The Production AI Playbook: Deploying Agents at Enterprise Scale
The gap between impressive agent demos and operated agent systems is where most of the real work lives.
Signal summary
This signal is about the production layer: evaluation, observability, data quality, orchestration, ownership, fallback paths, deployment practices, and governance. It is useful because it moves the conversation away from agent excitement and toward the operational conditions under which agents can be trusted with real work.
Why it matters
Most organisations do not fail at AI because nobody can make a demo. They fail because the demo is not connected to a maintainable system. Production agents need clear owners, traceable actions, testable behaviours, known failure modes, measurable outcomes, and escalation routes.
LGI reading
This is exactly the terrain LGI should occupy commercially. The offer is not "we add AI." The offer is "we help you build the operating layer around AI so it can survive contact with real work." That includes knowledge architecture, source grounding, workflow design, review systems, and governance surfaces.
Use this if you are thinking about
enterprise AI adoption, agent deployment, observability, evaluation, operational risk, and turning AI experiments into durable systems.
Related LGI concept
AI Operating Models
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