From prototype to production: Engineering agents with Agent DLC

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Publicado: 12 nov 2026, 15:38 UTC
Recolectado: 12 nov 2026, 15:41 UTC

Agents stall between demo and release when teams lack a repeatable way to measure how good an agent is and whether the last change helped. This post covers three agent properties that break traditional CI/CD, a measured case where an aggregate metric hides a coverage gap, and Agent DLC: one lifecycle, two loops, and one explicit standard for judging every…

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Over the past year, AI agents have moved from exploration into engineering. Enterprise teams are shipping large language model (LLM) systems that call tools, orchestrate APIs, and answer questions over internal knowledge. A successful demo can show what an agent is capable of. Before release, the team needs to answer a further question: “ Is this agent ready for production? ” The person responsible for that decision needs evidence that the agent meets the agreed requirements. To make that…

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