Multi-Agent Systems & Orchestration
Design, secure, and scale systems of coordinating AI agents in production.
About this course
This is the advanced course: what happens when one agent isn't enough. You'll cover supervisor/worker topologies, graph-based orchestration, adversarial self-correction, graph RAG, and the guardrails that keep a fleet of autonomous agents from destroying your data, your budget, or your customers' trust. Every module pairs architecture with the failure modes that show up only at scale — deadlocks between agents, prompt injection across trust boundaries, runaway cost spirals, and eval suites that actually catch regressions before your users do. This is dense, applied, production-grade material for engineers who are done reading blog posts about agents and are ready to ship systems of them. It assumes you can already build and run a single agent — if you want a full deep dive on that first (deployment, memory, tool design, cost/security governance for one agent), Building Production AI Agents covers it in more depth than this course's faster-paced review.
What you'll learn
- ✓Choose and justify the correct multi-agent topology (supervisor/worker, hierarchical, debate, swarm) for a given problem — and recognize when a single agent is the better engineering decision
- ✓Implement graph-based and role-based orchestration with explicit state machines, message schemas, and deadlock/loop detection
- ✓Design graph-augmented, multi-hop RAG pipelines with re-ranking and quantitative retrieval evaluation, including freshness strategies for large knowledge bases
- ✓Defend multi-agent systems against prompt injection, unsafe tool/code execution, and PII leakage using sandboxing, least-privilege permissions, and human-in-the-loop approval gates
- ✓Build automated eval suites — including calibrated LLM-as-judge pipelines — that catch behavioral regressions across model and prompt versions before deployment
- ✓Deploy and operate concurrent agent workloads with queuing, backpressure, multi-provider rate-limit management, cost-optimized model routing, and alerting tuned to agent-specific failure modes
Curriculum
Module 1
Multi-Agent Design Patterns
The core topologies for coordinating multiple LLM agents — supervisor/worker, hierarchical trees, debate, and swarm — and the engineering judgment to know when each is justified versus when it's expensive overkill.
- Supervisor/Worker and Hierarchical Agent Trees34 min
- Debate and Adversarial Patterns for Self-Correction32 min
- Swarm/Parallel-Exploration Patterns and the Overkill Question33 min
- Module 1 Quiz: Multi-Agent Design PatternsQuiz
Module 2
Orchestration Frameworks & Message Passing
How graph-based, role-based, and conversational orchestration frameworks actually structure inter-agent state and messages — and how to prevent the deadlocks and infinite loops that emerge only when multiple agents share control flow.
- Graph-Based Orchestration: Nodes, Edges, and Explicit State34 min
- Role-Based Crews and Conversational Multi-Agent Patterns32 min
- Message and State Design; Handling Deadlocks and Infinite Loops33 min
- Module 2 Quiz: Orchestration Frameworks & Message PassingQuiz
Module 3
Advanced RAG & Knowledge Systems
Beyond flat vector search: knowledge-graph-augmented retrieval, multi-hop reasoning, re-ranking pipelines, quantitative retrieval evaluation, and the operational work of keeping a large knowledge base fresh.
- Graph RAG: Knowledge-Graph-Augmented Retrieval34 min
- Multi-Hop Retrieval, Reasoning, and Re-ranking Pipelines33 min
- Retrieval Evaluation Metrics and Keeping a Knowledge Base Fresh32 min
- Module 3 Quiz: Advanced RAG & Knowledge SystemsQuiz
Module 4
Guardrails, Safety & Security
Concrete defenses against prompt injection, sandboxed execution for tools and generated code, PII handling, human-in-the-loop approval for high-risk actions, and least-privilege permission design across a multi-agent system.
- Prompt Injection: Attack Vectors and Concrete Defenses34 min
- Sandboxing Tool and Code Execution; PII Detection and Handling32 min
- Human-in-the-Loop Approval Gates and Least-Privilege Tool Permissions33 min
- Module 4 Quiz: Guardrails, Safety & SecurityQuiz
Module 5
Evaluation at Scale
Building automated eval suites that actually catch agent behavior regressions, calibrating LLM-as-judge pipelines against their known failure modes, and testing changes safely across model versions and live traffic.
- Building Automated Eval Suites for Agent Behavior33 min
- LLM-as-Judge Methodology and Its Pitfalls32 min
- Regression Testing Across Versions and Safe A/B Testing of Live Behavior33 min
- Module 5 Quiz: Evaluation at ScaleQuiz
Module 6
Production Deployment & Scaling
Infrastructure for concurrent agent workloads — queuing, backpressure, multi-provider rate-limit management, cost optimization through routing/caching/batching, and monitoring tuned to the specific ways agent systems fail.
- Infrastructure Patterns for Concurrent Agent Workloads34 min
- Rate-Limit Management and Cost Optimization: Routing, Caching, Batching33 min
- Monitoring and Alerting for Agent-Specific Failure Modes33 min
- Module 6 Quiz: Production Deployment & ScalingQuiz
Multi-Agent Systems & Orchestration — Final Exam
Comprehensive final exam covering the entire course.