Tool policy
Medium risk- Status
- Informal usage
- Recommended action
- Define approved tools, usage boundaries, and ownership expectations.
Sample report
Sample AI Coding Agent Audit Report
A preview of the type of findings, risk notes, and rollout recommendations SNS AI Labs provides in a fixed-scope AI Coding Agent Audit.
Important: This is a sample report format using illustrative findings. It does not describe a real client, real repository, or real audit. The examples show the expected report structure, not a promised finding set.
Buyer-facing preview
Engineering teams are adopting Cursor, Copilot, Claude Code, Codex, Gemini CLI, OpenHands, and similar tools quickly. The audit helps leaders understand whether these tools are improving delivery or creating hidden review, test, security, and maintainability risk.
Review load
Test gaps
Sensitive code paths
Measurement
Executive summary
Sample executive summary
This section shows how SNS would summarize the state of AI coding adoption for leadership. The bullets below are illustrative sample findings.
Sample finding: AI coding tools are being used by developers, but usage is inconsistent across repositories.
Sample finding: Repo-specific agent instructions are missing or incomplete.
Sample finding: Review effort is increasing because AI-assisted PRs are larger and less predictable.
Sample finding: CI exists, but regression coverage is not strong enough to absorb higher code volume.
Sample finding: Security-sensitive areas need clearer boundaries before wider AI coding adoption.
Sample finding: A 30-day rollout plan should focus on instructions, gates, measurement, and developer workflow discipline.
Scorecard
Sample readiness scorecard
A scorecard gives leadership a compact view of current adoption state, risk, and the next control to implement.
Repo instructions
Medium risk- Status
- Missing or inconsistent
- Recommended action
- Add AGENTS.md / CLAUDE.md / .cursor/rules / Codex instructions per repository.
Review process
High risk- Status
- AI-assisted PRs not clearly identified
- Recommended action
- Add PR checklist, risk notes, and scope limits for AI-assisted changes.
Test readiness
High risk- Status
- Basic CI exists, regression depth varies
- Recommended action
- Define required test commands and expand regression coverage.
Security boundaries
High risk- Status
- Sensitive paths not clearly marked
- Recommended action
- Define no-agent zones for auth, billing, infra, secrets, migrations, and security-critical code.
Measurement
Medium risk- Status
- No AI adoption baseline
- Recommended action
- Track cycle time, review time, PR size, test failures, reverts, defects, and tool cost.
Findings
Sample findings
Each finding connects an observation to the operational reason it matters and the control that would reduce risk.
1
Repo-specific instructions are missing
Medium risk
- Why it matters
- Agents need explicit knowledge of architecture, test commands, coding conventions, forbidden paths, and review expectations. Without this, output quality depends too heavily on individual prompting.
- Recommended control
- Add repository-level instructions for each approved agent workflow.
2
AI-assisted PRs are larger than the review process can comfortably absorb
High risk
- Why it matters
- Higher code volume can increase reviewer load, slow merges, and hide maintainability issues.
- Recommended control
- Limit PR scope, require change summaries, include test evidence, and add AI-assisted PR review checklist items.
3
No clear boundary exists for sensitive code paths
High risk
- Why it matters
- Authentication, billing, secrets, migrations, infrastructure, and security-sensitive code should not be modified casually by agents.
- Recommended control
- Define no-agent zones and require explicit human approval for sensitive paths.
4
Measurement is not defined
Medium risk
- Why it matters
- Without a baseline, leadership cannot tell whether AI coding tools improve delivery or only increase activity.
- Recommended control
- Create a 30-day measurement dashboard covering cycle time, review time, test failures, PR size, reverts, defect signals, developer experience, and tool cost.
5
Test commands are not standardized for agents
High risk
- Why it matters
- Agents should know exactly which lint, typecheck, unit test, integration test, and build commands must pass before a change is considered ready.
- Recommended control
- Define required validation commands per repository and include them in agent instructions.
Rollout
Sample 30-day rollout plan
The audit ends with a practical plan that can be executed by engineering leadership, security, and team leads.
Week 1
Baseline current usage
Inventory tools, repositories, CI, test commands, review bottlenecks, and security-sensitive areas.
Week 2
Add control files and review rules
Create repo-specific agent instructions, PR checklist updates, no-agent zones, and secret-handling guidance.
Week 3
Improve validation and measurement
Add or standardize lint, typecheck, test, and build commands, define regression expectations, and start tracking review and test signals.
Week 4
Pilot controlled usage
Run a controlled pilot with selected developers and repositories. Review PR quality, test pass rate, review time, and developer feedback.
Client inputs
What we need to run the audit
The audit can be done through interviews, workflow review, sanitized examples, CI/test review, and approved repository inspection where appropriate.
Team size
Tools currently used
Number of repositories
Main concerns: productivity, review load, security, maintainability, testing, rollout, or tool selection
CI/test workflow overview
Optional sanitized examples of AI-assisted changes
Source-code access model: none, limited, sanitized, or approved repository access
Want this applied to your team?