Multi-Agent Systems

An expert course on systems of cooperating agents: role design, handoffs, shared state, and containing cascading failures. Learners build a multi-agent workflow and stress-test its coordination.

What this course covers

The course runs across 12 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Architectural Foundations of Multi-Agent Systems — Analyze the core paradigms of multi-agent systems, exploring when to distribute tasks across specialized agents versus using a single monolithic agent.
Designing Specialized Agent Roles and Boundaries — Define clear agent personas, system instructions, and tool access boundaries to minimize overlap and maximize task execution efficiency.
Implementing Agent Handoffs and Routing Protocols — Configure explicit transfer protocols and routing logic to pass execution control and context smoothly between agents.
Coordination Patterns: Orchestration vs. Choreography — Compare centralized orchestrator models with decentralized peer-to-peer agent choreography to determine the optimal topology for your workflow.
Coding Multi-Agent Coordination Workflows — Write execution loops that manage sequential, parallel, and conditional agent execution patterns using a programmatic framework.
Shared State Architectures and Context Propagation — Design shared memory, blackboard systems, and context-passing mechanisms to maintain a single source of truth across agent transitions.
Resolving Concurrency and State Conflicts — Implement locking mechanisms, state merging, and conflict resolution strategies when multiple agents attempt to modify shared state concurrently.
Analyzing Multi-Agent Failure Modes — Identify system-level risks unique to multi-agent networks, including infinite handoff loops, hallucination propagation, and cascading deadlocks.
Designing Failure Containment and Circuit Breakers — Code self-healing patterns, retry budgets, fallback agents, and circuit breakers to isolate failures and prevent system-wide crashes.
Observability, Logging, and Handoff Traceability — Instrument your multi-agent system with distributed tracing to visualize agent transitions, tool execution, and token usage in real time.
Lab: Building a Resilient Multi-Agent Workflow — Develop a complete, end-to-end multi-agent system utilizing shared state, explicit handoffs, and custom tools to solve a complex business process.
Lab: Chaos Engineering and Stress-Testing — Inject synthetic failures, rate-limit exhausts, and corrupted inputs into your multi-agent workflow to validate and harden its error-recovery mechanisms.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick.

Who it is for

Builders, architects. The material is pitched at expert level, and takes roughly 180 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Category: Agents track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including guided lab, code lab exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a code assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

All AI Agents training courses →
Computer-Use & Browser Agents — This course covers agents that operate software the way people do - clicking, typing, browsing. Learners build a supervised computer-use workflow and learn where the reliability boundary really sits.
AI Agents & Agentic Workflows — Learners understand and design agentic systems that take multi-step actions with tools. The course covers what an agent is, tools and function calling, multi-step and multi-agent designs, MCP and orchestration, and guardrails. Participants finish able to design agents that are useful and safe.
Agent Memory & State — A design course on giving AI systems usable memory: architectures for persistence, what to store versus retrieve, and handling privacy and staleness. Learners add durable memory to a working agent.
Orchestration Frameworks — A judgment-heavy course on orchestration tooling (LangChain/LlamaIndex-class): what it solves, what it obscures, and when plain code is better. Learners build the same workflow twice and compare.
Zapier & Make: Advanced — An advanced automation course for Zapier and Make: multi-step workflows with AI reasoning steps, branching logic, and robust error handling. Learners ship an automation reliable enough to run unattended.
AI Automation Assessment — AI Automation Assessment: designing and running a reliable workflow.
Advanced Agent Engineering Pathway — Design, orchestrate, and operate complex agentic systems.

Frequently asked questions

How long does this course take?

It runs roughly 180 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code CAT-MAG.