Agents on longer tasks need more than one loop

Dear John,

An agent that calls tools and decides its next step can complete a task. Making that agent reliable over longer, more complex work takes more: loops that check its output, start a new run each time an event occurs, and help the agent improve from past runs.

In "Loop Engineering with LangChain Deep Agents," you'll learn a framework for four loops around an agent and how to build each one. This course is built in collaboration with LangChain and taught by Sydney Runkle, Product Manager and maintainer of Deep Agents at LangChain, and Esmaeil Gargari, Product Lead at DeepLearning.AI.

In this course, you'll learn how to:

• Build an agent loop that calls tools, observes results, and decides what to do next

• Add a verification loop that checks the agent’s output and sends feedback for another attempt

• Set up an event-driven loop so external events trigger new runs automatically

• Use a hill-climbing loop that analyzes traces from many runs to improve the agent’s harness

Keep learning,

The DeepLearning.AI team

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