Stop Building Dumb Copilots: Why Agentic RAG Is Your Only Fix

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Your copilot isn't smart, it's well-dressed, autocomplete,

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an over-caffeinated intern in a Microsoft badge

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who sounds confident while being utterly lost.

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The average admin installs it,

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asks one ambitious question like,

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summarized last quarter sales across regions,

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and then acts surprised when it responds with,

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whatever happens to fit in its limited context window.

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That's not inside, that's statistical guesswork delivered

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in a friendly tone.

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See, most so-called AI copilots run on something

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called retrieval augmented generation, rag for short.

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In theory, it's brilliant.

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You ask a question, it searches a knowledge base,

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grabs relevant chunks and glues them to your prompt,

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so the language model looks informed.

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In practice, it's like asking a single librarian

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for all human knowledge.

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She sprints down one aisle, grabs three random books

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and yells the answer while running back,

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one query, one retrieval, one chance to be wrong.

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Now contrast that with how actual enterprise decisions work.

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Do you ever need just one piece of data?

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No, you need the spreadsheet from finance,

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the research report from SharePoint,

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the metrics warehouse in Microsoft fabric,

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and usually some external market data

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that isn't even in your tendency.

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Classic rag collapses the moment your truth lives

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in more than one place.

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That can't plan multiple searches, can't validate contradictions,

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and certainly can't understand

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that quarterly performance means different things

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to marketing and manufacturing.

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Yet people keep calling these systems intelligent.

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Wrong, their context tourists.

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They visit your data estate, take a few selfies with PDFs

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and pretend they know the city.

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Meanwhile, the average admin honestly believes

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copilot has omniscient access to every SharePoint folder.

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It doesn't, unless you build explicit connectors and reasoners,

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it's operating blindfolded.

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This isn't just inefficient, it's dangerous.

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Without agent retrieval, enterprises drown

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in context fragmentation, decisions get made on partial data.

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Compliance risks go unnoticed.

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Teams chase hallucinated insights produced by a model

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that never bothered to double check itself.

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The irony, the fix already exists, enter agent rag.

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It doesn't just fetch information, it thinks through it.

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It plans, cross checks, and reasons

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like a digital research team.

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By the end of this episode, you'll know exactly

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how to make your copilot stop acting like a parrot

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and start behaving like a scientist.

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And yes, there are four steps.

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We'll fix your dumb copilot in four precise steps.

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The rag myth, why linear intelligence fails.

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All right, let's dissect the myth.

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Retrieval augmented generation sound sophisticated,

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but under the hood, it's brutally linear.

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Step one, retrieve a few slices of text

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based on vector similarity.

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Step two, stuff those slices into the prompt.

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Step three, generate an answer and declare victory.

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That's it.

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No memory of previous searches, no reasoning

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about contradictions, no recognition of user context.

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It's a straight line from query to answer

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a glorified SQL join written in English.

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The problem begins, the moment reality gets messy.

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Say you ask, compare our device reliability with competitors.

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A vanilla rag system hits one index, grabs any document

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mentioning reliability and spits out a summary.

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Did it check manufacturing logs in fabric?

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Did it read that new testing report buried in SharePoint?

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Did it validate external data from the web?

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Of course not.

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It doesn't even know those sources exist.

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It just paints over uncertainty with eloquence.

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Think of it like a library analogy turned tragic.

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You walk in and ask one librarian about global economics.

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She runs to a shelf labeled economics.

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Hands you a random book about currency exchange from 2012

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and declares the question solved.

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Good customer service, terrible scholarship.

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Real intelligence would recruit multiple librarians,

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one for finance, one for history, one for policy,

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and then synthesize their findings.

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That's the difference between retrieval and reasoning.

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In enterprises, this failure multiplies.

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A single executive question often touches dozens of systems.

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SharePoint documents, Power BI data sets,

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Azure SQL tables, email threats, classic rag

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flattens, all that complexity into a single hop.

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The result, inconsistent outputs, shallow summaries,

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and hallucinated data phrased with confident diction.

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Regulatory compliance, forget it.

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When the model pulls from whatever text happens to vector match,

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you lose povenants, try explaining that to your audit committee.

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Yet the marketing around co-pilot makes it sound omnipotent.

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The brochures whisper, ask anything, co-pilot knows your business.

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No, it doesn't.

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It performs text retrieval with amnesia.

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It can't plan, reflect, or verify.

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It doesn't know which SharePoint site contains the right document

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or whether the fabric warehouse is even synchronized.

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But because the phrasing is smooth, executives assume comprehension

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where there is only correlation.

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This illusion of intelligence is what companies are buying into.

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An expensive comfort blanket woven from probability distributions.

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They celebrate when co-pilot drafts an email correctly,

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ignoring that it misunderstood the source data entirely.

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They brag about AI-driven insights

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without realizing those insights are assembled

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from mismatched contexts and partial snapshots.

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Here's the economic consequence.

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Every mis-summarized report, every hallucinated KPI,

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cascades into poor decisions.

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Projects pivot based on fiction.

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Compliance teams chase ghosts.

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And when reality catches up, when the fabricated inside

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fails in production, the blame falls on AI limitations,

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not on the lazy architecture that ensured failure.

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The truth, linear intelligence fails because enterprises aren't linear.

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Data is distributed, contextual, and often contradictory.

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A fixed one-query pipeline can't adapt to that environment

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any more than a single neuron can think.

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What you need isn't a better prompt.

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You need a system that can plan.

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One that can decide which services to use in what order

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and how to verify the outcome.

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So how do we teach a co-pilot to operate

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like a research team instead of a parrot?

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That's where agentech rag enters.

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The evolutionary leap from reactive retrieval to proactive reasoning.

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It adds layers of planning, specialized retrievers,

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and verification loops.

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In other words, it stops pretending to be smart

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and finally learns to think.

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Enter agentech rag from search to reasoning.

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Here's where we move from gimmick to intelligence.

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Agentech rag isn't another buzz word.

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It's the missing faculty or co-pilot was born without.

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Executive function.

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Think of it as rag that grew a prefrontal cortex.

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Instead of running one query and crossing its digital fingers,

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it breaks a problem into parts, assigns tasks

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to different specialist agents, checks their work,

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and then synthesizes the outcome.

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In short, it converts language models from parrots into planners.

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Mechanically, agentech rag operates

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through multi-agent orchestration built on Azure AI agent service.

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Picture three roles in motion.

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First, the planner, a kind of digital project manager.

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The planner reads your query and decides which tools

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or data sources are relevant.

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Then come the retriever agents, domain experts,

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trained to access structure or unstructured data.

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Finally, the verifier or reason agent functioning

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as editor-in-chief checks consistency,

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validates citations and compiles the final response.

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Together, they run what we call an adaptive reasoning loop.

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Query, retrieve, validate, refine, and act.

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The crucial word is adaptive.

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Unlike standard rag, this loop doesn't terminate at the first output.

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It reroute when contradictions appear.

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Compare this orchestrated dance to a newsroom.

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The planner is the managing editor assigning beats.

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You check SharePoint for internal reports.

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You pull the sensor matrix from fabric.

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You scan the web for competitor data.

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Each retriever agent fetches its portion.

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The verifier fact checks the combined draft,

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reruns queries if citations conflict

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and only then publishes the summary.

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The result isn't a blob of text that merely sounds plausible.

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It's a coherent evidence linked inside.

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The leap here isn't bigger models.

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It's structured reasoning.

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Let's drill further into the planner's brain.

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It interprets your plain English question into a task map.

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Which retrievers to use, which order to run them in

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and how their findings should merge.

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This is where the Azure AI agent service earns its existence.

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It provides the orchestration layer that lets these agents communicate.

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Microservices that speak through APIs

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governed by Microsoft, Entra, Authentication, not guesswork.

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Now about security, because the average compliance officer

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is already reaching for the panic button.

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AgenteGrag doesn't cut corners.

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It's built around on behalf of authentication,

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meaning your identity travels with the request.

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The system doesn't impersonate you.

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It uses your verified token to fetch only what you have

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permission to see.

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Row-level security, RLS and column-level security,

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CLS come baked in.

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The AI can't accidentally reveal the CFO's forecast to an intern.

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Every retrieval call is logged, auditable and reversible.

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This matters because static rag has no concept of user context.

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It grabs whatever its search layer allows.

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Often bypassing the enterprise's permission scaffolding entirely.

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AgenteGrag restores that discipline.

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When the fabric retriever queries a lake house table,

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it enforces the same RLS rules your BI dashboards obey.

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When the SharePoint agent rummages through document libraries,

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it honors site-level permissions and Microsoft purview labels.

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So the same policies that protect your human users

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now protect your AI ones.

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Let's fold this back into workflow reality.

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Suppose the question is, which glucose range

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does product A underperform compared to product B?

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And what's the clinical impact?

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Standard rag will dump whatever snippets mentioned product A

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and glucose.

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AgenteGrag, powered by Azure AI agent service,

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would first have its planner identify three retrieval fronts.

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Fabric for sensor data, SharePoint for clinical notes

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and Bing for external publications.

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Each retriever agent brings in relevant evidence.

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The verifier compares trends across data sets,

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flags discrepancies, maybe even refines the original fabric query

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if an outlier appears.

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Only after validation does it synthesize the final insight

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with citations intact.

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That's iterative reasoning in action.

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Reactive rag stops after step one.

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AgenteGrag learns and adjusts mid-conversation.

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It can decompose follow-up questions automatically.

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Ask can we improve accuracy using recent studies

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and the same agents pivot to fetch emerging materials

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without losing context.

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Its continuous comprehension, not episodic memory loss.

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The compliance bonus is enormous.

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Every agent's action is traceable in audit logs,

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every token authenticated, every document touch logged.

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You get the illusion of omniscience

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with the paperwork of prudence, something auditors adore.

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So the philosophical shift is this.

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Retrieval alone provides information.

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Agency converts that information into process

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by introducing planning, specialization and verification

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as your agent service transforms random data

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pulls into accountable reasoning chains.

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In the enterprise world, that's the difference between

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an assistant you trust and one you quietly disable.

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Now that our co-pilot has a functioning brain,

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it's time to feed it a proper memory.

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In other words, let's give it somewhere substantial to look,

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starting with the unstructured chaos of SharePoint.

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Integrating SharePoint, turning chaos into knowledge.

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SharePoint is where corporate knowledge goes to hide.

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Every enterprise has one,

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an archaeological dig site of PowerPoints, meeting notes,

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outdated specifications and documents named final V12 really final.

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And to humans, it's chaos.

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To a naive rack system, it's unreadable chaos.

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Keyword search dutifully returns a thousand results,

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most of them irrelevant,

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and the average user scrolls until morale improves.

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Then they ask co-pilot for help and co-pilot promptly summarizes

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the wrong decade.

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AgenteGrag treats SharePoint differently,

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not as a library, but as a knowledge substrate.

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It knows that buried inside those folders

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are the qualitative insights

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that fabrics, structured tables will never capture.

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Context, decisions, rational.

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So instead of running a single keyword sweep,

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the SharePoint Retriever agent uses semantic embeddings

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00:10:45,480 --> 00:10:48,600
and vector search to map meaning, not just text.

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Ask about product reliability in humid environments,

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and it doesn't fixate on those exact words.

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It notices documents discussing failure modes,

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condensation resistance and adhesive performance.

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It recognizes intent.

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Here's where permission awareness becomes

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the make or break feature.

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Every SharePoint site has its own tangled web of permissions,

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teams, subsides, confidential folders.

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A dumb crawler ignores that and accidentally surfaces HR grievances

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in a marketing report.

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The agenteGmodel, however, authenticates on your behalf,

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inheriting your exact security context.

286
00:11:20,600 --> 00:11:22,600
It can only read what you're cleared to see.

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The output is trimmed by policy automatically,

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security trimming, courtesy of Microsoft Entra and PerView labels.

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00:11:28,000 --> 00:11:31,240
So the AI's intelligence never outpaces its authorization.

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Let's run a practical scenario.

291
00:11:32,800 --> 00:11:35,640
And R&D Manager types summarize performance differences

292
00:11:35,640 --> 00:11:37,840
of product A in coastal climates.

293
00:11:37,840 --> 00:11:40,200
The planner divides this into subquests.

294
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A fabric retriever prepares to analyze numeric sensor data,

295
00:11:43,160 --> 00:11:46,080
while the SharePoint agent dives into internal research papers,

296
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maintenance logs, and engineering notes

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00:11:48,040 --> 00:11:50,080
tagged with humidity-related terms.

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00:11:50,080 --> 00:11:52,440
It finds a 2023 field test report,

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00:11:52,440 --> 00:11:54,680
a discussion thread about material corrosion

300
00:11:54,680 --> 00:11:58,240
and a summarized findings document from the reliability team.

301
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Each document is vector scored for semantic relevance,

302
00:12:01,120 --> 00:12:03,760
retrieved, and passed up to the verifier agent

303
00:12:03,760 --> 00:12:06,600
that verifier cross-check those qualitative results

304
00:12:06,600 --> 00:12:08,040
against fabric numbers.

305
00:12:08,040 --> 00:12:10,600
If contradictions appear, say, an outdated document

306
00:12:10,600 --> 00:12:13,440
claims minimal corrosion, it flags the inconsistency

307
00:12:13,440 --> 00:12:15,360
and requests newer data.

308
00:12:15,360 --> 00:12:17,840
The final synthesized answer tells the R&D Manager,

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00:12:17,840 --> 00:12:20,520
precisely which assemblies degrade in high humidity

310
00:12:20,520 --> 00:12:22,080
and cites the validated sources.

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00:12:22,080 --> 00:12:24,440
What used to be several hours of manual digging

312
00:12:24,440 --> 00:12:27,120
now collapses into one continuous reasoning cycle.

313
00:12:27,120 --> 00:12:29,400
SharePoint shifts from a passive repository

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into an active participant in enterprise intelligence,

315
00:12:32,280 --> 00:12:35,120
essentially a neural memory of corporate decisions.

316
00:12:35,120 --> 00:12:37,640
The agentic rag layer doesn't simply read documents,

317
00:12:37,640 --> 00:12:40,080
it learns relationships, project lineage,

318
00:12:40,080 --> 00:12:42,400
authorship trends, contextual clusters,

319
00:12:42,400 --> 00:12:44,680
and can thread them into coherent arguments.

320
00:12:44,680 --> 00:12:46,400
Now compliance officers may still twitch

321
00:12:46,400 --> 00:12:48,160
when they hear autonomous retrieval.

322
00:12:48,160 --> 00:12:50,520
Relax, every query and document touch is logged,

323
00:12:50,520 --> 00:12:52,920
audit trails remain intact, regulatory frameworks,

324
00:12:52,920 --> 00:12:57,320
such as ISO 27001 and GDPR depend on accountability,

325
00:12:57,320 --> 00:12:59,680
and this system provides it automatically.

326
00:12:59,680 --> 00:13:01,400
The agent can even attach metadata

327
00:13:01,400 --> 00:13:03,600
citing the document path and access timestamps

328
00:13:03,600 --> 00:13:04,840
to every generated phrase.

329
00:13:04,840 --> 00:13:07,080
That means every claim the AI makes can be traced back

330
00:13:07,080 --> 00:13:10,680
to a specific version of a file, non-repudiation for robots.

331
00:13:10,680 --> 00:13:13,640
In short, integrating SharePoint under agentic rag

332
00:13:13,640 --> 00:13:16,520
turns content chaos into knowledge choreography.

333
00:13:16,520 --> 00:13:19,160
Your unstructured path becomes searchable by meaning,

334
00:13:19,160 --> 00:13:22,400
safeguarded by policy, and validated by cross-reference.

335
00:13:22,400 --> 00:13:24,520
SharePoint stops being a digital attic

336
00:13:24,520 --> 00:13:26,280
and becomes cognitive infrastructure,

337
00:13:26,280 --> 00:13:28,640
but qualitative context is only half the brain.

338
00:13:28,640 --> 00:13:30,880
The other half, the numeric structured truth

339
00:13:30,880 --> 00:13:33,880
lives within Microsoft fabric and that's where we go next.

340
00:13:33,880 --> 00:13:36,240
Microsoft fabric, the structured counterpart,

341
00:13:36,240 --> 00:13:38,880
welcome to the other hemisphere of your enterprise brain,

342
00:13:38,880 --> 00:13:40,280
the structured side.

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00:13:40,280 --> 00:13:42,560
If SharePoint stores your tribal knowledge,

344
00:13:42,560 --> 00:13:45,280
Microsoft fabric holds your empirical truth.

345
00:13:45,280 --> 00:13:47,000
It's the unified analytics backbone

346
00:13:47,000 --> 00:13:48,680
where numbers, models, and event streams

347
00:13:48,680 --> 00:13:51,400
converge into something approximating coherence.

348
00:13:51,400 --> 00:13:53,960
Most organizations treat it as a data warehouse.

349
00:13:53,960 --> 00:13:56,720
Under agentic rag, it becomes a reasoning substrate.

350
00:13:56,720 --> 00:13:58,760
Here's the role, fabric plays, precision.

351
00:13:58,760 --> 00:14:01,720
It doesn't speak anecdotes, it speaks telemetry, transactions,

352
00:14:01,720 --> 00:14:02,920
and time series.

353
00:14:02,920 --> 00:14:04,920
Within fabric live the lake house, the warehouse,

354
00:14:04,920 --> 00:14:08,040
and the semantic model, each a different dialect of structure.

355
00:14:08,040 --> 00:14:11,760
A fabric data agent built a top Azure AI agent service

356
00:14:11,760 --> 00:14:14,200
translates natural language into structured queries

357
00:14:14,200 --> 00:14:16,040
that traverse those layers securely.

358
00:14:16,040 --> 00:14:19,480
Ask, show quarterly yield variance for product A by region,

359
00:14:19,480 --> 00:14:22,440
and it quietly crafts and optimizes SQL-like query,

360
00:14:22,440 --> 00:14:24,440
targeting the right tables and partitions,

361
00:14:24,440 --> 00:14:26,760
executing against your authenticated context.

362
00:14:26,760 --> 00:14:30,040
No prompts, no power BID tours, direct governed intelligence.

363
00:14:30,040 --> 00:14:33,280
Now, raw access to numbers is meaningless without protection,

364
00:14:33,280 --> 00:14:36,320
so fabrication security isn't an option, it's architecture.

365
00:14:36,320 --> 00:14:38,680
Every interaction is encrypted and authenticated

366
00:14:38,680 --> 00:14:40,280
through Microsoft EntraID,

367
00:14:40,280 --> 00:14:41,720
ensuring that the agent operates

368
00:14:41,720 --> 00:14:43,800
under your verified user token.

369
00:14:43,800 --> 00:14:45,840
Remember the chaos of service principle shortcuts

370
00:14:45,840 --> 00:14:47,640
that ignore row-level security?

371
00:14:47,640 --> 00:14:48,960
Those are banned here.

372
00:14:48,960 --> 00:14:52,080
The on behalf of flow carries your identity end-to-end

373
00:14:52,080 --> 00:14:55,360
enforcing RLS and column-level security automatically.

374
00:14:55,360 --> 00:14:57,200
If finance marks a column confidential,

375
00:14:57,200 --> 00:14:58,520
the AI never glimpses it,

376
00:14:58,520 --> 00:15:01,520
and every execution leaves footprints in fabric audit logs,

377
00:15:01,520 --> 00:15:04,400
satisfying auditors who treat logs as bedtime stories.

378
00:15:04,400 --> 00:15:06,440
On top of that sits PerView governance,

379
00:15:06,440 --> 00:15:08,680
sensitivity labels travel with the data.

380
00:15:08,680 --> 00:15:11,760
Confidential, internal only, export restricted.

381
00:15:11,760 --> 00:15:14,800
When the fabric data agent composes a query using such fields,

382
00:15:14,800 --> 00:15:16,520
policies intercept instantly.

383
00:15:16,520 --> 00:15:18,400
Breach attempts trigger DLP enforcement

384
00:15:18,400 --> 00:15:19,880
before a single byte escapes.

385
00:15:19,880 --> 00:15:22,680
It's essentially enterprise-grade baby-proofing for AI.

386
00:15:22,680 --> 00:15:24,760
The model can explore but not electrocute itself,

387
00:15:24,760 --> 00:15:27,200
so what happens when we combine this structured discipline

388
00:15:27,200 --> 00:15:29,160
with the messy intuition of SharePoint?

389
00:15:29,160 --> 00:15:31,960
The Azure AI agent service orchestrates it,

390
00:15:31,960 --> 00:15:33,360
picture a court room.

391
00:15:33,360 --> 00:15:36,640
The fabric agent supplies the hard evidence, charts, counts,

392
00:15:36,640 --> 00:15:39,040
sensor averages, while the SharePoint agent

393
00:15:39,040 --> 00:15:40,960
delivers the witness testimonies.

394
00:15:40,960 --> 00:15:43,040
The verifier agent cross-examines them,

395
00:15:43,040 --> 00:15:44,920
ensuring the numbers and narratives aligned

396
00:15:44,920 --> 00:15:46,240
before presenting the verdict

397
00:15:46,240 --> 00:15:48,480
as a unified citation-rich answer.

398
00:15:48,480 --> 00:15:50,800
Let's use an example and operations lead asks,

399
00:15:50,800 --> 00:15:53,600
"Are we losing efficiency due to packaging defects?"

400
00:15:53,600 --> 00:15:55,320
The planner dispatches the fabric agent

401
00:15:55,320 --> 00:15:58,200
to retrieve yield rates by production line from the warehouse.

402
00:15:58,200 --> 00:16:00,160
Simultaneously it sends the SharePoint agent

403
00:16:00,160 --> 00:16:02,920
to scour maintenance logs and supply correspondence.

404
00:16:02,920 --> 00:16:04,560
The fabric agent returns a dataset

405
00:16:04,560 --> 00:16:07,520
showing minor dips correlating with humidity spikes.

406
00:16:07,520 --> 00:16:09,600
The SharePoint agent surfaces an email thread

407
00:16:09,600 --> 00:16:12,440
citing warped packaging materials during the same period.

408
00:16:12,440 --> 00:16:15,240
The verifier corroborates both, labels the correlation credible

409
00:16:15,240 --> 00:16:17,200
and the system drafts a concise finding

410
00:16:17,200 --> 00:16:20,080
complete with quantitative graphs and reference documents.

411
00:16:20,080 --> 00:16:23,400
Instant, six sigma diagnostics minus the sleepless analysts.

412
00:16:23,400 --> 00:16:27,200
Under the hood, the Azure AI agent service handles concurrency,

413
00:16:27,200 --> 00:16:29,080
batching retrievals through what's effectively

414
00:16:29,080 --> 00:16:30,360
a microservice mesh.

415
00:16:30,360 --> 00:16:33,480
Each agent publishes a schema describing what it can access,

416
00:16:33,480 --> 00:16:35,680
fabric schemers, SharePoint metadata,

417
00:16:35,680 --> 00:16:37,400
or Bing's open web context.

418
00:16:37,400 --> 00:16:38,800
The planner leverages that registry

419
00:16:38,800 --> 00:16:40,400
like an API directory.

420
00:16:40,400 --> 00:16:42,240
The result is modular intelligence.

421
00:16:42,240 --> 00:16:44,880
Swap or extend agents as new data domains emerge

422
00:16:44,880 --> 00:16:47,000
without rewriting the entire co-pilot,

423
00:16:47,000 --> 00:16:49,520
performance-wise, the innovation isn't bigger models,

424
00:16:49,520 --> 00:16:51,720
it's smarter retrieval order.

425
00:16:51,720 --> 00:16:53,400
The planner may query fabric first

426
00:16:53,400 --> 00:16:54,960
to define numerical boundaries,

427
00:16:54,960 --> 00:16:57,400
then use those to narrow SharePoint exploration

428
00:16:57,400 --> 00:16:59,440
that reduces noise and accelerates convergence.

429
00:16:59,440 --> 00:17:02,080
Think of it as data pruning through reason.

430
00:17:02,080 --> 00:17:04,240
Instead of drowning in 10,000 documents,

431
00:17:04,240 --> 00:17:05,760
the system knows which 10 matter

432
00:17:05,760 --> 00:17:08,080
because the numbers already framed the question.

433
00:17:08,080 --> 00:17:09,760
Compliance officers adore this setup

434
00:17:09,760 --> 00:17:12,040
because governance scales with intelligence.

435
00:17:12,040 --> 00:17:15,080
Every agent call is logged, every transformation traceable.

436
00:17:15,080 --> 00:17:17,840
When the CFO asks, where did this number come from?

437
00:17:17,840 --> 00:17:19,640
You can point directly to the fabric table,

438
00:17:19,640 --> 00:17:22,440
the timestamp and the executing user token.

439
00:17:22,440 --> 00:17:25,040
That transparency converts fear into trust.

440
00:17:25,040 --> 00:17:27,720
Critical currency in regulated industries like finance

441
00:17:27,720 --> 00:17:29,600
or health care, combine these properties

442
00:17:29,600 --> 00:17:31,560
and you've built an analytical symphony,

443
00:17:31,560 --> 00:17:33,480
structured precision from fabric harmonized

444
00:17:33,480 --> 00:17:35,720
with contextual understanding from SharePoint.

445
00:17:35,720 --> 00:17:37,920
The enterprise moves from search and hope

446
00:17:37,920 --> 00:17:39,920
to retrieve, verify, decide,

447
00:17:39,920 --> 00:17:41,320
and yes, the speed is startling now.

448
00:17:41,320 --> 00:17:43,560
Combine this multi-tier recall with reasoning

449
00:17:43,560 --> 00:17:45,560
and you get velocity, terrifying velocity,

450
00:17:45,560 --> 00:17:46,920
the research to decision cycle

451
00:17:46,920 --> 00:17:49,040
that once required a parade of analysts

452
00:17:49,040 --> 00:17:51,040
now collapses into hours,

453
00:17:51,040 --> 00:17:53,600
which leads us directly to the final movement.

454
00:17:53,600 --> 00:17:54,680
Impact.

455
00:17:54,680 --> 00:17:56,920
The enterprise impact from months to minutes.

456
00:17:56,920 --> 00:17:59,200
Let's talk consequences, the good kind.

457
00:17:59,200 --> 00:18:01,320
When you upgrade from passive rack to agentegrag

458
00:18:01,320 --> 00:18:02,800
across fabric and SharePoint,

459
00:18:02,800 --> 00:18:04,640
the average enterprise timeline bends,

460
00:18:04,640 --> 00:18:06,000
what took months of reporting turns

461
00:18:06,000 --> 00:18:08,120
into minutes of verified synthesis.

462
00:18:08,120 --> 00:18:10,200
The AI no longer waits for humans

463
00:18:10,200 --> 00:18:12,440
to translate questions into data queries.

464
00:18:12,440 --> 00:18:14,400
It performs that translation, validation,

465
00:18:14,400 --> 00:18:16,160
and summarization automatically.

466
00:18:16,160 --> 00:18:17,560
Consider R&D.

467
00:18:17,560 --> 00:18:19,640
Teams used to assemble cross-functional committees

468
00:18:19,640 --> 00:18:20,760
to align on data.

469
00:18:20,760 --> 00:18:22,600
Engineers exporting test metrics,

470
00:18:22,600 --> 00:18:25,400
analysts cleaning them, compliance checking permissions,

471
00:18:25,400 --> 00:18:28,640
and someone inevitably renaming files, final final.

472
00:18:28,640 --> 00:18:30,360
Agentegrag crashes that cycle.

473
00:18:30,360 --> 00:18:31,640
The planner orchestrates agents

474
00:18:31,640 --> 00:18:33,240
that retrieve fabric performance tables

475
00:18:33,240 --> 00:18:35,160
and SharePoint design notes simultaneously,

476
00:18:35,160 --> 00:18:37,760
merge them and draft a validated summary,

477
00:18:37,760 --> 00:18:40,520
complete with citations and security labels.

478
00:18:40,520 --> 00:18:42,880
Decision latency, nearly zero.

479
00:18:42,880 --> 00:18:45,680
Compliance and audit experience similar shockwaves.

480
00:18:45,680 --> 00:18:48,440
Traditional audits meant emailing evidence packs for weeks.

481
00:18:48,440 --> 00:18:50,640
Now, the same AI that wrote the report

482
00:18:50,640 --> 00:18:53,040
can regenerate its reasoning trail on demand.

483
00:18:53,040 --> 00:18:55,280
Every retrieval call, query string, file path,

484
00:18:55,280 --> 00:18:56,760
and timestamp is recorded,

485
00:18:56,760 --> 00:18:59,800
auditors can replay the exact steps leading to a conclusion,

486
00:18:59,800 --> 00:19:01,960
transforming due diligence from a scavenger hunt

487
00:19:01,960 --> 00:19:02,960
into a checkbox.

488
00:19:02,960 --> 00:19:04,440
What used to be a risk exposure

489
00:19:04,440 --> 00:19:06,200
becomes a governance jewel,

490
00:19:06,200 --> 00:19:08,320
manufacturing gains a predictive edge.

491
00:19:08,320 --> 00:19:11,520
Fabrics quantitative streams reveal process deviations.

492
00:19:11,520 --> 00:19:13,880
SharePoint's qualitative notes explain causes.

493
00:19:13,880 --> 00:19:16,880
The agente loop correlates them before alerts escalate,

494
00:19:16,880 --> 00:19:18,840
effectively performing continuous improvement

495
00:19:18,840 --> 00:19:20,280
without a six-sigma consultant.

496
00:19:20,280 --> 00:19:22,480
Imagine replacing a room of overworked interns

497
00:19:22,480 --> 00:19:24,040
with a panel of expert consultants

498
00:19:24,040 --> 00:19:25,920
who never sleep, never forget context,

499
00:19:25,920 --> 00:19:27,200
and never exceed budget.

500
00:19:27,200 --> 00:19:30,200
That's the operational equivalent of compounding intelligence.

501
00:19:30,200 --> 00:19:32,880
Of course, automation only matters if it's accountable.

502
00:19:32,880 --> 00:19:35,920
Agentegrag preserves every layer of enterprise inheritance,

503
00:19:35,920 --> 00:19:38,400
permissions, sensitivity labels, and audit logs,

504
00:19:38,400 --> 00:19:41,400
so CIOs can scale intelligence without scaling risk.

505
00:19:41,400 --> 00:19:43,520
Each insight is traceable to the user credential

506
00:19:43,520 --> 00:19:46,080
and governed repository that produced it.

507
00:19:46,080 --> 00:19:48,720
The system operates like a financial ledger for thought,

508
00:19:48,720 --> 00:19:51,880
transparent, reversible, and tamper evident.

509
00:19:51,880 --> 00:19:54,040
And the human cost, reduced boredom.

510
00:19:54,040 --> 00:19:56,440
Professionals stop copy-pasting from exports

511
00:19:56,440 --> 00:19:58,520
and start interpreting synthesized truths,

512
00:19:58,520 --> 00:20:00,920
meetings shorten because the AI arrives

513
00:20:00,920 --> 00:20:03,360
with prevalidated evidence projects accelerate

514
00:20:03,360 --> 00:20:05,760
because data and narrative converge instantly.

515
00:20:05,760 --> 00:20:08,040
This is the part most executives miss.

516
00:20:08,040 --> 00:20:10,640
Moving fast isn't reckless when the reasoning is documented.

517
00:20:10,640 --> 00:20:13,320
The real recklessness is still building dumb co-pilots,

518
00:20:13,320 --> 00:20:15,080
single-shot context blind parrots,

519
00:20:15,080 --> 00:20:16,520
masquerading as strategists.

520
00:20:16,520 --> 00:20:19,120
Those belong in the museum of early AI curiosities

521
00:20:19,120 --> 00:20:20,080
next to Clippy.

522
00:20:20,080 --> 00:20:22,320
So if your enterprise still celebrates a co-pilot

523
00:20:22,320 --> 00:20:24,040
that only retrieves, congratulations,

524
00:20:24,040 --> 00:20:26,040
you're funding a fancy auto-complete.

525
00:20:26,040 --> 00:20:28,160
The serious players are already using agents

526
00:20:28,160 --> 00:20:29,800
that plan, verify, and act.

527
00:20:29,800 --> 00:20:31,240
The regression from months to minutes

528
00:20:31,240 --> 00:20:32,600
is just the surface benefit.

529
00:20:32,600 --> 00:20:34,360
The deeper one is epistemic integrity,

530
00:20:34,360 --> 00:20:36,320
the decisions that actually reflect reality

531
00:20:36,320 --> 00:20:37,800
because the intelligence constructing

532
00:20:37,800 --> 00:20:38,880
them's held accountable.

533
00:20:38,880 --> 00:20:40,800
This is why continuing to build dumb co-pilots

534
00:20:40,800 --> 00:20:43,520
isn't merely inefficient, it's reckless.

535
00:20:43,520 --> 00:20:45,960
The future of enterprise AI isn't bigger prompts.

536
00:20:45,960 --> 00:20:48,160
It's smaller feedback loops with better memory

537
00:20:48,160 --> 00:20:48,920
and governance.

538
00:20:48,920 --> 00:20:51,120
Agentech Ragh delivers both.

539
00:20:51,120 --> 00:20:54,800
Intelligence finally behaves like a system, not a stunt.

540
00:20:54,800 --> 00:20:56,240
Stop building, start thinking.

541
00:20:56,240 --> 00:20:57,960
Ragh without agency is obsolete.

542
00:20:57,960 --> 00:21:00,040
It's yesterday's architecture pretending to handle

543
00:21:00,040 --> 00:21:01,280
tomorrow's problems.

544
00:21:01,280 --> 00:21:03,200
The modern enterprise doesn't need chatbots.

545
00:21:03,200 --> 00:21:04,880
It needs cognitive infrastructure,

546
00:21:04,880 --> 00:21:08,520
systems that can plan, verify, and act under your identity,

547
00:21:08,520 --> 00:21:09,640
not beside it.

548
00:21:09,640 --> 00:21:12,560
That's Agentech Ragh, an ecosystem of deliberate reasoning

549
00:21:12,560 --> 00:21:14,480
built on Azure AI agent service,

550
00:21:14,480 --> 00:21:17,160
speaking securely to SharePoint and Microsoft Fabric

551
00:21:17,160 --> 00:21:20,200
like a team of experts sharing one authenticated brain.

552
00:21:20,200 --> 00:21:22,960
If your co-pilot still can't schedule its own retrievals,

553
00:21:22,960 --> 00:21:25,480
validate references or explain its reasoning trail,

554
00:21:25,480 --> 00:21:27,760
stop calling it intelligent, it's decorative.

555
00:21:27,760 --> 00:21:31,320
Intelligence implies intent, verification, and consequence.

556
00:21:31,320 --> 00:21:33,480
Decorative AI performs monologues,

557
00:21:33,480 --> 00:21:35,760
agentech AI conducts experiments.

558
00:21:35,760 --> 00:21:37,400
The difference is accountability.

559
00:21:37,400 --> 00:21:40,200
One creates output, the other creates understanding.

560
00:21:40,200 --> 00:21:42,920
This architectural shift isn't optional anymore.

561
00:21:42,920 --> 00:21:44,840
The moment your decision spans structured

562
00:21:44,840 --> 00:21:47,960
and unstructured data, static Ragh collapses.

563
00:21:47,960 --> 00:21:49,400
Compliance officers already know it,

564
00:21:49,400 --> 00:21:52,080
analysts already feel it, and leadership will soon demand

565
00:21:52,080 --> 00:21:54,200
proof of reasoning, not just confidence of tone.

566
00:21:54,200 --> 00:21:57,000
Microsoft Stack finally provides the scaffolding.

567
00:21:57,000 --> 00:21:59,320
Azure AI agent service for orchestration,

568
00:21:59,320 --> 00:22:01,280
fabric data agents for quantitative truth,

569
00:22:01,280 --> 00:22:03,160
SharePoint retrievers for context,

570
00:22:03,160 --> 00:22:05,720
and purview governance sealing the whole nervous system.

571
00:22:05,720 --> 00:22:09,440
So here's your challenge, stop deploying show ponies.

572
00:22:09,440 --> 00:22:11,200
Build agents that argue with themselves

573
00:22:11,200 --> 00:22:12,680
until they agree on truth.

574
00:22:12,680 --> 00:22:14,600
Experiment with multi-agent planning.

575
00:22:14,600 --> 00:22:16,320
Test on behalf of authentication,

576
00:22:16,320 --> 00:22:17,840
wire fabric and SharePoint together

577
00:22:17,840 --> 00:22:20,000
until your AI can actually defend its answers.

578
00:22:20,000 --> 00:22:21,320
Because intelligence without action

579
00:22:21,320 --> 00:22:22,480
isn't intelligence at all.

580
00:22:22,480 --> 00:22:25,080
If this explanation clarified more than your vendor ever did,

581
00:22:25,080 --> 00:22:25,840
subscribe.

582
00:22:25,840 --> 00:22:28,560
The next deep dive deconstructs multi-agent design

583
00:22:28,560 --> 00:22:31,120
inside Microsoft 365 AI,

584
00:22:31,120 --> 00:22:33,680
how to make your co-pilot not just attentive,

585
00:22:33,680 --> 00:22:36,000
but sentient within policy.

586
00:22:36,000 --> 00:22:38,720
Efficiency isn't magic, it's architecture.

587
00:22:38,720 --> 00:22:40,560
Lock in your upgrade path, subscribe,

588
00:22:40,560 --> 00:22:43,760
enable alerts, and let new insights deploy automatically.