Governing AI Agents Without Killing Them
What governance looks like in code when the system has real tools and real consequences.
Production AI Engineering
Most teams do not have an AI problem. They have a production problem.
Production AI engineering is the discipline of turning AI features, agent workflows, and LLM systems into software that can survive real users, real token budgets, and real operational constraints.
It is not just prompt design. It is architecture, evals, observability, governance, rollout patterns, and the codebase discipline required to ship AI without accumulating silent failure.
This is the category I care about most because it sits at the boundary between what demos well and what actually holds up.
What Production AI Engineering Covers
Architecture
Choosing the right shape for a system before you add complexity you can't support: structured outputs, RAG, agents, tool use, memory, orchestration.
Evaluation
Defining what good output looks like, how to test it, and how to detect regression before users do.
Observability
Tracing, decision logs, cost visibility, and enough instrumentation to debug behavior instead of guessing.
Governance
Tool boundaries, approval flows, auditability, and the controls that matter when AI systems can act.
Team Workflow
Claude Code adoption, conventions, documentation, and the codebase changes that make AI tools useful across a team.
What Breaks Between Demo And Production
The feature works in staging but falls apart under real load
Latency, token budgets, malformed inputs, and edge cases do not show up in the happy-path demo.
No one knows if the system is getting worse
Without evals and observability, regressions arrive quietly and the team argues from anecdotes.
The model is blamed for a codebase or workflow problem
A surprising amount of AI pain is actually missing conventions, weak feedback loops, and unclear boundaries.
Agents have too much power and too little structure
Tool sprawl, weak approval paths, and vague instructions create blast radius faster than most teams expect.
Best Starting Points
Production AI Retainer
For teams that need architecture judgment and hands-on implementation inside the real codebase.
See how I workCodebase Readiness Assessment
A free assessment for teams who want to understand whether their repository supports AI-assisted development.
Run the assessmentAI Agent Architecture
For the agent-specific side of memory, tool orchestration, governance, and production patterns.
Go deeper on agentsReading On The Topic
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The Observability Layer Your AI Agent Is Missing
A video walkthrough of what teams usually skip until they need to debug behavior in production.
Common Questions
What is production AI engineering?
Production AI engineering is the work required to ship AI systems that survive the real world: architecture, evals, observability, governance, rollout patterns, and the workflow discipline around them.
How is production AI engineering different from AI consulting?
Generic AI consulting usually stays at the strategy or tool-selection layer. Production AI engineering is about implementation judgment under real constraints: what breaks, how to measure it, and how to ship something your team can actually operate.
Who needs this?
Teams that are already building or shipping AI features, not just exploring possibilities. It is especially useful when the demo worked and now the constraints are getting real.
Where should a team start?
Start with the highest-leverage bottleneck. Sometimes that is evals. Sometimes it is observability. Sometimes it is the codebase itself. That is why I use a Foundation Sprint model instead of guessing from a sales deck.
Need a senior technical voice on production AI?
I work with engineering teams on retainer to ship reliable AI features, harden the AI stack, and make agentic systems that survive past the demo.
Get practical production AI writing
Architecture, evals, observability, Claude Code workflows, and the tradeoffs that only show up once systems leave the demo.
Occasional emails. No fluff.