Agent Frameworks
Explore how to implement and manage agent frameworks that safely control tool usage in AI applications. Understand the importance of explicit stopping rules, traceability, and stateful orchestration using loops, graphs, and popular frameworks like LangChain and LangGraph. Learn to select the right architecture to balance control, visibility, and operational efficiency for producing reliable, debuggable AI agents.
A production Research Assistant is easy to describe but harder to control.
It should retrieve relevant sources, synthesize an answer, and cite its evidence within a bounded workflow. It also needs explicit stopping rules rather than relying on an answer that merely sounds complete. Make those rules testable: cap the number of steps, stop when verification passes or further retrieval adds no useful sources, record tool calls and their inputs, and route tools through defined conditions so a prompt change does not silently alter execution.
Rule: If we cannot name the stop condition and the trace evidence we will inspect, do not pick a framework yet. ...