In this module, you will learn how to design multi-agent systems that coordinate through GitHub-native artifacts, remain observable through logs and workflow outputs, and recover safely through retry, rollback, and human escalation.
By the end of this module, you'll be able to:
- Define agent responsibilities and scope boundaries within the SDLC
- Coordinate multi-agent workflows using GitHub Actions events and orchestration patterns
- Isolate agent execution using branches, workflows, permissions, and concurrency controls
- Detect and resolve conflicts using GitHub-native validation and review mechanisms
- Ensure observability, attribution, and traceability of agent actions
- Diagnose failures and implement recovery strategies for reliable multi-agent systems
This module introduces the foundations of agent memory, state management, and evaluation. You'll learn how to structure memory, persist progress through GitHub artifacts, maintain consistency across environments, and define clear signals for success.
By the end of this module, you'll be able to:
- Learn agent memory strategies using short-term, long-term, and external memory
- Learn how to maintain and persistent agent state and manage context drift
- Learn how to manage agent state across tools and environments
- Understand agent evaluation signals and success criteria
Agent systems must operate within clear governance frameworks to ensure security, compliance, and accountability. In the GitHub ecosystem, governance is enforced through repository controls, workflows, and policies that help prevent unauthorized changes, unsafe deployments, and sensitive data exposure.
By the end of this module, you'll be able to:
- Define risk-based autonomy and action boundaries for agent systems
- Enforce governance using GitHub-native controls such as rulesets, checks, CODEOWNERS, and environments
- Design human-in-the-loop workflows for high-risk actions
- Control agent capabilities using least-privilege permissions
- Make agent actions observable, traceable, and auditable
- Maintain governance and operational reliability over time

Syllabus

Multi-Agent Systems and Orchestration
- Introduction
- Define multi-agent responsibilities in the SDLC
- Orchestrate agents using GitHub workflows
- Isolate execution - branches, workflows, permissions, and concurrency
- Detect and resolve conflicts using GitHub-native arbitration
- Make the system observable - attribution, evidence, and handoffs
- Operate reliably at scale - diagnose failures and recover safely
- Knowledge Check
- Summary
Memory, State, and Evaluation
- Introduction
- Implement agent memory strategies
- Persist agent state and manage context drift
- Ensure continuity of agent memory and state across tools and environments
- Define evaluation signals and enforce quality gates
- Analyze agent failures and improve behavior
- Knowledge Check
- Summary
Governance, guardrails, and operations
- Introduction
- Define risk-based autonomy and action boundaries
- Enforce governance with GitHub controls
- Design human-in-the-loop workflows
- Control agent capabilities using least privilege
- Make actions observable, traceable, and auditable
- Maintain governance and operational reliability
- Knowledge check
- Summary