A practical look at Microsoft’s Skill-induced failure findings and how to evaluate SME knowledge, evidence authority, scope, and cost in enterprise agents.
What Agent Skills and SKILL.md do, how they differ from MCP, and how enterprise domain experts can turn practical review know-how into reusable guidance without replacing runtime controls.
Lessons from RAG, voice-agent, and automation projects show that enterprise AI succeeds less by choosing the newest technology than by defining Value, Decision, Authority, Execution, and Adoption boundaries that survive production.
Instead of expanding RFP keywords into broad searches, combine Deal Signals with a Canonical Market Lens, define the evidence required, and use Web Search to validate strategy-relevant deltas.
Instead of re-injecting market reports, competitor research, and company capabilities into every prompt, maintain them in an LLM Wiki and compile a reusable Canonical Market Lens for enterprise Web Search.
Enterprise RAG needs a business-valid retrieval scope before semantic ranking begins. This article explains how Deal Context and a Retrieval Scope Contract can constrain the candidate corpus before embeddings and rerankers do their work.
A confidence score makes an automation rule look easy. My JEV email classification PoC showed why the score must first be tested against outcomes and weighed against the consequences of a wrong decision.
A manager’s PowerPoint template onboarding job reveals why an Agent Run cannot own the identity, state, and recovery of long running Enterprise AI work.
I built a JEV and n8n email classification PoC expecting to focus on model performance. The harder work was defining the active request, writing category boundaries, and deciding when a model judgment becomes a business decision.
OpenAI’s managed Codex harness can take over much of the work of running an agent. The harder question is which responsibilities still belong to the application, and how to make the runtime replaceable.