reflection-injection

To stop an AI agent from repeating past errors, you can inject relevant past reflections into its context before each new iteration. This provides persistent, context-aware learning by filtering previous failures based on task similarity and error types.

Contextual relevance filtering for agent memory injection works by matching past reflections against the current context using task type, error type, and file or module similarity. This ensures only pertinent lessons learned are loaded into the agent context.

The best way to implement self-correction in agentic loops is to use episodic memory injection. By loading and summarizing past experiences when retrying after errors, the agent receives the context needed to avoid previous mistakes and adjust its approach.

No specific framework is required to enable agent learning from previous iterations. The mechanism works by injecting filtered past reflections directly into the agent context, making it compatible with any agentic loop requiring memory and self-correction capabilities.

You should use episodic memory injection in your agent workflow specifically when starting new iterations or retrying after errors. It satisfies the need for persistent learning by summarizing past experiences to prevent the agent from making the same mistake again.