Learn to build better AI Agents through better context

Learn More: bit.ly/4yy5iWP The longer an agent works, the more repeated, contradictory, or stale information builds up in its context. Models perform worse as their context grows, a problem Jeff Huber and his team at Chroma named Context Rot. In our new course, Context Engineering, taught by Jeff Huber alongside guest speaker Drew Breunig, you'll build agent harnesses from scratch in Python and learn to give agents the right information, in the right format, at the right time. In this course, you'll learn: - Agentic search: Retrieve data with multiple search tools from different sources. Use traces to improve tool selection through evals. - Context management: Use pruning, compaction, and scratchpads to format the information an agent needs to complete a task. - Agentic memory: Store context from user feedback as reusable memories, retrieve it when a task needs it, and update records across sessions as information changes. Enroll for free: bit.ly/4yy5iWP