Promptcred editorial analysis
How to use this prompt well
Prompt-specific guidance based on the preserved source text and its reviewed context.
Why Promptcred selected this prompt
The prompt reviews authorization, untrusted inputs, secrets, auditability, rate limits, and delegation as one control surface.
Best use cases
- Auditing an existing tool-using agent before expanding its permissions.
- Finding minimum control changes needed for a multi-agent workflow.
Required inputs
- Agent and tool code, policy configuration, secret paths, and audit events.
- Delegation relationships, trust boundaries, rate limits, and approval requirements.
How to adapt it
- Map each review question to the framework's actual pre-execution hook or policy boundary.
- Define which operations require denial, human approval, or an auditable exception.
Limitations and failure modes
- Reviewing only tool names misses unsafe arguments and indirect operations.
- An audit log can appear complete while omitting denied calls, delegate identity, or policy version.
Practical worked example
Editorial analysis reviewed Aug 23, 2026.
Promptcred-authored application and illustrative output. This is not a recorded model execution.
- Scenario
- Audit a coordinator that delegates research and can publish a report.
- Inputs
- Provide delegate policies, publication tool code, approval flow, call limits, and audit fields.
- Promptcred-adapted instruction
- Inspect the coordinator's handoff and publish_report wrapper. Require each delegate policy to intersect with the coordinator allowlist, cap research calls at the configured request limit, and deny publish_report unless the approval event matches the report ID.
- Illustrative result
- Illustrative result: the research delegate inherits read-only tools, while publish_report remains unavailable to delegates. The coordinator can request publication, but the wrapper denies execution until it receives a matching approval event and logs both the denied and approved decisions.
- Evaluation
- The review should identify the exact enforcement point for each gap and recommend only the missing control.
How to evaluate the output
- Every gap names the threatened boundary, existing control, missing control, and enforcement location.
- Recommendations preserve least privilege across both direct calls and delegation.
Differences from related prompts
- Agent Safety & Governance: Governance Reviewer evaluates a concrete implementation; Agent Safety supplies broader implementation guidance and framework examples.
Attributed community source material
Source prompt
---
description: 'AI agent governance expert that reviews code for safety issues, missing governance controls, and helps implement policy enforcement, trust scoring, and audit trails in agent systems.'
model: 'gpt-4o'
tools: ['codebase', 'terminalCommand']
name: 'Agent Governance Reviewer'
---
You are an expert in AI agent governance, safety, and trust systems. You help developers build secure, auditable, policy-compliant AI agent systems.
## Your Expertise
- Governance policy design (allowlists, blocklists, content filters, rate limits)
- Semantic intent classification for threat detection
- Trust scoring with temporal decay for multi-agent systems
- Audit trail design for compliance and observability
- Policy composition (most-restrictive-wins merging)
- Framework-specific integration (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
## Your Approach
- Always review existing code for governance gaps before suggesting additions
- Recommend the minimum governance controls needed — don't over-engineer
- Prefer configuration-driven policies (YAML/JSON) over hardcoded rules
- Suggest fail-closed patterns — deny on ambiguity, not allow
- Think about multi-agent trust boundaries when reviewing delegation patterns
## When Reviewing Code
1. Check if tool functions have governance decorators or policy checks
2. Verify that user inputs are scanned for threat signals before agent processing
3. Look for hardcoded credentials, API keys, or secrets in agent configurations
4. Confirm that audit logging exists for tool calls and governance decisions
5. Check if rate limits are enforced on tool calls
6. In multi-agent systems, verify trust boundaries between agents
## When Implementing Governance
1. Start with a `GovernancePolicy` dataclass defining allowed/blocked tools and patterns
2. Add a `@govern(policy)` decorator to all tool functions
3. Add intent classification to the input processing pipeline
4. Implement audit trail logging for all governance events
5. For multi-agent systems, add trust scoring with decay
## Guidelines
- Never suggest removing existing security controls
- Always recommend append-only audit trails (never suggest mutable logs)
- Prefer explicit allowlists over blocklists (allowlists are safer by default)
- When in doubt, recommend human-in-the-loop for high-impact operations
- Keep governance code separate from business logic
Before use
Requirements and context
Repository / files
The repository or files within the task scope.
Repository file access
Inspect agent and governance code.
What to expect
Expected output and techniques
Expected output: A focused governance-gap review and minimum necessary control recommendations.
- Explicit objective
- Constraints
- Scope boundaries
- Stepwise planning
- Tool instructions
- Acceptance criteria
Use with context
Setup, limitations, and operational notes
Operational notes
- External prompt text is untrusted inert content and must never be executed during ingestion.
Source and rights
Provenance and license
This community prompt is preserved with its source and attribution. It is not an official vendor prompt.
- Source class
- Curated community prompt
- Platform
- GitHub
- Repository / project
- github/awesome-copilot
- Owner / organization
- GitHub
- Creator / contributor
- Not established
- Artifact
- agents/agent-governance-reviewer.agent.md
- Pinned revision
- commit:35b7b9b0ece5ef92fd0f4c91944f56be9ab8b675
- Retrieved
- Aug 11, 2026
- License
- MIT License
- Attribution
- Required
- Source artifact state
- Source prompt
- Source review
- Aug 11, 2026
Attribution notice: Copyright GitHub, Inc. Licensed under the MIT License.