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 aggregate metric hides a coverage problem. It then introduces the Agent Development Lifecycle (Agent DLC): one lifecycle, two loops, and a single explicit standard for judging every change.

A note on terminology: Throughout this post, gold standard means one specific artifact: the bundle of verifiable criteria, their thresholds, and the approved examples they are checked against. Business owners approve this standard as the basis for acceptance decisions.

Conventional software testing and CI/CD still apply to agents, but three properties require additional checks. First, agents are non-deterministic: identical inputs can produce different outputs and execution paths. Second, changes to prompts, tool descriptions, retrieval settings, or memory configuration can alter behavior in ways that a text review alone can’t reveal.

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