context-engineering

To maintain consistent AI agent behavior across sessions, provide structured, persistent context like project-wide rules files. This ensures the right information is available at the start of every session, improving output quality and reducing hallucinations.

Agent context engineering is the practice of providing structured, persistent context to AI agents to improve output quality. Apply it when starting new sessions, switching between tasks or projects, or when output quality degrades during an active session.

Structure AI prompting context using a hierarchy: load project rules files first, followed by architecture specs, relevant source files, error output, and conversation history. Selective context loading balances persistence and relevance to reduce hallucinations.

Set up AI agent context management by creating a persistent rules file, such as CLAUDE.md, that defines project conventions. Loading this file initializes a consistent agent context for reliable outputs across your project.

AI output quality degrades during long conversations when relevant context is lost or overwhelmed. You can restore quality by selectively reloading architecture specs, rules files, and source context to rebalance persistence and relevance.