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The Agent Development Lifecycle 101 by Harrison Chase

LangChain22 July 2026Watch on YouTube

Part of series

Ep. 11 · LangChain Deep Agents

Stap-voor-stap uitleg en tutorials over het bouwen, configureren en deployen van Deep Agents met het LangChain-framework.

View the series

What you'll learn

  • The Agent Development Lifecycle is a framework for systematically building, testing, deploying, and improving AI agents in production, not as one-off demos.
  • Traces, evaluations, experiments, and monitoring form the core of continuously improving agents in practice.
  • Teams can analyze production behavior to build stronger evaluations and identify recurring failure patterns.
  • Long-horizon agents require durable execution, context management, integrated tools, and human-in-the-loop workflows.
  • Shifting from 'build an agent' to a continuous improvement loop reduces guesswork and improves reliability.

Frequently asked questions

What is the difference between traditionally building agents and the Agent Development Lifecycle?
The traditional approach treats agents as one-off demos or isolated projects, while the Agent Development Lifecycle is a repeatable system where teams continuously build, test, deploy, monitor, and improve agents over time.
How do traces and evaluations help teams improve agents?
Traces capture production behavior, allowing teams to identify recurring failure patterns and diagnose root causes. This behavior is then used to build stronger evaluations, ensuring future improvements target larger issues.
What components are needed to run long-horizon agents in production?
Long-horizon agents require durable execution for extended tasks, context management to maintain information, integrated tools for external interactions, sandboxes for safety, and human-in-the-loop workflows for human oversight.
How can teams catch failures earlier and prioritize fixes?
By analyzing production traces, teams can identify recurring failure patterns and diagnose root causes, which helps them prioritize fixes and strengthen evaluation coverage over time.

Topics

ai agentsproduction deploymentevaluation & testinglangchainmlops

Description from the channel

How teams build, test, deploy, and improve production agents Everyone wants to ship agents, but the best teams are no longer treating agents as one-off demos or isolated projects. They are building a repeatable Agent Development Lifecycle: a system for building, testing, deploying, monitoring, and continuously improving agents over time. Join Harrison Chase, CEO and co-founder for LangChain for a walkthrough of the Agent Development Lifecycle and why it matters for teams moving agents into production. We’ll cover how leading teams are shifting from “build an agent” to an ongoing improvement loop powered by traces, evals, experiments, feedback, and production monitoring. You’ll learn how teams can catch failures earlier, turn production behavior into stronger evals, and improve agents without relying on guesswork. We’ll also show how teams identify recurring failure patterns from production traces, diagnose root causes, prioritize fixes, and strengthen eval coverage over time. Finally, we’ll touch on what it takes to run long-horizon agents with durable execution, context, tools, sandboxes, and human-in-the-loop workflows.