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Comprehensive overview of memory architectures for AI agents — short-term context, long-term vector storage, episodic memory, and hybrid approaches.
Lessons learned from building multi-agent systems in production. Covers coordination patterns, failure modes, and when single agents are enough.
How to write effective tool descriptions and system prompts that help LLMs reliably select and use the right tools with correct parameters.