What is context engineering for AI agents?
Context engineering is the work of selecting and organizing the information an AI system receives for a task. It includes instructions, retrieved evidence, tool descriptions, and relevant state. The aim is useful context within constraints, rather than the largest possible prompt or memory store.
What does the source say?
Anthropic describes context engineering as a development of prompt engineering and discusses selection, compaction, and memory strategies. These are implementation techniques; their value should be tested against the workflow you need to support.
How can you apply this?
Pick a failed answer and inspect the information available at the decision point. Mark what was missing, irrelevant, contradictory, or stale. Create a smaller context package that repairs one problem. Replay the same task and a contrasting example to check whether the change helps generally or only fits the original failure. Save the before-and-after evidence packages with their outcomes so another person can reproduce and challenge the comparison.
What should you watch out for?
A longer context window does not guarantee attention to the right evidence. Keep provenance and access controls attached to retrieved information.