context-engineering

To reduce AI agent hallucinations, curate context hierarchically by loading project rules, specifications, source files, and error outputs in order of relevance so the agent receives focused, accurate information.

Hierarchical context loading is a strategy that orders context ingestion by persistence and relevance—starting with rules files, then specs, source files, errors, and conversation history—to keep agents focused and produce reproducible behavior.

Set up a persistent rules file by defining project commands, conventions, and boundaries in a file like CLAUDE.md that is always loaded at the start of a coding session to enforce consistent agent behavior.

AI agent output quality declines in long conversations due to context confusion and bloated history. Manage this by surfacing ambiguities, summarizing long sessions, and compacting conversation history to prevent incorrect assumptions.

Yes, you can use selective context packing strategies during task switches to load only the relevant spec sections, source files, and test outputs needed for the new task, preventing wasted iterations and maintaining accuracy.

The best way to onboard an AI agent to fix a failing test is to load the project rules, the relevant specification excerpt, the related source and test files, and the failing test output to reproduce and resolve the issue accurately.