Free Course — No signup required

Build Multi-Agent
AI Systems

Five project-based lessons on building sophisticated multi-agent systems: agent factory pattern, orchestrator with JSON planning, debate loop, parallel pipeline with concurrency control, and production observability with AgentRun.

5 lessons
Self-paced
~60 min/day
Free forever
Start Day 1 — Free → See what's covered
What you'll learn

Multi-agent systems are how AI gets hard work done.

Single prompts have limits. Agent systems can plan, debate, parallelize, and self-correct. This course shows you how to build them in a way that actually works in production.

  • Build a reusable Agent class with the factory pattern
  • Create an orchestrator that breaks complex tasks into JSON plans
  • Implement a debate loop where agents generate, critique, and revise
  • Run multiple agents in parallel with controlled concurrency
  • Track agent state, tool calls, and results with AgentRun
  • Deploy multi-agent systems with proper error handling and fallbacks
5x
more task complexity handled
5
practical lessons
$0
course cost
1
working agent system
5-Day Curriculum

Every lesson has a real exercise.
No theory without practice.

01
Agent Factory — Reusable Agent Architecture
Build a clean Agent class with configurable system prompts, tools, and temperature. The foundation for every agent you'll build.
Agent classFactory patternSystem prompts
→
02
Orchestrator — JSON Planning and Task Decomposition
Build an orchestrator agent that breaks complex goals into JSON plans and delegates subtasks to specialized worker agents.
OrchestrationJSON planningTask delegation
→
03
Debate Loop — Generate, Critique, Revise
Build a two-agent debate loop where a generator produces output and a critic agent evaluates and requests revisions until quality meets a threshold.
Debate loopCritic agentQuality control
→
04
Parallel Pipeline — Concurrent Agent Execution
Run multiple independent agents in parallel using Promise.all with a concurrency limiter. Process 10x more work in the same time.
Parallel executionConcurrency controlPromise.all
→
05
AgentRun — Observability and State Management
Build an AgentRun class that tracks the full execution trace: inputs, outputs, tool calls, token usage, errors, and timing for every agent.
ObservabilityState trackingExecution traces
→
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Your instructor
B
Bo Peng
Founder, Precision AI Academy

Kaggle Top 200 data scientist. Built production multi-agent systems for real clients. Trained 400+ developers on AI systems design. This course is distilled from what actually works — not the hype.

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