Context Engineering for Enterprise AI

Project overview

Context engineering for enterprise agents that can cite sources, operate within boundaries and escalate uncertainty.

Problem

Prompt-only approaches do not provide the context quality, tool boundaries or verification required for dependable enterprise action.

Constraints

• Uncertain model output
• Changing source knowledge
• Permission boundaries
• Need for evidence and review

Architecture or approach

Curated context, retrieval contracts, constrained tools, validation checkpoints, provenance and explicit human escalation.

Key engineering decisions

• Treat context as architecture rather than prompt decoration
• Separate recommendation from execution
• Require evidence for consequential outputs

Trade-offs

More controls reduce apparent autonomy while increasing reliability and organizational trust.

Outcome or current status

Technical research and conference body of work connected to a devm.io article and ML Con sessions; publication/event links remain pending verification.

Lessons learned

A reliable agent is a governed system, not an unconstrained model loop.

What I would improve next

Publish verified materials and expand scenario-based evaluation.