Definition
A repeatable operating cycle for taking an agent from an initial design into production, then using real behavior to improve it without losing control of changes. It covers the model and the parts around it: prompts, tools, context, runtime, permissions, evaluation data, deployment, tracing, feedback, and policy.
The cycle matters because an agent can return a technically successful response and still fail the task. Pre-deployment evaluation tests known cases. Production monitoring reveals tool mistakes, policy violations, unexpected trajectories, and failures the original test set missed. Those traces become new evaluation cases before the next prompt, model, tool, or harness version is promoted.
Where the framework came from
Harrison Chase gave the label a concrete form in LangChain's May 2026 essay, The Agent Development Lifecycle. Its central loop is build, test, deploy, monitor. The essay treats iteration as the return path through that loop and governance as a concern around the whole system, including cost, tool access, auditability, and human approval. This is a vendor-authored operating model, not a formal standard, but it names work that one-off agent demos usually leave implicit.
Distinguish it from nearby terms
The software development lifecycle covers software delivery generally. The agent development lifecycle adds behavior that cannot be understood from application code alone: nondeterministic runs, model and prompt changes, tool authority, context assembly, trajectory evaluation, and production feedback.
It is also broader than an agent improvement loop. Improvement is one path through the lifecycle. Deployment, incident response, access control, retirement, and accountability remain lifecycle concerns even when no optimization is underway.
Check your understanding
Your agent passes a launch evaluation, then production traces show that it chooses the wrong account when two customers have similar names. Which lifecycle step is missing? The useful answer is not simply "better prompting." Capture the failure, add it to a regression set, change the system, evaluate the candidate, and deploy it through the same controlled path.