No Hype AI: Agent Orchestration

When one agent is not enough — the limits of single agents (context, complexity, reliability) and the case for multi-agent systems.

The four dominant coordination patterns — supervisor, peer-to-peer, hierarchical, and pipeline — and when to reach for each.

How agents talk: message passing, shared state (the blackboard), and event-driven designs — with serialization and protocol design.

Breaking big goals into agent-sized work — functional, data, temporal, and hybrid decomposition, dependencies, and result reassembly.

Build a multi-agent research workflow with Claude — agent definitions, handoffs, shared context, and orchestration-layer guardrails.

Combining durable workflow engines (Temporal, Prefect, Airflow) with AI agents for timeouts, retries, and audit trails.

Keeping fleets resilient — retry strategies, fallbacks, human-in-the-loop escalation, circuit breakers, and dead-letter queues.

Seeing inside multi-agent systems — distributed tracing, structured logging, metrics, and OpenTelemetry instrumentation.

Controlling spend — token budgeting, prompt caching, intelligent routing, and result caching for large token savings.

Shipping for real — deployment, SLOs, chaos testing, graceful degradation, agent-to-agent auth, and a production-readiness checklist.