memory-blackboard

To share context between multiple AI agents, you can implement a blackboard pattern within session memory. This creates a structured directory for inter-agent communication, enabling agents to read previous outcomes and build progressive context without redundant exploration.

Multi-agent workflows waste tokens when agents lack shared memory and independently re-derive the same information. Implementing a blackboard pattern caches results in session memory, preventing duplicate exploration and significantly reducing token usage across the workflow.

Implementing a blackboard pattern for cross-domain tasks involves writing phase results to a shared session memory directory. Later phases read these cached files to align their execution with earlier requirements, enabling seamless progressive context building across different domains.

Yes, you can use session memory to cache results for multi-phase workflows. By writing decisions to structured files within a session directory, subsequent phases retrieve the cached context directly, ensuring later implementations align with earlier project outcomes.

The best way to prevent duplicate work in multi-agent systems is adopting a shared memory protocol using the blackboard pattern. This approach facilitates efficient context sharing by caching derived information, ensuring agents do not re-explore identical domains or re-derive existing data.