cognitive-architectures

A cognitive architecture for language agents provides modular memory layers, action-space definitions, and decision-loop orchestration like ReAct or OODA to manage perception, action selection, and planning for reliable, goal-directed behavior.

The best way to implement memory management for autonomous agents is using modular patterns for working, episodic, semantic, and procedural memory, combined with hybrid keyword and vector retrieval strategies and memory decay to preserve long-term user preferences.

Decision loops like ReAct and OODA work for tool-enabled workflows by orchestrating action selection through structured templates, applying reflection strategies, and executing retry or fallback controls to mitigate context overflow and action loops during multi-step tasks.

Yes, you can use structured JSON outputs for tool integration in cognitive architectures. The design provides guidance on defining external and internal actions, token-efficient tool descriptions, and structured JSON arguments for safe API and knowledge store integrations.

You prevent context overflow and infinite action loops in LLM agents by applying decision-making templates with built-in reflection strategies, retry and fallback controls, and specific mitigations designed for agentic behaviors within the cognitive architecture.

Yes, this approach supports hybrid retrieval strategies for long-term memory by defining patterns that combine keyword and vector retrieval across semantic and episodic memory layers, ensuring efficient recall and memory decay for autonomous assistants.