Peter 
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 practical multi-model coding pattern: split one implementation mission into Builder, Investigator, Auditor, and Verifier roles, then spend expensive reasoning only where it adds value.
JEV and GPT-6 Luna produced the same final classification across 17 Email-to-Case samples. The real difference appeared in cost, evaluation, Question Contracts, Evidence Contracts, and where business authority remained.
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.
MCP does not require a rebuild of stable REST APIs. The architectural choice is how to expose each capability to agents through OpenAPI, vendor hosted MCP, or a shared custom MCP surface.
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.