Exam Code: GH-600
Exam Name: Developing in Agentic AI Systems
Certification Provider: Microsoft
Corresponding Certification: GitHub Administrator

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement guardrails and accountability	10–15%	- Define autonomy levels 1. Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards 2. Classify agent actions by operational, security, and compliance risk to right-size human interventions - Implement guardrails and human-in-the-loop workflows 1. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes 2. Identify the subset of actions that require human judgment 3. Scope permissions and execution contexts to enforce least-privilege access 4. Block actions that violate defined security, compliance, or Responsible AI policies 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk
Perform evaluation, error analysis, and tuning	15–20%	- Analyze agent failures and identify root causes 1. Identify failures by using logs, plans, traces, outputs, and workflow artifacts 2. Classify root causes, including reasoning errors, tool misuse, and context or environment issues - Tune agent behavior based on evaluation results 1. Refine memory usage 2. Refine tool usage and tool access 3. Revise instructions, workflows, or constraints - Define success criteria and evaluation signals for agent tasks 1. Identify qualitative and quantitative evaluation signals to evaluate agents 2. Align evaluation criteria with development intent 3. Specify expected outcomes and operational constraints for agent tasks 4. Generate evaluation signals by using automated scanning tools
Implement tool use and environment interaction	20–25%	- Configure MCP servers 1. Configure a GitHub remote MCP server 2. Add an MCP server as a tool to an agent 3. Configure MCP registries 4. Configure MCP allow lists - Integrate agents within development environments 1. Configure an agent to be invoked in a CI workflow 2. Configure an agent to handle environment-specific constraints 3. Enable an agent to perform autonomous actions, including creating branches and pull requests 4. Evaluate the execution context for an agent 5. Configure an agent's scope to a specific repository 6. Configure an agent to use branch-based scope - Select and configure agent tools 1. Identify required tools 2. Configure agent tools 3. Configure agent tool permissions - Operate agents with safe execution paths and robust error handling 1. Implement error handling 2. Implement traceability and accountability for agent actions 3. Implement retries 4. Implement escalation paths 5. Implement rollbacks
Prepare agent architecture and SDLC processes	15–20%	- Configure observability and control for autonomous agents 1. Plan and implement the degree of agent autonomy, including guardrails 2. Configure human intervention for autonomous agents without slowing delivery 3. Configure agents to produce inspectable artifacts within standard development tooling - Integrate agents into the software development lifecycle (SDLC) 1. Identify and mitigate common anti-patterns in agents 2. Identify steps for agents to perform 3. Define inputs, outputs, and success criteria for agents - Define boundaries between planning, reasoning, and action 1. Validate agent plans 2. Prevent agent action until the agent checks and approves 3. Configure agent planning to be distinct from agent execution 4. Configure an agent to output a structured plan
Orchestrate multi-agent coordination	15–20%	- Detect and respond to multi-agent failures and degraded behavior 1. Respond to degraded behavior or coordination across agents 2. Implement multi-agent recovery patterns, including rollback and human-in-the-loop 3. Identify failed, partial, or stalled agent executions - Operate and manage multi-agent workflows 1. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs 2. Configure agent isolation for parallel execution 3. Apply an orchestration pattern to coordinate multiple agents - Manage the lifecycle of agents within multi-agent workflows 1. Update, reconfigure, or replace agents without disrupting active workflows 2. Add agents to existing multi-agent workflows 3. Retire agents while preserving auditability and workflow continuity - Configure observability for multi-agent behavior by using logs, artifacts, and operational signals 1. Document key decisions, handoffs, and outcomes across agents 2. Configure multi-agent workflows to produce artifacts suitable for review and audit 3. Perform post-hoc analysis of multi-agent behavior
Manage memory, state, and execution	10–15%	- Persist agent state and manage context drift 1. Capture task progress and decisions as durable artifacts 2. Resume agent work without repeating steps or diverging from prior decisions 3. Detect and correct drift during extended agent execution - Implement agent memory strategies 1. Define memory expiration, pruning, and reset rules 2. Choose between short-term, long-term, and external memory 3. Scope agent memory to task-relevant information - Ensure continuity of agent memory and state across tools and environments 1. Prevent stale context 2. Share agent state 3. Prevent conflicting context

Microsoft Developing in Agentic AI Systems Sample Questions:

Question #1

You are about to start a complex refactoring task in the GitHub Copilot CLI.
Before Copilot makes any changes, you need to review and agree on the approach.
What should you do first?

A. From the Copilot CLI, switch to plan mode.
B. Start the Copilot CLI and specify the --agent=Task parameter.
C. From the Copilot CLI, run the /compact command.
D. Start the Copilot CLI and specify the --allow-all parameter.

Reveal Solution Discussion 0

Correct Answer: A 🗳️