Building Multi-Agent Systems Micro-Credential

Building Multi-Agent Systems Micro-Credential

Duration: 3-4 Hours

Description

A single agent hits a ceiling fast — most real work needs several specialists coordinating, not one generalist trying to do everything. This session picks up where Beginner’s Guide to Agentic AI leaves off: designing systems where multiple agents divide a task, hand work off to each other, and check one another’s output. Participants compare coordination patterns — a manager agent delegating to workers, versus a peer-to-peer handoff model — connect agents to shared tools and data through the Model Context Protocol (MCP), and build a working multi-agent workflow from scratch.

Audience

This session is for developers who have completed Beginner’s Guide to Agentic AI or built a single tool-using agent on their own and are ready to design systems where multiple agents work together. It’s not a first introduction to agents — it assumes you already know what an agent loop is and want to go further.

Objectives

  • Identify when a task calls for multiple agents instead of one
  • Compare coordination patterns: manager/worker delegation versus peer-to-peer handoff
  • Connect agents to shared tools and data using MCP
  • Build, test, and debug a working multi-agent workflow

Prerequisites

Completion of Beginner’s Guide to Agentic AI Micro-Credential or equivalent experience building a single tool-using agent is expected. Basic Python proficiency is required.

Related AI Courses

See the full AI training roadmap and course directory for how this fits into a broader learning path.

New to agents entirely? Start with Beginner’s Guide to Agentic AI Micro-Credential first. For a full, production-grade treatment of multi-agent systems, planning, and reliability, see Advanced AI Agents or Introduction to AI Agents. Concerned about the security implications of autonomous agents? See AI Security & Red-Teaming Micro-Credential.

Course Outline

Module 1: Why Multiple Agents?

  • The Limits of a Single Agent
  • Coordination Patterns: Manager/Worker vs. Peer-to-Peer
  • Where Frameworks Fit In
  • Lab – Decompose a Complex Task into Agent Roles

Module 2: Building a Coordinated System

  • Designing Agent Roles and Handoffs
  • Shared Context and Memory Across Agents
  • Connecting Agents to Tools with MCP
  • Lab – Build a Two-Agent Research-and-Write Workflow

Module 3: Reliability at Scale

  • Evaluating Multi-Agent Output
  • Failure Modes: Loops, Conflicting Actions, Runaway Cost
  • Guardrails and Human Checkpoints
  • Lab – Add a Review Checkpoint to a Multi-Agent Pipeline