on long horizon tasks, meaning anything requiring more than 10 steps to resolve, not a marginal improvement nearly triple. Think about what that means in practice. A task like the Meridian account example from earlier, pulling contract terms, cross referencing conversation history, checking CRM status, that's a long horizon task by definition. It's not one lookup, it's a chain of them. And on that kind of work, traditional single-shot retrieval just falls over far more often than the agentic loop does. Nearly three times more often, according to the benchmark data. Then there's the number that matters even more if you care about trust. Hallucination Reduction - Agenetic Rags cuts hallucinations by 47% compared to traditional rag. Almost half, that's not a small footnote. Hallucination is the thing that actually kills adoption inside organizations. Because one confidently wrong answer does more damage than 10 honest, I don't know responses. And the reason agentic architectures cut that number nearly in half comes straight from the loop we walked through earlier. The evaluation step, where the model checks whether what it retrieved actually answers the question, catches a huge share of the errors that a single-pass system would have just shipped straight to you. So far this sounds like a clean win, it isn't. Here's the trade-off. And it needs to be named immediately, not buried three sections later. Agenetic Rags is 2-10 times more expensive and noticeably slower. That's a real range and it's wide for a reason because the cost depends entirely on how many reasoning loops a given task actually requires. But even at the low end you're paying double for every query that goes through this pipeline instead of the traditional one. One benchmark makes this trade-off concrete in a way that's worth sitting with. Iterative Agent Rag, the fully looping, self-correcting version, came in roughly 10 times more expensive and four times slower than traditional retrieval. But it also cut the hallucination rate from 23% down to 8%. Same task, same underlying data, wildly different cost and wildly different reliability. So here's what that trade-off actually forces you to confront. It's not a question of which architecture is better, full stop. That framing doesn't survive contact with the numbers. A system that's 10 times more expensive isn't better. It's better for something specific at a cost that has to be worth paying. The real question isn't which is better. It's which task deserves which architecture. Because if you're answering a PTO question, paying 10 times more and waiting four times longer for a marginal accuracy gain is a bad trade every time. You're making a decision that involves real money or real risk and getting a wrong cost you a client or a compliance violation that same trade-off flips completely. The extra cost isn't overhead anymore. It's insurance. That reframing architecture matched to task, not architecture chosen by default is the thing most people skip. And it's exactly why the next place this breaks down isn't in the data. It's in the assumption that a GENTIK is simply the upgrade. When traditional rags still wins. So let's push back on the hype for a second because everything we've covered so far could easily be read as "agentic rag wins" always end of story. It doesn't and treating it that way is exactly how organizations waste money. Take a simple factoid query. What's the guest Wi-Fi password? What time does the Denver office close on Fridays? Run that through an agentic pipeline and you get what? A planning step deciding it needs to check a source. A retrieval call. An evaluation step asking whether the result is sufficient. All of that reasoning overhead for a question with one obvious answer sitting in one obvious document. Not only does that add cost for no reason, it can actually make things worse. Slower. For the exact same answer you would have gotten from a single look up in a fraction of the time. The agentic loop doesn't know the question was simple. It runs its full process anyway unless something upstream tells it not to. So you end up paying the latency tax and the compute tax on a question that never needed either. And here's where it gets expensive fast, not slowly. Multiply that overhead by volume. If every single query coming into your co-pilot deployment triggers a full reasoning loop, planning, multi source retrieval, evaluation, possible requering, your costs aren't going up by some fixed percentage. They're multiplying because each step in that loop is its own model call and model calls are what you're paying for. A help desk, fielding a thousand basic questions a day, doesn't need a thousand reasoning loops. It needs a thousand fast, cheap lookups. So the actual skill here isn't implement a genetic rag. That's not a skill that's a checkbox. The real skill is knowing inside your own organization, which questions are simple lookups and which are genuine multi-step reasoning problems. That's a triage job and it's harder than it sounds because the surface wording of a question doesn't always tell you which category it belongs to. What's my pto balance is simple, why did my request get denied might not be get that triage wrong in one direction and you're running expensive reasoning loops on questions that never needed them. Get it wrong in the other direction and you're back to the invoice problem from earlier where a single short system gives you a wrong or incomplete answer because the question actually needed to reach into three different sources. This is exactly why almost nobody builds a system that's purely one or purely the other, which is why most companies whether they say it out loud or not, land on a hybrid approach. Hybrid rag, the quiet enterprise default. Here's something that doesn't get said enough in conversations about this. Most production systems running today aren't fully agentic. They're hybrid, quietly without much fanfare. Hybrid rag has become the default architecture almost everywhere. This is deployed seriously. The logic is straight forward once you see it. Simple queries get routed to fast, cheap, traditional retrieval, one embedding, one vector lookup, one pass through the model for summarization. No planning step, no evaluation loop, no multiple retrieval attempts, just the classic pipeline, doing what it's always done well, complex or ambiguous queries get routed somewhere else entirely. The agentic path with full planning and iteration. That's where the reasoning loop we walked through earlier actually kicks in checking multiple sources, evaluating whether results are sufficient, requeering when they're not. What makes this work isn't luck. It's that the rooting decision itself becomes something someone actually designed, not something that happens by accident. Somewhere in the system there's a classification step, a moment where the incoming query gets assessed for complexity before it ever reaches a retrieval pipeline. Is this a single hop factoid or does it look like it needs reasoning across sources? That classification determines which path the query takes. Get that routing logic right and you capture the speed and cost benefits of traditional rag on the majority of queries, while still getting the accuracy gains of a gentic rag on the smaller share of questions that actually justify it. Now tie that back to copilot specifically because this isn't an abstract enterprise pattern happening somewhere else. It's effectively the direction Microsoft is building toward with wave three. Instead of treating every query the same way, running everything through one fixed pipeline, regardless of complexity, the architecture is shifting toward tiered response strategies. Simple asks get handled quickly. Anything that looks like it needs multi-step reasoning gets escalated into the fuller orchestration loop with planning, multi source retrieval and iteration. That's not a compromise between two competing architectures. It's the recognition that neither one on its own is the right answer for everything copilot gets asked to do. Copilot wave three, what actually changed? So let's actually name what's happening here because wave three sounds like marketing language until you look at what it's describing underneath. Copilot started as a chat assistant, you typed, it responded, the conversation stayed inside a single box on your screen. That's the version most people still picture when they hear the name. But that version has been quietly replaced by something else, an autonomous multi-step executor, not a chat window anymore, a system that can take a goal, break it into steps and carry those steps out across multiple apps without unirating each one. That's the actual shift wave three signals and it's worth putting in plain words because Microsoft's own language tends to stay pretty abstract about it. Less ask and receive, more delegate and verify. You're not typing a question and waiting for a paragraph back. You're handing off a task and checking the result afterward. The work happens in between and you're not watching it happen step by step anymore. That's a genuinely different relationship with the tool. Ask and receive keeps you in the loop constantly. Every query, every answer, every next question you type based on what came back. You get and verify pulls you out of the middle of the process and puts you at the start and the end of it instead. You set the goal, you check the outcome. Everything in between is the orchestration loop we spent the last few sections walking through. Now, none of this is theoretical or early stage anymore and that's the part worth sitting with for a second. Microsoft 365 Copilot has more than 20 million paid enterprise users, more than 60% of the Fortune 500 are already running it. This is deployed at scale inside the majority of the largest companies in the world. Whatever architecture is running under the hood right now is already touching millions of actual work days, which brings up something subtle that's easy to miss if you're not paying close attention to how this gets framed. Copilot agents are increasingly described both by Microsoft and by the IT teams deploying them as digital employees, not features, not add-ons bolted onto word or outlook, employees. That word choice isn't accidental and it isn't just branding either. A feature does one thing the same way every time you click it and employee gets handed a goal and figures out a path to it using judgment along the way. That's exactly the distinction we've been building toward this whole episode. The move from a tool you operate to a system you delegate to calling it a digital employee is just naming out loud what the architecture already does. But here's the thing about employees, digital or otherwise, you don't just hand someone a badge and unlimited access and walk away, you manage them, you define what they can touch, what they can't and who's responsible when something goes wrong. If Copilot is being treated like an employee at 20 million users and rising, it needs to be managed like one, not eventually. Now agents as digital employees. What is managing an agent actually look like in practice? It starts with identity and Microsoft's answer to that is building it directly into Entra. Here's how it works. Each agent gets its own identity, not a shared service account, not a generic API key sitting in some config file. An actual identity, the same category of object Entra already uses to manage people. That identity carries access controls, specific defined permissions about what this agent can see and touch and it carries an ordered trail, a record of what the agent actually did, when and with what data. Think about what that replaces. Before this, agent access in most organizations meant a service account with broad permissions, set up once, rarely revisited and almost never ordered it line by line. Now the agent shows up in the same identity system as your employees. It can see it, it can restrict it, it can pull its history the same way they'd pull an access log for a person. Compliance and oversight run through Microsoft purview. Purview is what tracks what these agents are doing with your data, whether that's flagging when an agent touches sensitive content, enforcing data loss prevention policies against agent actions, the same way it does against human actions, or giving compliance teams a place to actually review agent behavior instead of taking it on faith. Purview doesn't care whether the thing querying a SharePoint library is a person or an agent, it applies the same policy layer either way. And here's why that matters more than it might sound like on the surface. An agent with tenant wide access and no accountability isn't a productivity win. It's a liability sitting quietly until it isn't quiet anymore. Given autonomous system broad reach into your SharePoint, your outlook, your data verse without identity, without access controls, without an ordered trail, and you've built something that can move fast and touch a lot. With no way to reconstruct what happened if something goes wrong, that's not hypothetical risk, that's the exact kind of gap regulators and security teams have spent years closing for human accounts, and it doesn't disappear just because the thing accessing the data isn't a person, which gets at the actual shift underneath all of this. The old model treated permissions as a people problem, who has access to what, reviewed on a schedule tied to a person's role. The new model has to treat permissions as a people and agents problem together in the same system under the same rules. An agent isn't a separate category you handle differently. It's another identity in the same governance framework, just one that happens to act autonomously instead of clicking through menus itself. And this is exactly the layer most organization skip. It's not that they ignore it on purpose. It's that agente features are the exciting part, the demo that gets applause in a meeting. Identity, access controls, audit trails, purview policies, none of that demos well. So it gets pushed to, we'll set that up later, while the agents already running in production already touching data already accumulating in access history nobody's watching. The real world workflows already running. So let's leave the governance conversation for a second and look at what's actually running right now today inside real Microsoft 365 tenants. Because everything we've covered so far could still sound theoretical if we stopped at architecture diagrams. It isn't theoretical. It's already live. Take email triage. There's a workflow agent pattern running inside outlook right now that watches your inbox checks whether a new message contains a question directed at you. And if it does goes and researches the answer using whatever knowledge sources it has access to not just pulling a can reply actually drafting a full response formatted with the question addressed directly. And it sends that draft back to you for review before anything goes to the original sender on top of that it posts the summary in teams telling you who emailed what they asked and what it's proposing to say that's not a single look up that's passing intent retrieving from more than one place drafting content and routing the results to two different destinations you and teams before a human even steps in. Then there's the personal productivity digest same underlying pattern different shape instead of reacting to one email the agent runs on a schedule scanning across teams email and documents and pulls together what actually matters. An AI news digest that checks the source every morning and delivers a summary to your inbox and teams a scan of your planet tasks that flags anything due today and pings you before you have noticed yourself a rundown of important emails from the last 24 hours. You're not the one hunting through your inbox to figure out what needs attention none of that is one query against one source it's continuous it's multi source and it's proactive instead of waiting for you to ask calendar time blocking works the same way just with a different trigger. This isn't just copilot looking at your existing meetings and finding gaps it's reasoning from your task list add something to a time block list in Microsoft to do something like booking a room for next Friday and the agent has to actually interpret that what does next Friday mean relative to today. What does the task title imply about duration then it writes an actual event into your calendar based on that interpretation that's language understanding feeding a decision feeding an action not a static rule that just checks for open slots. And then there's the one that looks the most like a traditional business process on the surface but isn't underneath form driven approval workflows someone submits a request a trading inquiry and expense exception whatever the form is for. And instead of rooting on one fixed rule the workflow pulls context from three separate systems before it decides where that request goes. Maybe it checks the request is history in one system current volume thresholds in another and account status in the third the rooting decision isn't hard coded to one field on the form it's assembled from context gathered across systems in real time before the workflow branches notice what all four of these actually have in common none of them are single question hitting a single source and coming back with an answer. Every one of them requires the model to check more than one place decide whether what it found is enough and in some cases take an action based on what it concludes that's the orchestration loop we walked through earlier running quietly inside tools people already use every day. And that's really the point worth sitting with here these aren't edge cases anymore some clever demo built to show off what's technically possible they're becoming the baseline the default shape of what a co pilot workflow looks like once you stop assuming it's just answering questions. Industry specific applications step outside Microsoft 365 for a second because this pattern isn't unique to email and calendars the same orchestration loop shows up in industries where getting the answer wrong actually hurt someone and looking at those cases makes the stakes a lot more concrete health care first picture an agent feeling a patient question about a new medication. A single shot system would grab whatever document mentions that drug and summarize it that's not good enough here and everyone in that field knows it a patient asking about side effects might actually be asking whether it's safe given a condition mentioned in their chart or whether it conflicts with something in an active clinical trial they're enrolled in answering that well means cross referencing the patient's own records against trial protocols and current treatment guidelines three sources not one evaluated together before anything gets set back to that patient. Finance works the same way just with a different kind of cost for getting it wrong fraud monitoring built on single anomaly flagging catches the obvious stuff one weird transaction one out of patent login but actual fraud rarely looks like one anomaly it looks like a pattern a small transaction here a login from a new location there a change to account details a few days later none of which trips in alarm on its own an agent that reasons across transaction history instead of flagging isolated events can connect that sequence the way a human investigator would if that investigator had time to check every account personally. It doesn't have that time the agent does manufacturing is maybe the clearest version of this because the sources are so obviously different from each other sensor data tells you a machine's temperature is drifting slightly that alone means almost nothing sensors drift constantly but cross reference that against maintenance logs has this part failed before under similar conditions and against technician notes did someone flag something unusual on the last inspection that never made it into a formal report and suddenly a small sensor deviation turns into an actual actionable prediction. None of those three sources alone gets you there notice the thread running through all three of these it's not the industry that matters here it's the shape of the problem health care finance manufacturing completely different domains completely different data and yet every single one of them needs multiple sources reconciled in real time before good answer or good prediction is even possible that's not a coincidence that's exactly the class of problem agentic rag exists to solve the one traditional single shot retrieval was never built to touch but here's the catch nobody glasses over for the first time. Nobody glasses over for long none of this works if the agent has no way to actually reach the systems sensor feeds clinical databases transaction logs technician notes those don't just sit in a share point library waiting to be indexed getting an agent from reasoning about what it needs to actually pulling it from a live system is a different problem entirely and it's the one we're covering next. The model context protocol shift so here's the actual answer to that reach problem and it has a name mcp model context protocol it's emerging as the standard way for agents to connect to enterprise systems whether that's a clinical database a transaction log or a maintenance tracker running on some line of business platform nobody outside IT has ever heard of think about why a standard even matters here without one every single connection between an agent in a data source gets built from scratch custom code custom authentication custom error handling for every system every time that's not just. It's fragile once it exists because it breaks the moment the underlying system changes something on its end and nobody notices until an agent starts failing silently and it's expensive to maintain because someone has to own that custom connection forever patching it every time either side updates mcp replaces that one off approach with something closer to a shared language instead of building a bespoke bridge to every system an agent might need you build to one protocol and any agent that speaks mcp can use it that's the difference between wiring a new cable for every device your own versus everything just work. There's a phrase that captures this shift better than any technical description api design is the new embedding strategy sit with that for a second because it's a real reframe for years the whole game and rag was about documents how you chunk them how you embed them how you make them searchable that matter because the thing being retrieved was text sitting still waiting to be found but agents don't just need searchable documents anymore they need calable systems things they can query trigger and get structured answers back from if your api isn't designed for that clearly described predictable discoverable it doesn't matter how good your embeddings are the agent can't reach it now none of this means every organization needs a team of engineers hand building mcp connections one at a time that's exactly where copilot studio and the workflow agent come in they give business users a way to define these connections through configuration and natural language instructions not custom code someone who understands the process not the plumbing can be a little bit more difficult to do. Not the plumbing can set up what an agent should connect to and when without needing to be the person who wrote the integration layer underneath it but here's where it gets harder not easier building the connection is only half the problem the protocol gets the agent to the data it says nothing about whether what comes back is actually correct actually current actually safe to act on you can have a perfectly engineered pipe running between your agent and a clinical database and still have no idea whether the answer flowing through it deserves to be trusted that's the real problem waiting on the other side of this not reaching the system. Trusting what it hands you once you do why grounding still matters more than ever let's talk about the word that's been sitting quietly underneath this entire episode grounding because everything we've walked through the planning the multisource retrieval the loop that checks and re checks itself none of it means anything if the answer at the end isn't actually tied to something real. Grounding just means this response comes from your actual enterprise content your contracts your emails your CRM records not from whatever the model happens to remember from its training data that distinction sounds small until you picture the alternative a model answering a question about your remote work policy using general knowledge about remote work policies instead of the actual document sitting in your share point isn't wrong exactly it's just not answering about you it's answering about some average version of every company like yours and passing that off as your policy we already looked at the number that shows why this matters so much. Agentech rag cuts hallucinations by 47% compared to traditional rag that game doesn't come from the model getting smarter in some general sense it comes from grounding getting enforced more aggressively at every step of the loop the evaluation stage checking whether a retrieved document actually supports the claim being made the requerying that happens when it doesn't. Here's the part worth sitting with though because it's a little counter intuitive you think more autonomy means less need to check the model's work it's the opposite more autonomy means more chances for the agent to go off track a single shot system can only be wrong once on one query in one place and a genetic system is making judgment calls at every stage of a multi-step loop deciding what counts as sufficient deciding when to stop looking deciding how to interpret an ambiguous result every one of those judgment calls is a place where drift can creep in. So the more freedom you give the system to reason on its own the more grounding has to be enforced not less it's not a trade off you get to relax on just because the system got more capable this is exactly why safety checks sit at the end of the orchestration pipeline and why they're not optional polish tacked on at the finish. They exist specifically to catch the moment an agents conclusion outpaces what it sources actually support before anything reaches you something in that pipeline is asking whether the final answer can actually be traced back to a real document a real record a real piece of content the agent was authorized to see if it can't that's the failure point those checks are built to catch and this is where the employee framing earns its keep again you don't let an employee act on a hunch and presented as fact not without asking where that came from you expect them to point to the file the email the meeting note that backs up what they're telling you. And agent deserves exactly the same expectation more autonomy doesn't earn it more trust by default it earns it more scrutiny because the more it's allowed to decide on its own the more it needs a way to show its work. The adoption curve reality check so with all of that in mind where does the rest of the market actually stand right now because it's easy to listen to everything we've covered and assume this is already the norm everywhere it isn't not yet the research is direct about this mainstream enterprise adoption of a gentle drag is projected for 20 20 20 20 28 not now not next quarter a couple of years out at least right now most organizations sit in the innovator or early adopter phase not the mainstream phase that matters because those two phases behave completely differently innovators tolerate rough edges they expect to troubleshoot they expect some failures and they treat that as the cost of getting ahead of the curve mainstream adoption assume something closer to a solved problem something you deploy without needing a dedicated team babysitting it we're not there most companies experimenting with this right now are whether they admit it or not still in the tolerate rough edges phase and there's a reason grounding the exact thing we just spent this whole section on matters so much at this specific point in the adoption curve 47% of organizations using generative AI have already had at least one negative consequence from it almost half that's not a hypothetical risk sitting somewhere in the future that's already happened broadly across the market which is exactly why grounded approaches aren't a nice to have feature right now they're the thing standing between early adopter with a rough process and cautionary tail in next quarters board meeting here's the number that should really slow you down though only about 5% of enterprise agent systems that get built average production 5% 95% of what gets built demoed funded staffed never actually makes it to the point where real employees are using it on real work every day that gap between demo deployment is where most agentech rack projects actually die not in the pitch meeting not in the proof of concept where everything looks impressive because someone hand picked the test cases they die in the space between this work when we showed it to leadership and this needs to handle every edge case every day at scale without someone standing next to it that gap is enormous and it's exactly the gap we're going to look at next because understanding why projects die there is the difference between building something real and building another entry in that 95% why most agentech projects never reach production so let's actually break down what's sitting inside that gap because it's hard to operationalize isn't an answer it's a shrug the blockers have names and they show up in roughly the same order every time latency cost reliability complexity operational overhead 5 separate problems and a team can solve 4 of them and still get taken out by the 5th operational overhead is the one that catches people off guard because it doesn't show up until after launch an agent making autonomous decisions doesn't run itself once it's live someone has to watch what it's doing keep it working when an upstream system changes something without warning and figure out why it made a specific call when it doesn't match what a person would have done that's not a one time set up cost and it's a job most teams never actually budget it for because the project plan stopped at deploy not staff the person who babysits this thing every day going forward then there's the complexity trap and this one's almost a right of passage at this point a team build something genuinely impressive shows it off gets the funding gets the go ahead then it hits real usage real usage means edge cases nobody thought to test because the demo only ever ran the cases someone hand picked to make it look good the question phrase slightly differently than expected a document that doesn't quite match the form at the agent was built around a source that's momentarily down the demo never had to survive any of that production does every single day and that's exactly where a lot of these projects quietly stall out now layer something else on top of that something that makes the whole situation worse instead of just harder only 27% of generative AI users review all outputs before use roughly a quarter which means the other three quarters are shipping acting on or forwarding outputs they haven't actually checked think about what that means once an age not a person is the one generating those outputs most organizations are trusting decisions and answers they've never verified from a system that's making autonomous calls about what to check what to trust and when to stop looking nobody's watching most of it and the review rate tells you that plainly that's the actual accountability gap and it's exactly what governance frameworks the identity models the purview oversight the ordered trails we walked through earlier are built to close not because autonomy is inherently unsafe but because autonomy without anyone checking the work is a different kind of risk than a person making the same mistake a person you can ask a system running unreviewed you find out about after the fact none of this is an argument against building these systems it's an argument against building them carelessly the organizations that make it past that 95% failure point aren't the ones that skipped the hard parts they are the ones that planned for the monitoring budgeted for the edge cases and built review into the process instead of assuming the output would just be right build it deliberately not fast. The ROI timeline nobody advertises. Let's talk money, because everything so far has been architecture and governance and at some point somebody in finance is going to ask when this pays for itself. Here's the finding stated plainly. Enterprise is implementing agent-based, rag architectures, often see ROI within three to six months. That's a real number and it's faster than a lot of enterprise software ever pays back. But there's a qualifier attached to it that gets dropped constantly when people repeat this stat in a pitch deck. High volume operations, not every use case, not any use case high volume operation specifically. That qualifier is doing almost all the work in that sentence and it's worth understanding why volume is what makes the payback math actually function. Building an orchestration loop, the planning step, the multi-source retrieval, the evaluation and requiring isn't free. Someone has to design it connected to the right systems, test it against edge cases and keep it running. That's a fixed cost paid once upfront, regardless of how often the thing gets used. Afterward, now run that fixed cost against two different scenarios. In the first, that orchestration runs a few dozen times a day. In the second, it runs a few thousand times a day. Same build cost in both cases. Completely different payback timeline because the value generated each time it runs has to divide against that same fixed cost and dividing it across a few dozen uses a day gets you nowhere fast. Dividing it across a few thousand gets you to that three to six month window. The research is actually describing this is exactly where companies get it wrong and it's a mistake that's easy to make because it doesn't look like a mistake going in. A team picks a process that feels important. Maybe it's genuinely complex. Maybe it genuinely needs multi-step reasoning across sources and builds the full agente pipeline for it. Then it turns out that process only gets triggered 20 or 30 times a day. The complexity was real. The volume wasn't. Six months later, someone's asking why the ROI never materialized and the answer isn't that agente.rag failed. It's that agente.rag was aimed at a target that could never generate enough repetitions to earn back what it costs to build, which is exactly why the decision of where to deploy agente.rag matters as much as how you build it. You can get the architecture, the react loop, the grounding, the governance, all completely right and still lose the ROI argument because you pointed all of that engineering at a process that runs 30 times a day instead of 30,000 efficiency techniques making this viable. Now, everything we've said about cost and latency so far has been treated as a fixed tax, something you pay because that's what agente reasoning costs. That's starting to shift and it's worth understanding how because it changes the volume math we just walked through. Take this adapt. What it does is adapt how much reasoning the model actually applies based on how hard the question is instead of running the same heavy process on every query regardless of difficulty. The result up to 22.4% token reduction while keeping accuracy comparable. That's not a rounding error. That's cost control without sacrificing the output quality you were getting before. Then there's focused chain of thought reasoning, sometimes shortened to F-coating. Standard chain of thought has the model reason through a problem in a fairly loose exploratory way which works but burns time and tokens on paths that don't end up mattering. Focused chain of thought structures that reasoning process more tightly and the result is inference running two to three times faster than standard chain of thought again without giving up accuracy to get there. Here's why this actually matters if you're the one making the build or don't build call. Everything we covered earlier that two to ten X cost multiplier, the multi second latency gap between traditional and agente pipelines, all of that was measured against today's baseline. That baseline is not fixed. It's shrinking as techniques like these mature and get folded into how these systems run by default and this isn't some academic footnote sitting in a research paper nobody outside a lab will ever read. These are the kinds of optimizations vendors are already building into production co-pilot systems right now quietly under the hood without necessarily calling attention to it which means the tradeoff calculation we walked through a few sections back weighing whether a process has enough volume or complexity to justify the agente overhead gets easier to justify with every one of these efficiency gains that ships. But token savings and faster inference only address one axis of this they make a single agents reasoning loop cheaper and quicker they don't touch what happens when the problem itself outgrows what one agent reasoning alone can actually handle that's a different kind of scaling question and it's the one waiting on the other side of this where this is headed multi agent orchestration so one agent choosing between data sources reasoning in a loop checking its own work that's everything we've covered so far but that's not where this stops the next layer isn't a smarter single agent its multiple agents coordinating with each other picture it this way instead of one system that plans retrieves and verifies you get separate agents built for separate jobs one that specializes in retrieval one that specializes in verification one that handles a specific line of business system and they hand work back and forth instead of one model trying to do all of it alone that's a different shape of problem than anything we've walked through this episode because now the question isn't just did this agent make a good decision it's did these agents coordinate well enough to avoid stepping on each other there's early research into decentralized versions of this agents working as a network rather than reporting up to one central decision maker no single orchestrator sitting on top directing traffic instead agents that discover each other specialize over time and collaborate directly closer to a network than a hierarchy it's early and it's worth naming without overselling it but it's the direction a meaningful chunk of the research is pointing here's the part that actually changes what retrieval even means going forward applications themselves are becoming calable resources not just data sources sitting there waiting to be searched that's a real shift from everything we covered with mcp a few sections back it's not just that an agent can reach into a system and pull data out it's that agents can trigger other agents or trigger entire services directly as part of completing a task the line between look something up and make something happen starts to disappear because both are just actions the agent can take at any point in its reasoning now tie that back to co-pilot specifically because this isn't some distant hypothetical sitting in a research paper workflow agent and co-pilot studio are already pointing exactly this direction we already walked through agents that can call workflows as tools and workflows that can call agents as steps that's the early shape of this it's not agents talking to a database it's agents talking to other agents and to other workflows deciding at runtime which one to invoke based on what the task actually needs which gives us the through line for the entire episode laid out plainly retrieval one document one answer then reasoning a model that plans chooses iterates then autonomous action a system that doesn't just answer but does and now coordinated systems of action multiple agents working together on pieces of a problem too large for any one of them to hold alone that's the actual arc underneath everything we've covered and it's still being written what this means for your co-pilot strategy so pull back up to the level where decisions actually get made because none of this matters if it doesn't change what you do Monday morning this isn't about chasing every new feature Microsoft chips it's tempting to treat each wave each new capability announcement as something you need to adopt immediately just because it exists that's not a strategy that's just reacting to a release calendar and reacting to a release calendar is exactly how you end up with a kind of unmonetored ungoverned agent sprawl we spent half this episode warning against the real strategic question is narrower and it's the one worth actually sitting with which processes in your organization are complex enough to justify a gentick or a ag and which are better served by simple retrieval we've spent this whole episode building the case for both sides of that question some of your workflows are genuinely multi-step multi-source and worth the cost of orchestration a lot of them aren't and forcing a gentick architecture onto a simple lookup just burns budget for no gain governance isn't optional overhead sitting on top of the fun part it's the difference between a copilot rollout that scales and one that gets shut down after an incident identity access controls audit trails none of that is bureaucracy standing in the way of progress it's the thing that let's progress keep happening after the first mistake instead of getting frozen the moment something goes wrong and nobody can explain why here's what's actually separating the organizations getting real value from this right now it's not speed the organization's winning with this aren't the ones moving fastest they're the ones matching the architecture to the actual task deploying a gentick rag where the volume and complexity earn it back and leaving simple retrieval alone everywhere else which brings us right back to where we started the assumption we opened with was simple copilot's job is to answer questions type something in get something back done that assumption held up fine right up until a gentick rag entered the picture and the moment it did copilot stopped being a question answering tool it became something that plans checks multiple sources decides whether what it found is good enough and in a growing number of cases actually does something with the answer instead of just handing it to you so here's what to do with that starting now not someday pick one copilot workflow you're running today anyone and ask it a single question does this actually need retrieval or does it need reasoning if it's a simple lookup one source one clear answer leave it alone simple retrieval is cheap and it works but if you find yourself explaining why the answer came back incomplete or wrong or missing entirely there's a decent chance you're asking a single short system to do a multi-step job that's worth auditing this week not next quarter if this changed how you think about copilot's architecture leave a review it's the fastest way to help someone else find this before they build the wrong thing at scale and if you want to keep pushing on this connect with me mercopieters on LinkedIn tell me what you're running into tell me what you want covered next this show gets let's build from those conversations.