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.
About this course
Multi-agent AI systems — networks of AI models that collaborate, delegate, and coordinate to complete complex tasks — represent the frontier of AI engineering in 2026. Building agentic systems means understanding orchestration patterns, tool use, memory, inter-agent communication, and how to handle the unique failure modes that emerge when AI models make autonomous decisions. This multi-agent AI tutorial covers the architectures that power autonomous coding assistants, research agents, and workflow automation at scale.
This course is for AI engineers and senior developers who have experience with single-model AI applications and want to build systems where models reason over long horizons, delegate subtasks, and use tools to interact with the world. After completing it you will be able to design and implement multi-agent pipelines using Claude, understand when agents are the right architecture, and build the guardrails that make autonomous AI systems trustworthy.
Frequently asked questions
What is a multi-agent system?
A multi-agent system is an AI architecture where multiple models (agents) work together rather than a single model handling everything. An orchestrator agent breaks down a task and delegates to specialised sub-agents — a researcher, a coder, a reviewer — each with its own tools and focus. The agents communicate results back to the orchestrator, which synthesises them. This approach handles tasks too long or complex for a single context window.
When should I use a multi-agent architecture?
Multi-agent architectures make sense when: a task requires more steps than fit in a single context window, different parts benefit from specialised prompting, parallel execution would save significant time, or you need independent verification. For simpler tasks, a single well-prompted model with tool calling is faster, cheaper, and easier to debug than a multi-agent system.
What tools do agents need?
Agents need tools that let them interact with the world: web search, code execution, file reading and writing, database queries, API calls, and communication with other agents. Tool design is a critical skill — tools need clear descriptions so the model knows when to use them, structured output formats, and error handling. This course covers designing robust tools and the tool-calling patterns that make agents reliable.
How do I handle failures in a multi-agent system?
Multi-agent systems fail in more complex ways than single-model applications: agents can loop, hallucinate tool calls, produce inconsistent outputs, or cascade errors from one agent to another. Robust systems implement: step limits, output validation between agents, human-in-the-loop approval for high-stakes actions, comprehensive logging of every decision, and graceful fallback when an agent cannot complete its task.
What is the difference between an agent and a chain?
A chain is a fixed sequence of LLM calls where each step output feeds into the next — deterministic and predictable. An agent is dynamic — it decides at runtime which tools to call, in what order, and when to stop, based on what it observes. Chains are easier to debug; agents are more flexible for open-ended tasks. Most production systems use chains where possible and agents where the task genuinely requires dynamic decision-making.