Most people think AI agents fail because of weak prompts. Not true. Prompts guide reasoning—but executors, validation, and workflow graphs are what guarantee reliability. In this episode, we reveal the architecture behind stable, predictable, enterprise-ready AI agents using Microsoft 365 Graph, Azure OpenAI, and Copilot Studio. You’ll learn why traditional prompt-only agents hallucinate tools, break policies, and silently fail—and how a contract-first, validator-enforced architecture fixes accuracy, latency, cost, and auditability. This is the mental model and blueprint every AI builder should have started with. What You’ll Learn 1. Why Prompts Fail at Real-World OperationsThe difference between cognition (LLMs) and operations (executors)Why models hallucinate tools and ignore preconditionsHow executors enforce idempotency, postconditions, and error recoveryThe “silent partial” problem that breaks enterprise workflows2. Workflow Graphs: The Map AI Agents Actually NeedNodes, edges, state, and explicit control flowWhy DAGs (directed acyclic graphs) dominate reliable workflowsState isolation: persistent vs ephemeral vs derivedCompensations and rollback logic for real-world side effectsMemory boundaries to prevent cross-session leakage3. Secure-by-Design: Validation That Stops ChaosStatic graph validation: cycles, unreachable nodes, contract checksRuntime policy checks: RBAC, ABAC, allowlists, token scopesInput/output sanitization to prevent prompt injectionSandboxing, segmentation, and safe egress controlsImmutable logging and node-level tracing for auditability4. Microsoft Integration: M365 Graph + Azure OpenAI + Copilot StudioLeast-privilege Graph access with selective fields and delta queriesChunking, provenance, and citation enforcementAzure OpenAI as a reasoning layer with schema-bound outputsCopilot Studio for orchestration, human checkpoints, and approvalsReliable execution using idempotency keys, retries, and validation gates5. Before/After Metrics: The ProofHigher factual accuracy due to citation-verified groundingLower p95 latency via parallel nodes + early exitReduced token cost from selective context and structured plansDramatic drop in admin overhead through traceability and observabilityStable first-pass completion rates with fewer human rescues6. The One Gate That Prevents Dumb Agent MistakesThe pre-execution contract check:Capability matchPolicy compliancePostcondition feasibilityDeny-with-reason paths that provide safe alternativesPreventing privilege escalation, data leaks, and invalid actionsKey TakeawaysPrompts are thoughts. Executors are actions. Validation is safety.Reliable AI agents require architecture—not vibes.Graph validation, policy enforcement, and idempotent execution turn “smart” into safe + correct.Grounding with Microsoft Graph and Azure OpenAI citations ensures accuracy you can audit.A single contract gate prevents 90% of catastrophic agent failures.Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support.Follow us on:LInkedInSubstack

Ver todos los episodios