recall-context

To prevent AI agents from repeating past mistakes, you need to surface relevant historical context before starting a task. Fetching prior failure events and auto-promoted prevention rules provides corrective guidance to block risky actions early in the workflow.

You can load prior session context into an agent workflow by using a recall tool to fetch bundled context packs. This mechanism retrieves reusable project assumptions and historical constraints to reduce missing information and rework.

Prevention rules in agent coding tasks are auto-promoted guidelines tied to historical failure events. They provide corrective guidance for the current task, ensuring risky actions are blocked early before code execution begins.

Yes, you can retrieve past failure history before modifying previously failing code. Using a recall tool returns specific failure tags and the corrective actions that fixed the issue last time, ensuring safe modifications.

Recall context works for resuming work across sessions by loading bundled project context from prior interactions. It returns structured context packs containing known constraints and lessons learned to reduce missing assumptions.

You should use a recall tool for coding tasks when starting new features, touching code that has failed before, or resuming work with known constraints. It fetches prevention rules and past failures to provide corrective guidance before execution.