From prototype to production: Engineering agents with Agent DLC | Amazon Web Services
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 decision consistently, teams need a repeatable, affordable way to assess the agent’s current behavior and whether the latest change has improved it.
In traditional software development, teams answer these questions with unit tests, continuous integration and continuous delivery (CI/CD), and passing builds. Those mechanisms are necessary but not sufficient for agents, for three reasons, each requiring additional checks. This post explains these three properties and presents a measured case where an...
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