The Agent Development Lifecycle: Build, Test, Deploy & Monitor AI Agents | LangChain
The Agent Development Lifecycle (ADLC) outlines a systematic process for building, testing, deploying, and monitoring agents, emphasizing iterative development, real-world testing, and continuous improvement. It involves selecting appropriate tools, such as code-first or no-code options, to structure agents, whether through simple tool-calling loops or complex interactions involving prompts, skills, and context management. Testing includes creating datasets, metrics, and experiments to evaluate performance, while deployment requires a runtime environment that supports durable execution and human-in-the-loop patterns. Monitoring tracks metrics and traces to assess agent behavior and inform future iterations.
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