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Microsoft GH-600 Exam Syllabus Topics:

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

Microsoft Developing in Agentic AI Systems Sample Questions:

Question #1

You have a GitHub repository that uses the GitHub Copilot coding agent to run agent plans for assigned issues.
You discover that some issues contain ambiguous implementation steps.
You need to reduce the likelihood that Copilot executes plans based on ambiguous implementation steps.
What should you do?

A. Add acceptance criteria and directions to the issues before assigning the issues to the agent.
B. Add a copilot-setup-steps.yml file to preinstall dependencies in the agent environment.
C. Mention @copilot in a pull request comment to request changes after the plan runs.
D. Add comments to the issues after assigning the issues to the agent.

Reveal Solution Discussion 0

Correct Answer: A 🗳️