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GitHub Certified: Agentic AI Developer Cheat Sheet

Practice this exam

Prepare Agent Architecture and SDLC Processes

  • 15-20% of exam
  • Agent Task Design Planning vs Execution Observability and Control Plan Validation

Implement Tool Use and Environment Interaction

  • 20-25% of exam
  • Tool Scope MCP Configuration Development Environments Safe Execution

Manage Memory, State, and Execution

  • 10-15% of exam
  • Memory Strategy Durable State Context Drift State Continuity

Perform Evaluation, Error Analysis, and Tuning

  • 15-20% of exam
  • Evaluation Signals Failure Evidence Root Cause Classes Behavior Tuning

Orchestrate Multi-Agent Coordination

  • 15-20% of exam
  • Agent Coordination Conflict Control Multi-Agent Recovery Agent Lifecycle

Implement Guardrails and Accountability

  • 10-15% of exam
  • Risk Classification Autonomy Levels Least Privilege Authorization Gates

Quick Facts

  • Exam code GH-600
  • Exam title Developing in Agentic AI Systems
  • Credential GitHub Certified: Agentic AI Developer
  • Maintainer GitHub
  • Exam provider Microsoft
  • Level Intermediate
  • Exam time 120 minutes
  • Question count Not published
  • Passing score 700/1000 scaled
  • Domains Six
  • Language English
  • Delivery Proctored through Pearson VUE
  • Components Interactive items may appear
  • First retake Wait 24 hours
  • Later retakes Wait 14 days
  • Attempt limit Five within 12 months
  • Credential validity Not published
  • In-exam Learn Unavailable for GitHub exams
  • Blueprint update May 13, 2026

IOS Task Contract AR

  • Inputs, outputs, success before execution
  • Inputs Outputs Success criteria

Credential vs Exam AR

Credential

  • Agentic AI Developer
  • Maintained by GitHub
  • Earned after passing

Exam

  • GH-600
  • Developing in Agentic AI Systems
  • Provided by Microsoft
  • Credential name differs from exam title

Architecture and Control Picker AR

  • Task is underspecified → Define inputs, outputs, success ( Set boundaries )
  • Actions begin before review → Separate planning from execution ( Add action gate )
  • Intent is hard to inspect → Emit structured plan ( Make steps reviewable )
  • Plan exceeds scope → Reject or revise plan ( Validate first )
  • Action carries material risk → Require human approval ( Right-size intervention )
  • Work runs autonomously → Produce inspectable artifacts ( Enable oversight )
  • Operator may need control → Configure intervention path ( Avoid delivery blockage )
  • Action is low-risk reversible → Allow proportionate autonomy ( Minimize needless approvals )

Agent Task Design AR

  • SDLC integration Places agents within development workflows
  • Task boundary Limits agent responsibilities
  • Input Information available before work
  • Output Required deliverable or artifact
  • Success criterion Observable completion condition
  • Operational constraint Boundary on permitted execution
  • Agent anti-pattern Recurring harmful design choice
  • Planning Defines intended action sequence
  • Reasoning Selects and justifies approach
  • Execution Performs permitted actions
  • Structured plan Reviewable ordered action proposal
  • Plan validation Checks scope, safety, and feasibility

Plan Control Cycle AR

  • Plan, validate, authorize, execute, trace
  • Plan Validate Authorize Execute Trace

GH-600 vs GH-300 AR

GH-600

  • Agentic AI systems
  • Multi-agent orchestration
  • Production guardrails

GH-300

  • GitHub Copilot certification
  • Separate exam blueprint
  • Not this exam
  • Agent operations versus Copilot certification

Observability and Control AR

  • Inspectable artifact Reviewable evidence of agent work
  • Log Timestamped event record
  • Trace Linked execution path
  • Workflow output Result produced by automation
  • Human intervention Operator changes or halts behavior
  • Action gate Blocks execution pending approval
  • Autonomy Permitted independent action
  • Control plane GitHub governs agent operations
  • System of record Authoritative durable workflow evidence
  • Review checkpoint Human validates before continuation

GH-600 vs AI-102 AR

GH-600

  • GitHub control plane
  • Agentic SDLC workflows
  • GitHub credential

AI-102

  • Azure AI solutions
  • Different skills measured
  • Microsoft Azure credential
  • GitHub agents versus Azure AI

Planning vs Execution AR

Planning

  • Proposes intended actions
  • Produces structured plan
  • Validated before action

Execution

  • Performs permitted actions
  • Uses configured tools
  • Produces traceable artifacts
  • Review intent before effects

MCP Allowlist vs Registry TO

Allowlist

  • Controls permitted servers
  • Policy enforcement
  • Reduces unsafe access

Registry

  • Catalogs MCP servers
  • Supports discovery
  • Stores server metadata
  • Permission versus discovery

Tool and Execution Picker TO

  • Agent lacks required capability → Select and configure tool ( Grant minimum access )
  • Agent needs external context → Configure MCP server ( Expose required capability )
  • MCP choices need control → Apply allowlist policy ( Permit approved servers )
  • Work targets one repository → Apply repository scope ( Limit blast radius )
  • Changes need isolation → Apply branch scope ( Review through pull request )
  • Automation should invoke agent → Integrate CI workflow ( Check execution context )
  • Failure appears transient → Retry with limits ( Record each attempt )
  • Partial changes are unsafe → Roll back state ( Restore known-good point )
  • Recovery remains uncertain → Escalate with evidence ( Preserve traceability )

Tool Scope TO

  • Tool selection Matches capability to task
  • Tool configuration Defines tool behavior and connection
  • Tool permission Controls allowed tool actions
  • Execution context Environment surrounding agent work
  • Repository scope Limits work to one repository
  • Branch scope Confines changes to assigned branch
  • CI invocation Starts agent from workflow
  • Branch creation Isolates proposed repository changes
  • Pull request creation Presents changes for review
  • Environment constraint Limit specific to execution environment
  • Least privilege Minimum access required

Retry vs Rollback TO

Retry

  • Attempts operation again
  • Fits transient failure
  • Needs limits and trace

Rollback

  • Reverses partial effects
  • Restores known-good state
  • Fits unsafe residue
  • Repeat versus restore

MCP Configuration TO

  • MCP Standard connecting models, tools, data
  • MCP server Exposes tools or context
  • GitHub remote MCP Remote GitHub tool integration
  • MCP registry Catalogs discoverable MCP servers
  • MCP allowlist Permits approved MCP servers
  • Managed settings Enforces MCP client configuration
  • Tool exposure Makes server capability available
  • Server permission Scopes accessible server actions
  • Approved server Server authorized by policy
  • Registry entry Metadata supporting server discovery

Safe Execution TO

  • Error handling Controlled response to failure
  • Retry Repeats eligible failed operation
  • Rollback Restores known-good state
  • Escalation Hands unresolved issue to human
  • Traceability Links actions to evidence
  • Checkpoint Recoverable progress boundary
  • Failure signal Evidence execution degraded
  • Partial failure Only some work completes
  • Safe path Controlled execution and recovery route
  • Accountability Actions remain attributable and reviewable

Memory vs State ME

Memory

  • Retains relevant information
  • Needs scope and pruning
  • May be short-term

State

  • Tracks execution progress
  • Persists decisions and status
  • Enables accurate resume
  • Knowledge versus workflow position

Memory and Evaluation Picker ME

  • Context serves current task → Use short-term memory ( Keep task-relevant )
  • Work must resume later → Persist durable artifacts ( Record progress and decisions )
  • State crosses tools → Share explicit state ( Prevent conflicting context )
  • Context may be stale → Refresh and validate ( Check current environment )
  • Execution drifts from decisions → Detect and correct drift ( Use decision record )
  • Success is ambiguous → Define evaluation signals ( Align development intent )
  • Machine checks are available → Generate automated signals ( Use scanning tools )
  • Agent output fails → Inspect logs, plans, traces ( Classify root cause )
  • Reasoning caused failure → Revise instructions or constraints ( Retest behavior )
  • Tool use caused failure → Refine tool usage ( Adjust access if needed )

Memory Strategy ME

  • Short-term memory Current task working context
  • Long-term memory Persists beyond current execution
  • External memory State outside active context
  • Task relevance Excludes unrelated stored information
  • Expiration Removes memory after defined time
  • Pruning Removes low-value context
  • Reset Clears selected memory or state
  • Repository fact Repository-scoped shared knowledge
  • User preference User-scoped interaction preference
  • Memory scope Defines permitted information boundary

Short-Term vs Long-Term Memory ME

Short-term

  • Current task context
  • Temporary
  • Prune aggressively

Long-term

  • Persists across execution
  • Needs expiration rules
  • Validate before reuse
  • Working context versus persistence

Durable State ME

  • Durable artifact Persistent progress evidence
  • Decision record Preserves why choice was made
  • Progress record Tracks completed and remaining work
  • State continuity Preserves work across tools
  • Resume Continues without repeating steps
  • Context drift Execution diverges from prior decisions
  • Stale context Outdated task or environment state
  • Conflicting context Shared information disagrees
  • Shared state Coordinated cross-agent information
  • State handoff Transfers progress with evidence

Failure Roots EV

  • Reasoning, tool, context, environment
  • Reasoning error Tool misuse Context issue Environment issue

Reasoning Error vs Tool Misuse EV

Reasoning error

  • Plan or logic flawed
  • Tool may work correctly
  • Tune instructions or constraints

Tool misuse

  • Tool applied incorrectly
  • Parameters or sequence wrong
  • Tune usage or access
  • Wrong thinking versus wrong operation

Evaluation Signals EV

  • Expected outcome Intended task result
  • Qualitative signal Judgment-based quality evidence
  • Quantitative signal Numeric performance evidence
  • Automated scan Machine-generated evaluation signal
  • Development intent Team's actual delivery objective
  • Acceptance criterion Measurable pass condition
  • Evaluation baseline Reference for result comparison
  • Test result Behavior correctness signal
  • Security finding Automated risk signal
  • Operational signal Evidence of runtime behavior

Evaluation vs Observability EV

Evaluation

  • Judges task success
  • Uses aligned signals
  • Drives tuning

Observability

  • Exposes agent behavior
  • Produces logs and artifacts
  • Enables diagnosis
  • Judge outcome versus expose behavior

Failure Evidence and Tuning EV

  • Reasoning error Flawed analysis or plan
  • Tool misuse Wrong tool, parameters, or sequence
  • Context issue Missing, conflicting, or stale information
  • Environment issue Incompatible execution conditions
  • Root cause Underlying failure mechanism
  • Plans Evidence of intended actions
  • Outputs Evidence of resulting artifacts
  • Instruction tuning Revises workflows or constraints
  • Memory tuning Refines stored context usage
  • Tool tuning Refines usage and access

Multi-Agent Loop OR

  • Isolate, observe, hand off, recover
  • Isolate Observe Handoff Recover

Orchestration and Guardrail Picker OR

  • Subtasks are independent → Run isolated parallel agents ( Assign clear ownership )
  • Subtasks have dependencies → Sequence documented handoffs ( Transfer state explicitly )
  • Agents edit overlapping scope → Serialize or reassign ( Prevent change conflicts )
  • Agents duplicate effort → Clarify ownership boundaries ( Stop redundant work )
  • Outputs contradict → Reconcile before integration ( Record final decision )
  • Execution stalls or degrades → Activate recovery pattern ( Use artifacts and signals )
  • Agent lifecycle changes → Preserve audit and continuity ( Add, replace, or retire )
  • Action violates policy → Block action ( Do not merely warn )
  • Change is irreversible → Require explicit authorization ( Use controlled path )

Agent Coordination OR

  • Orchestration pattern Coordinates multiple agent roles
  • Agent isolation Separates parallel execution contexts
  • Parallel execution Agents work concurrently
  • Overlapping changes Agents edit same scope
  • Duplicated effort Agents repeat same task
  • Contradictory outputs Agent conclusions cannot coexist
  • Handoff Transfers work and evidence
  • Key decision Recorded coordination choice
  • Post-hoc analysis Reviews completed agent behavior
  • Audit artifact Supports later workflow review

Multi-Agent Recovery OR

  • Failed execution Agent cannot complete work
  • Partial execution Agent completes incomplete subset
  • Stalled execution Agent stops making progress
  • Degraded behavior Coordination quality worsens
  • Recovery pattern Restores coordinated workflow progress
  • Human-in-the-loop Human judgment enters workflow
  • Add agent Introduces new workflow participant
  • Reconfigure agent Changes role without disruption
  • Replace agent Substitutes workflow participant
  • Retire agent Removes agent while preserving continuity

Risk Controls Autonomy GU

  • Higher risk means tighter control
  • Classify risk Set autonomy Gate sensitive action

Autonomy vs Unrestricted Access GU

Autonomy

  • Risk-adjusted authority
  • Defined boundaries
  • Inspectable actions

Unrestricted access

  • Excessive permissions
  • Undefined action scope
  • Avoid this design
  • Independence still needs boundaries

Risk and Autonomy GU

  • Risk classification Groups actions by consequences
  • Operational risk Threatens workflow reliability
  • Security risk Threatens systems, data, or access
  • Compliance risk Threatens regulatory or policy obligations
  • Autonomy level Determines independent authority
  • Human judgment Manual decision required
  • Policy block Prevents forbidden action
  • Explicit authorization Deliberate approval to act
  • Irreversible change Cannot readily be undone
  • Compliance-sensitive change Requires controlled execution path
  • Responsible AI Organizational AI behavior standards
  • Low-value approval Does not materially reduce risk

Common Traps

Scaled Score Is Not Percentage

  • Passing score is 700/1000 ≠ Raw 70% is not guaranteed

Time Is Not Question Count

  • Exam time is 120 minutes ≠ Question count is unpublished

Credential Is Not Exam Title

  • Credential: Agentic AI Developer ≠ Exam: Developing in Agentic AI Systems

GH-600 Is Not GH-300

  • GH-600 covers agentic systems ≠ GH-300 is Copilot certification

GH-600 Is Not AI-102

  • GH-600 uses GitHub control plane ≠ AI-102 covers Azure AI solutions

Plan Is Not Execution

  • Plan proposes reviewable actions ≠ Execution creates effects

Memory Is Not Infinite Context

  • Memory needs scope and pruning ≠ Irrelevant context increases drift

Retry Is Not Infinite Loop

  • Retry eligible transient failures ≠ Escalate persistent failures

Parallel Is Not Shared Workspace

  • Parallel work needs isolation ≠ Shared scope creates conflicts

Artifact Is Not Chat History

  • Artifact supports review and audit ≠ Conversation alone may lack structure

Least Privilege Is Not No Tools

  • Grant capabilities required for task ≠ Deny unnecessary access

Human Control Is Not Blanket Approval

  • Gate materially risky actions ≠ Minimize low-value approvals

Last Minute

  • 1. GH-600 is Agentic AI Developer
  • 2. Exam title differs from credential
  • 3. Six domain ranges match blueprint
  • 4. Questions are not officially published
  • 5. 120 minutes is official exam time
  • 6. 700 is a scaled score
  • 7. Define inputs, outputs, success criteria
  • 8. Separate planning from execution
  • 9. Validate plans before agent action
  • 10. Scope tools by least privilege
  • 11. Know MCP server, registry, allowlist
  • 12. Record retries, rollbacks, escalations
  • 13. Persist progress and decisions
  • 14. Detect stale context and drift
  • 15. Align signals with development intent
  • 16. Classify each failure root cause
  • 17. Isolate parallel agent execution
  • 18. Document decisions and handoffs
  • 19. Gate irreversible sensitive changes
  • 20. Do not expect in-exam Learn

GitHub Certified: Agentic AI Developer (GH-600)

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