cognitive-systems-engineering

Design a cognitive architecture by implementing tiered memory patterns for working, episodic, semantic, and procedural memory using sliding windows, vector stores, and context files. This approach provides persistent, goal-directed AI agents that manage memory and make repeatable decisions.

The best way to implement decision cycles for multi-agent coordination is to select OODA or ReAct loops with retry counters and escalation. Pair these with orchestrator-workers, blackboard, pipeline, or debate topologies to decompose complex tasks across specialist agents.

RAG integrates with episodic and semantic memory by using vector stores to retrieve past interactions and relevant knowledge via embeddings. This grounds the agent, allowing it to retain session context and retrieve historical data for production decision logic.

Yes, you can use OODA or ReAct patterns for production agent retry and escalation logic. These decision cycles support tool integration and high-stakes verification by providing structured loops with retry counters and explicit escalation pathways.

You need multi-agent patterns for orchestrating AI tool use when decomposing complex tasks across specialists, such as customer support or data pipelines. Orchestrator-workers and debate topologies coordinate tool use, retries, and task delegation.

Stateless prompt-response agents lack persistent memory, repeatable decision cycles, and multi-agent coordination. This prevents them from retaining session context, retrieving past interactions via embeddings, or delegating specialized tasks in high-stakes verification scenarios.