AGENTIC ARCHITECTURE
Multi-Agent Systems
Learn how specialized agents communicate, coordinate and delegate work to solve complex problems as a reliable system.
Multi-Agent Systems Learning Path
Follow the modules in order to move from single agents to specialized teams, coordination patterns, evaluation and production-ready multi-agent architectures.
From Single Agent to Multi-Agent
Why single agents hit a wall at scale, what the industry data actually shows about multi-agent systems in 2026, and the honest trade-off you're making the moment you add a second agent.
Agent Roles and Specialization
What it actually means to give agents different roles, the canonical Planner/Executor/Critic pattern real production systems converge on, and why role separation is about independence — not just dividing labor.
Agent Communication
How agents actually pass information to each other — message structures, communication modes, why it gets measurably harder as agent count grows, and how Google's real A2A protocol solves this at industry scale.
Agent Coordination and Collaboration
Who decides which agent acts when — task allocation, synchronization, dependencies, and duplicate work — grounded in Anthropic's own published engineering lessons and real research into agent coordination failures.
Agent Delegation
How a real delegation decision actually gets made — capability matching, cost, latency, reliability, and permissions — with real 2026 production data, a counter-intuitive capability finding, and a real security incident.
Agent Orchestration
The control plane that manages a multi-agent system's complete execution lifecycle — who decides what runs first, how results get aggregated, and why most production agent outages are orchestration failures, not model failures.
The Supervisor Pattern
The 2026 production default for multi-agent architecture — how it works, why it became the standard starting point, and the precise, numbered thresholds at which it stops being the right choice.
Hierarchical Multi-Agent Systems
What happens when a flat supervisor runs out of room — stacking supervisors into layers, the precise latency cost each additional tier adds, and two real production systems built this way.
Peer-to-Peer Agent Systems
What happens with no central coordinator at all — real topology thresholds, the actual mechanics of stigmergic coordination, a convergence formula, and the honest security incidents this pattern has already produced.
Sequential vs. Parallel Agent Execution
When agent work must happen in order, when it doesn't, and the real mechanics of race conditions — including a failure mode that produces silent data corruption indistinguishable from a model error.
Agent Routing
The dispatch mechanism deciding which agent a request reaches in the first place — three real routing strategies, why misrouted queries fail silently, and real 2026 data showing exactly how routing accuracy degrades as systems grow.
Shared State and Memory
What agents need to see in common versus what should stay private, real ownership architectures from named production systems, and four precisely defined failure modes — including a real, dated security incident.
Conflict Resolution
Why naive voting amplifies shared errors instead of canceling them, a real study finding structured debate performing worse than independent voting, and the real distributed-systems math behind reaching consensus among unreliable agents.
Planning in Multi-Agent Systems
What changes when a plan has to account for multiple agents, not just multiple steps — formal task decomposition, why LLM-generated plans need classical validation, and when a failure needs local recovery versus a full replan.
Other Architectural Patterns
Pipeline, blackboard, event-driven, and market-based architectures — including a pattern traced to a real 1980s AI system still running in named production companies today — plus how every pattern in this course actually composes in practice.
Multi-Agent Failure Modes
How multi-agent systems actually break in production — a real 2026 study showing one falsehood injected at the wrong point causes 100% system-wide failure across five major frameworks, plus circular delegation, poor termination, and the rest of the practical failure catalog.
Multi-Agent Security
Why more agents means a larger attack surface — real, sanitized attack payloads used against production multi-agent frameworks, a documented attack against the A2A protocol from Module 3, and real 2026 incident data.
Multi-Agent Observability
The span-hierarchy mechanism that makes cross-agent root cause analysis possible, real named observability platforms and how they differ, and a current research finding that observability data itself can reveal where to attack a system.
Evaluating Multi-Agent Systems
Why scoring individual agents in isolation doesn't work — the three real levels a evaluation needs, a precise distinction between agent failures and infrastructure failures, and the five-dimensional CLEAR framework built specifically for multi-agent systems.
Production Considerations
The real gap between pilot and production scale — a precise 2026 survey finding only 14% of enterprises successfully scaled an agent organization-wide — plus cognitive monoculture, shadow deployment, and a real named company's production scaling data.
Building a Multi-Agent System
Taking the recurring legal-contract pipeline from concept through pseudocode through working Python to real LangGraph mechanics — including the specific technical reason checkpointing turns a fragile loop into a production tool.
Real-World Multi-Agent Systems
Real, named companies running multi-agent systems in production — precise 2026 adoption data by industry, direct evidence the Planner-Executor-Critic pattern taught throughout this course is the most common real architecture, and the honest data on how many deployments actually deliver value.
When Multi-Agent Is NOT Worth It
The final module: a complete decision framework for when multiple agents are worth their real cost, synthesizing every finding this course has gathered — and this course's closing principle.
WHY MULTI-AGENT?
More agents means more coordination, not automatically better results
Multi-agent systems can divide complex work across specialists, but they also introduce communication overhead, shared-state challenges and new failure modes. Learn the patterns and tradeoffs that make collaboration worth the added complexity.