Agentic AI
Advanced
Multi-Agent Orchestration
Coordinate specialized agents with explicit contracts and bounded handoffs.
Course Overview
Learn when multiple agents add value, how to keep their responsibilities narrow, and how to test delegation paths. The examples emphasize predictable interfaces, budgets, and failure handling.
What You Will Learn
Coordinate worker agents using supervisor, peer-to-peer, and hierarchical topologies
Define explicit agent delegation contracts and structured payload interfaces
Prevent infinite agent delegation loops with strict execution budgets
Implement conflict resolution when worker agents produce contradictory outputs
Trace multi-agent execution paths using span context propagation
Tools & Technologies Used
LangGraph
Python 3.11
FastAPI
Pydantic
OpenTelemetry
Structured Curriculum
2 Modules · 6 Lessons · 210 Minutes Total
Module 1
Module 1: Multi-Agent Topologies & Delegation Contracts
3 lessons
Module 1: Multi-Agent Topologies & Delegation Contracts
Structure agent hierarchy and handoff mechanisms.
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Supervisor vs. Peer Orchestration Patterns
Analyze when centralized routing outperforms decentralized peer handoffs.
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Defining Handoff Schemas & Memory Isolation
Build scoped memory buffers so specialized agents receive only relevant contextual history.
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Execution Budgets & Infinite Loop Traps
Enforce maximum delegation depths and time-to-live execution counters across agent boundaries.
Module 2
Module 2: Consensus, Error Propagation & Observability
3 lessons
Module 2: Consensus, Error Propagation & Observability
Manage agent failures, consensus aggregation, and distributed tracing.
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Consensus Aggregation & Conflict Resolution
Build a voter node that synthesizes responses from multiple domain-expert agents.
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Graceful Fallback & Worker Replacement
Handle worker agent timeout or invalid output by rerouting tasks to alternate nodes.
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Distributed Tracing of Multi-Agent Runs
Propagate trace correlation IDs across agent calls to render clean visual trace graphs.
Practical Project & Capstone Outcome
🚀 Capstone Project
Automated Software Code Review Panel
Build a multi-agent system comprising a Security Auditor Agent, Performance Analyst Agent, and Style Guide Agent led by a Chief Reviewer Supervisor that delivers a consolidated code review report.
Prerequisites
- Understanding of single-agent loops and basic graph workflows
- Python async programming
Intended Audience
- Senior Backend Engineers building complex automated agent teams
- System Architects designing multi-domain automation systems
Instructor Information
O
Owen Carter
Course Author & Industry Expert
Owen Carter is a Principal Distributed Systems Engineer who specializes in multi-agent microservice architecture and cloud orchestration.
Frequently Asked Questions
When should I use multi-agent systems instead of one big prompt?
When tasks require distinct domain tools, isolated memory contexts, or specialized evaluation criteria that overwhelm single-prompt contexts.