SKU/Artículo: AMZ-B0G4R7MKS3

Scaling LLM Agents: Distributed Cognition & Multi-Agent Ecosystems: A Practical Guide to Architecting Collaborative, Tool-Driven, and Self-Optimizing AI Systems (The Agentic AI Engineering Series)

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0.81 kg

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Amazon

Sobre este producto

How to structure LLM agents into clusters with clear roles, capabilities, and communication protocols
Coordinator and scheduler patterns for robust multi-agent task execution
Decentralized routing fabrics for fast, scalable message passing
Shared vector memory systems for persistent state, grounding, and context fusion
Reflection and optimization loops that help agents correct themselves and learn from outcomes
Tool-driven orchestration using APIs, function calling, and external workflows
Monitoring, evaluation, and telemetry layers to keep your multi-agent system safe, reliable, and transparent
Scalable system topologies for production workloads, real-time reasoning, and enterprise automation
AI engineers designing intelligent, high-performance systems
Software architects modernizing applications with agentic patterns
Founders and CTOs building AI-first products
Researchers exploring distributed cognition and emergent behaviors
Developers wanting to go beyond prompts and learn real LLM engineering

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Scaling LLM Agents: Distributed Cognition & Multi-Agent Ecosystems: A Practical Guide to Architecting Collaborative, Tool-Driven, and Self-Optimizing AI Systems (The Agentic AI Engineering Series)

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Scaling LLM Agents: Distributed Cognition & Multi-Agent Ecosystems: A Practical Guide to Architecting Collaborative, Tool-Driven, and Self-Optimizing AI Systems (The Agentic AI Engineering Series)

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