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11 articles
Explore Google''s Agent-to-Agent (A2A) protocol for production multi-agent systems. Learn agent cards, task lifecycles, and how to orchestrate multiple AI agents at scale.
Master the art of designing tools that LLMs can reliably use. Learn schema patterns, error handling, idempotency, and production tool registries.
Design APIs for AI agents: structured errors, idempotency keys, verbose context, bulk operations, OpenAPI specs, token-based rate limiting, and version stability.
Deploy CrewAI multi-agent systems to production. Learn crew composition, memory systems, custom tools, and scaling patterns for reliable AI teams.
Master LangGraph for production AI agents. Learn stateful workflows, checkpointing, human-in-the-loop patterns, and deployment strategies.
Build production-grade stateful agents with LangGraph: graph definitions, Postgres checkpointing, human interrupts, streaming, error handling, and deployment.
Design bulletproof LLM agents with structured tool definitions, parallel execution, result validation, human-in-the-loop gates, and comprehensive observability.
Learn how Anthropic''s Model Context Protocol enables AI agents to securely share tools and context. We explore the open standard, build an MCP server, and compare it to function calling.
Build scalable multi-agent systems using the orchestrator-worker pattern. Learn task routing, state management, error recovery, and production deployment patterns.
Learn the Plan-and-Execute pattern for slashing AI inference costs. Use frontier models for planning, cheap models for execution, and optimally route tasks by type.
Learn how agentic RAG systems use reasoning and iterative retrieval to outperform static RAG pipelines, including CRAG, FLARE, and self-ask decomposition patterns.