Multi-Agent Systems with Claude
Single-prompt AI is powerful. Multi-agent AI is transformative. When you compose agents that can use tools, call each other, maintain state, and operate autonomously over long horizons, you unlock a fundamentally different class of product. This course covers the full stack of multi-agent engineering: tool use, the Model Context Protocol, orchestrator patterns, human-in-the-loop design, state management, testing, security, and shipping agents to production.
What you'll learn
Course outline
Free — no account needed
What Are AI Agents?
The agent loop, autonomy levels, and when to use agents vs simple prompts
Tool Use with Claude
Define tools, handle tool calls, return results, and build multi-tool agents
MCP — Model Context Protocol
Connect Claude to any data source or tool with the open standard for agent tool use
Full course — $99 one-time
Orchestrator and Subagent Patterns
Design multi-agent workflows — fan-out, sequential chains, specialist agents, and result aggregation
Human-in-the-Loop Design
Know when agents must pause for approval — interruption points, confirmation gates, and override patterns
State Management for Agents
Persist agent state across sessions, manage long context, and handle multi-step workflows correctly
Testing Agents
Unit test tools, integration test agent behaviour, and build eval suites for autonomous workflows
Multi-Agent Security
Prompt injection in agentic contexts, tool sandboxing, least privilege, and trust boundaries
Deploying Agents to Production
Background workers, queues, timeouts, observability, and cloud deployment patterns for production agents
Build a Real Agent — End to End
Apply the full course: build, test, secure, and deploy a production-grade multi-agent workflow
Get the full course
10 lessons — from the agent loop to building and deploying a production-grade multi-agent workflow.