Stop Building Dumb Copilots: Why Context Engineering Is Your Only Fix

In this episode, we break down the real reason most Copilots fail inside the Power Platform: context debt. Your model isn’t hallucinating because it’s dumb—it’s guessing because you starved it. We walk through a complete, repeatable context engineering blueprint for Copilot Studio and Power Automate, designed to eliminate hallucinations, reduce cross-tenant drift, and dramatically cut latency and cost. You’ll learn how to build the four-layer spine every enterprise Copilot needs: System Context, Retrieval, Tools, and Policies—plus the missing governance layer most teams overlook. What You’ll Learn 1. Why Your Copilot Fails (Context Debt)

• How missing system rules and vague identity cause inconsistent answers
• Why ungrounded Dataverse data leads to hallucinated fields
• The hidden impact of undefined tools and cross-environment drift
• How governance gaps create policy violations and compliance risks

2. Layer 1 — System Context That Doesn’t Drift

• The System Message pattern used in real enterprise deployments
• Identity, scope, refusal policy, schema awareness, and logging rules
• How to parameterize system context across Dev/UAT/Prod
• The “six-line” system message formula that stops ambiguity cold

3. Layer 2 — Retrieval That Grounds to Dataverse

• How to build a Dataverse-first schema index
• Why PDFs and document libraries aren’t grounding—and how to fix them
• Chunking, security trimming, hybrid search, and caching for speed
• The schema grounding checklist every agent needs

4. Layer 3 — Tooling + Policy Enforcement

• Turning Power Automate flows into safe, least-privilege “agent verbs”
• How to encode preconditions, sensitivity flags, and refusal logic
• Using DLP, Conditional Access, Purview, and MIP labels to prevent drift
• Why you need an admin kill-switch (and how to add one)

5. End-to-End Build (With Before/After Metrics)

• Step-by-step Copilot Studio + Power Automate build
• Schema indexing, tool catalog, prompt wrappers, and environment bindings
• Before/after metrics: latency, token usage, hallucinations, policy adherence
• Real example: correcting an invalid “fast-track to approved” request

Key Takeaways

• Models don’t provide truth—they only predict text. You provide the truth.
• The four layers—System, Retrieval, Tools, Policies—eliminate drift and hallucination.
• Dataverse schema is the spine; documents are secondary.
• Governance isn’t optional: DLP, Conditional Access, and sensitivity labels define reality.
• A fully engineered context cuts latency, costs, hallucinations, and audit risk.
  
  
  

Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support.

Follow us on:
LInkedIn
Substack