Step-by-step tutorials on building voice and chat AI agents using OpenAI Agents SDK, Realtime API, function calling, multi-agent orchestration, and production deployment patterns.
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Understand how to evaluate text-to-SQL systems using the Spider and BIRD benchmarks, implement execution accuracy metrics, and build custom evaluation datasets for your specific database schema.
Learn how to take text-to-SQL from prototype to production with query caching, usage analytics, performance monitoring, cost optimization, and scaling strategies for high-traffic deployments.
Learn how to architect AI agent swarms that self-organize without a central coordinator. Covers pheromone-like signaling, emergent task allocation, and decentralized scaling patterns with Python examples.
Design robust communication protocols for multi-agent systems including message schemas, capability advertisement, negotiation protocols, and service discovery mechanisms with practical Python implementations.
Explore multi-agent reinforcement learning (MARL) concepts including reward shaping, cooperative versus competitive strategies, and policy gradient methods for agent teams with practical Python implementations.
Build an end-to-end code generation pipeline using five specialized AI agents — Planner, Coder, Reviewer, Tester, and Deployer — with complete handoff data structures and orchestration logic in Python.
Master Hierarchical Task Network (HTN) planning for AI agents including task decomposition, method selection, plan refinement, and execution monitoring with complete Python implementations.
Compare three fundamental memory sharing architectures for multi-agent systems — blackboard, message passing, and shared vector stores — with implementation patterns, consistency considerations, and performance tradeoffs.
