Agent Frameworks
Explore how to build and manage AI agents using different frameworks to ensure safe tool usage and effective memory management. Understand production constraints like explicit stop conditions, traceability, and control flow. Learn to choose between custom loops, stateful graphs, and standard frameworks to maintain operational visibility and debugging capabilities. This lesson helps you design bounded, inspectable agent architectures suitable for production environments.
A production Research Assistant is easy to describe and hard to constrain. It should run a bounded sequence like retrieve relevant sources, synthesize an answer, and cite what it used, but it also needs to stop on purpose, not just when the output looks finished.
Those requirements become an envelope we can test. The agent should cap steps, keep an explicit stop condition such as no new sources found or verification passed, record which tools ran with what inputs, and route tools predictably so a prompt tweak does not silently change behavior.
Rule
If we cannot name the stop condition and the trace evidence we will inspect, do not pick a framework yet. ...