ReasoningBank Intelligence

Adaptive learning for AI agents is enabled through ReasoningBank Intelligence, which records task outcomes and automatically identifies optimal strategies. Initialize ReasoningBank with AgentDB persistence, then log experiences with task type, approach, and results; the system recognizes patterns and recommends strategies over time.

Yes. ReasoningBank tracks agent decisions and outcomes across tasks, recognizing which strategies perform best in specific contexts. Agents continuously update their behavior based on learned patterns, improving decision-making without manual reconfiguration.

ReasoningBank requires agentic-flow v1.5.11 or later, AgentDB for persistent experience storage, and Node.js 18 or higher. These provide the workflow orchestration, data persistence layer, and runtime environment necessary for adaptive learning.

Pattern recognition in ReasoningBank identifies which review strategies yield the best results for specific code types. For example, agents learn that static analysis-first approaches reduce false positives on medium-complexity TypeScript, then automatically apply that optimized strategy to similar tasks.

Yes. ReasoningBank provides meta-cognitive capabilities—pattern recognition and strategy optimization—that allow agents to reflect on their own performance and self-improve. This enables continuous learning without external retraining or manual strategy adjustments.

ReasoningBank optimizes strategy selection across domains including code review, incident response, and data processing. Any workflow where agent decisions vary by context and outcomes are measurable benefits from continuous pattern recognition and adaptive strategy application.