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Developing in Agentic AI Systems

Course Summary

This course builds practical skills for developing, deploying, and managing agentic AI systems within GitHub-based software development workflows. Participants explore how to integrate AI agents into the software development lifecycle (SDLC) including designing agent architecture, configuring tools and execution environments, and managing agent memory, state, and execution. Through hands-on learning, participants evaluate and tune agent performance, implement governance and guardrails, and coordinate multi-agent systems to operate, supervise, and govern AI agents safely and reliably in production, using GitHub as the system of record and control plane.

Prerequisites:

  • Experience with the software development lifecycle (SDLC), GitHub workflows, and code review/security practices
  • Familiarity with GitHub repositories, branches, pull requests, and GitHub Actions
  • Experience with coding agents such as GitHub Copilot, including MCP servers and agent customization 
  • Awareness of repository governance concepts 

NOTE: Pluralsight requires each participant to have access to the Generative AI tool(s) used in this course. Where Customer does not provide that access, Pluralsight will provide it and AI lab fees will apply.

Purpose
Operate, supervise, and govern AI agents in production using GitHub as the control plane
Audience
IT professionals who operate, integrate, supervise, and govern AI agents in production SDLC workflows
Role
AI Engineers | Software Developers | DevOps Engineers | Application Developers | Solution Architect | Product Managers | Security Engineers
Skill level
Intermediate
Style
Lecture | Hands-on Activites | Labs
Duration
1 day
Related technologies
GitHub Copilot | GitHub Actions | Model Context Protocol (MCP) 

 

Learning Objectives
  • Define agentic AI in the SDLC and describe GitHub's role as the system of record and control plane
  • Map agent responsibilities to SDLC stages and separate planning, reasoning, and execution
  • Configure agent tools, MCP servers, and execution environments with clear boundaries and permissions
  • Implement agent memory strategies and persist state across tools and environments
  • Define evaluation signals, analyze agent failures, and tune agent behavior
  • Orchestrate and observe multi-agent workflows, including conflict detection and recovery
  • Define autonomy levels and implement guardrails, human-in-the-loop workflows, and accountability controls

What you'll learn:

In this Developing in Agentic AI Systems course, you'll learn:

Foundations of Agentic AI in GitHub

  • Define agentic AI in the SDLC and distinguish agents from assistants
  • Explain the agent lifecycle: plan, act, evaluate
  • Describe GitHub as the system of record and control plane
  • Identify responsibilities, risks, anti-patterns, and traceability needs
  • Apply the contributor model to agent-generated work

Designing Agent Architecture and SDLC Integration

  • Map agent responsibilities to the SDLC
  • Define inputs, outputs, and success criteria
  • Separate planning, reasoning, and execution
  • Implement PR governance with templates, checks, CODEOWNERS, rules, and environment gates
  • Build reliable workflows: outputs, contexts, triggers, and cross-job handoffs
  • Control and operate agents: observability, tools, MCP, secrets, hooks, and reliability

Tooling, MCP, and Agent Execution Environments

  • Understand how agents interact with GitHub APIs and workflows
  • Configure Model Context Protocol (MCP) servers, registries, and allow lists
  • Define execution context and boundaries (repository, branch, workflow scope)
  • Identify agent execution limits and protections
  • Enable autonomous actions such as branch and pull request creation

Memory, State, and Evaluation

  • Implement agent memory strategies: short-term, long-term, and external memory
  • 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

Multi-Agent Systems and Orchestration

  • 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

Governance, Guardrails, and Operations

  • Define risk-based autonomy and action boundaries
  • Enforce governance with GitHub controls (rulesets, checks, CODEOWNERS, environments)
  • Design human-in-the-loop workflows for high-risk actions
  • Control agent capabilities using least-privilege permissions
  • Make actions observable, traceable, and auditable
  • Maintain governance and operational reliability over time   

Developing in Agentic AI Systems

real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
fully customized

Tailored Outlines

Once-size-fits-all doesn't apply to training teams. That's where we come in!

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