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Purpose of this document
This study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.
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About the exam
Some exams are localized into other languages, and those are updated approximately eight weeks after the English version is updated. While Microsoft makes every effort to update localized versions as noted, there may be times when the localized versions of an exam are not updated on this schedule. Other available languages are listed in the Schedule Exam section of the Exam Details webpage. If the exam isn't available in your preferred language, you can request an additional 30 minutes to complete the exam.
Note
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
Note
Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.
Skills measured
Audience profile
You should have subject matter expertise in operating, integrating, supervising, and governing AI agents inside production-grade SDLC workflows and development environments, ensuring reliability, safety, and velocity using GitHub as the system of record and control plane.
Your responsibilities for this role include:
Operating agent workflows inside the SDLC
Supervising autonomous behavior with GitHub controls
Evaluating and tuning agent outputs using scans and artifacts
Configuring custom agents
Coordinating multi-agent execution safely
You work closely with architects, platform engineers, DevOps engineers, application developers, product managers, and security engineers to develop, deploy, operate, and manage agents that operate within the GitHub platform.
You should have experience with the software development lifecycle (SDLC), workflows in GitHub and controls, and code quality, security, and review practices. You should also have experience with coding agents including GitHub Copilot, MCP servers and agent customization such as custom instructions, custom agents, tools, and Copilot setup steps.
Skills at a glance
Prepare agent architecture and SDLC processes (15–20%)
Implement tool use and environment interaction (20–25%)
Manage memory, state, and execution (10–15%)
Perform evaluation, error analysis, and tuning (15–20%)
Orchestrate multi-agent coordination (15–20%)
Implement guardrails and accountability (10–15%)
Prepare agent architecture and SDLC processes (15–20%)
Integrate agents into the software development lifecycle (SDLC)
Identify steps for agents to perform
Identify and mitigate common anti-patterns in agents
Define inputs, outputs, and success criteria for agents
Define boundaries between planning, reasoning, and action
Configure agent planning to be distinct from agent execution
Configure an agent to output a structured plan
Validate agent plans
Prevent agent action until the agent checked and approved
Configure observability and control for autonomous agents
Plan and implement the degree of agent autonomy, including guardrails
Configure agent to produce inspectable artifacts within standard development tooling
Configure human intervention for autonomous agents without slowing delivery
Implement tool use and environment interaction (20–25%)
Select and configure agent tools
Identify required tools
Configure agent tools
Configure agent tool permissions
Configure MCP servers
Add an MCP server as a tool to an agent
Configure a GitHub remote MCP server
Configure the MCP registries
Configure MCP allow lists
Integrate agents within development environments
Evaluate the execution context for an agent
Configure an agent's scope to a specific repository
Configure an agent to be invoked in a CI workflow
Configure an agent to use branch-based scope
Enable an agent to perform autonomous actions, including creating branches and pull requests
Configure an agent to handle environment-specific constraints
Operate agents with safe execution paths and robust error handling
Implement error handling
Implement retries
Implement rollbacks
Implement escalation paths
Implement traceability and accountability for agent actions
Manage memory, state, and execution (10–15%)
Implement agent memory strategies
Choose between short-term, long-term, and external memory
Scope agent memory to task-relevant information
Define memory expiration, pruning, and reset rules
Persist agent state and manage context drift
Capture task progress and decisions as durable artifacts
Resume agent work without repeating steps or diverging from prior decisions
Detect and correct drift during extended agent execution
Ensure continuity of agent memory and state across tools and environments
Share agent state
Prevent conflicting context
Prevent stale context
Perform evaluation, error analysis, and tuning (15–20%)
Define success criteria and evaluation signals for agent tasks
Specify expected outcomes and operational constraints for agent tasks
Identify qualitative and quantitative evaluation signals to evaluate agents
Align evaluation criteria with development intent
Generate evaluation signals by using automated scanning tools
Analyze agent failures and identify root causes
Identify failures by using logs, plans, traces, outputs, and workflow artifacts
Classify root causes, including reasoning errors, tool misuse, and context or environment issues
Tune agent behavior based on evaluation results
Revise instructions, workflows, or constraints
Refine memory usage
Refine tool usage and tool access
Orchestrate multi-agent coordination (15–20%)
Operate and manage multi-agent workflows
Apply an orchestration pattern to coordinate multiple agents
Configure agent isolation for parallel execution
Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs
Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
Configure multi-agent workflows to produce artifacts suitable for review and audit
Document key decisions, handoffs, and outcomes across agents
Perform post-hoc analysis of multi-agent behavior
Detect and respond to multi-agent failures and degraded behavior
Identify failed, partial, or stalled agent executions
Respond to degraded behavior or coordination across agents
Implement multi-agent recovery patterns, including rollback and human-in-the-loop
Manage the lifecycle of agents within multi-agent workflows
Add agents to existing multi-agent workflows
Update, reconfigure, or replace agents without disrupting active workflows
Retire agents while preserving auditability and workflow continuity
Implement guardrails and accountability (10–15%)
Define autonomy levels
Classify agent actions by operational, security, and compliance risk to right-size human interventions
Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards
Implement guardrails and human-in-the-loop workflows
Identify the subset of actions that require human judgment
Block actions that violate defined security, compliance, or Responsible AI policies
Scope permissions and execution contexts to enforce least-privilege access
Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes
Preserve execution velocity by minimizing approvals that do not materially reduce risk
Study resources
We recommend that you train and get hands-on experience before you take the exam. We offer self-study options and classroom training as well as links to documentation, community sites, and videos.
| Study resources | Links to learning and documentation |
|---|---|
| Get trained | Choose from self-paced learning paths and modules or take an instructor-led course on Microsoft Learn – Foundations of Agentic AI in GitHub, Designing Agent Architecture and SDLC Integration, Tooling, MCP, and Agent Execution Environments |
| Find documentation | Prepare agent architecture and SDLC processes Implement Tool Use and Environment Interaction Manage Memory, State, and Execution Perform Evaluation, Error Analysis, and Tuning Orchestrate Multi-Agent Coordination Implement Guardrails and Accountability |
| Ask a question | GitHub Community Discussions |
| Get community support | GitHub Blog |
| Follow GitHub | Twitter |
| Find a video | YouTube |