Is AI actually improving engineering delivery?
We look at workflow usage, review effort, cycle signals, test failures, PR size, and developer friction instead of relying on tool adoption alone.
AI Coding Agent Audit for CTOs
Are your developers using Cursor, Copilot, Claude Code, Codex, or Gemini without knowing if it is helping or hurting?
SNS AI Labs audits your engineering workflow and gives you a practical AI coding-agent adoption report: usage patterns, review bottlenecks, test gaps, prompt practices, security concerns, maintainability risks, and a 30-day rollout plan.
Review load
Test readiness
Security controls
Measurement
Fixed-scope review
Interviews, workflow review, sanitized examples, CI/test review, and limited repo inspection where approved.
Buyer pain
AI coding tools are already in your engineering system. Are they controlled?
The issue is no longer whether AI can generate code. The issue is whether the engineering system can absorb AI-generated code without increasing review load, security risk, test debt, or maintainability problems.
Developers use different AI tools without a common policy.
Pull requests become larger or harder to review.
AI-generated code enters the repo without clear ownership.
Test coverage does not keep up with code volume.
Secrets, dependency changes, and security-sensitive files are not protected.
Management cannot tell whether AI is improving delivery or adding hidden cost.
Teams do not have repo-specific agent instructions.
CI and review gates are not ready for AI-assisted development.
Audit focus
The audit answers three questions
Is it increasing review, security, or maintainability risk?
We inspect the controls around sensitive files, dependencies, secrets, generated code ownership, review policy, and regression coverage.
What controls should be in place before scaling?
The report gives practical next steps for repo instructions, CI gates, review rules, secure prompting, measurement, and rollout discipline.
Deliverables
What the audit includes
The output is a practical adoption report for engineering leaders, not a generic tool comparison.
Current AI coding tool usage assessment
Repo and CI/test maturity review
PR review bottleneck analysis
AI-generated-code risk checklist
Tool recommendation across Cursor, Copilot, Codex, Claude Code, Gemini CLI, OpenHands, or similar tools
Repo-specific agent instruction recommendations
Prompt and workflow standards
Regression-suite improvement plan
Security and secret-handling review
Measurement approach for cycle time, review effort, test failures, revert rate, and defect signals
30-day adoption roadmap
Management summary
Sample audit output
The report turns observations into specific controls
A typical audit report highlights concrete findings, risk level, and the controls needed to make AI-assisted development easier to review and govern.
- Area
- Repo instructions
- Finding
- Agent rules are missing or inconsistent across repositories.
- Risk
- Medium
- Recommendation
- Add repo-specific instructions such as AGENTS.md, CLAUDE.md, .cursor/rules, or Codex instructions with architecture notes, test commands, coding standards, and no-agent zones.
- Area
- Review load
- Finding
- AI-assisted pull requests are larger and harder for reviewers to assess.
- Risk
- High
- Recommendation
- Limit PR scope, require change summaries, add risk notes, and use a reviewer checklist.
- Area
- Secrets and sensitive files
- Finding
- No clear boundaries exist for auth, billing, infra, migrations, or security-critical files.
- Risk
- High
- Recommendation
- Define no-agent zones, require human approval, and enforce secret scanning.
- Area
- Test readiness
- Finding
- Code is produced faster than the regression suite can validate it.
- Risk
- High
- Recommendation
- Document lint, typecheck, unit, integration, and build commands for every repo.
- Area
- Measurement
- Finding
- There is no baseline for cycle time, review effort, test failures, reverts, defects, or cost.
- Risk
- Medium
- Recommendation
- Create a 30-day dashboard for adoption quality and engineering impact.
- Area
- Tool policy
- Finding
- Teams use different tools and prompts without a common workflow policy.
- Risk
- Medium
- Recommendation
- Set a vendor-neutral policy for tools, prompts, ownership, review, and security use.
Pricing
Fixed-scope audit
Final scope and pricing depend on team size, number of repositories, codebase complexity, access constraints, and review depth.
Starter Audit
Starting at $800 / ₹75,000
- For teams up to 10 developers
- 5 business days
- Focused workflow and readiness review
Standard Audit
Starting at $1,600 / ₹1,50,000
- For teams up to 30 developers
- 7 business days
- Deeper repo, CI, policy, and rollout review
Vendor-neutral review
SNS does not require clients to buy a specific AI coding tool and does not resell tool licenses. The audit focuses on workflow quality, risk controls, and fit for your engineering environment.
Remote delivery
The audit can be delivered remotely for India-based, global, and distributed teams through interviews, workflow review, CI/test review, sanitized examples, and limited repository inspection where approved.
Implementation support
After the audit: AI Coding Agent Control Setup
If the team wants implementation support after the audit, SNS can help set up the controls recommended in the report. Final scope is defined after the audit.
Implementation Setup
Starting at $2,200 / ₹2,00,000
Monthly Advisory
Starting at $550 / ₹50,000 per month
Tool setup policy
AGENTS.md / CLAUDE.md / .cursor/rules / Codex instruction files
Repo-specific agent constraints
PR checklist and review gates
Test, lint, typecheck, and build enforcement
CI/CD integration
AI-generated code review policy
Security scan and dependency rules
Regression suite improvements
Developer onboarding session
Before/after measurement baseline
Best fit
- SaaS startups with 5-50 developers
- IT service companies
- Product engineering teams
- CTOs evaluating Cursor, Copilot, Claude Code, Codex, Gemini CLI, or OpenHands
- Teams with legacy codebases
- Security-sensitive teams where AI coding is already happening informally
- Teams that want measurable adoption rather than random tool usage
Not a fit if
- You only need a basic AI tool license recommendation.
- You do not want to change review, test, or CI practices.
- You expect AI coding tools to improve delivery without engineering discipline.
- You want generic AI training rather than repo-specific adoption controls.
FAQ
Common questions
Why not just buy Cursor, Copilot, ChatGPT, or Claude Code?
Tool licenses give developers access to AI. They do not create repo-specific instructions, review gates, regression strategy, secure prompt standards, tool policy, or measurement discipline.
Do you need access to our source code?
Not always. Many audits can be done through interviews, workflow review, sanitized examples, CI/test review, and policy review. Where source inspection is approved, access can be limited to selected repositories or files.
Do you resell or represent any AI coding tool?
No. SNS provides a vendor-neutral review. We do not require clients to buy a specific AI coding tool, and we do not resell tool licenses.
Can this be done remotely?
Yes. The audit can be delivered remotely for India-based, global, and distributed engineering teams through interviews, workflow review, document review, and approved repository inspection.
Can this work with legacy repositories?
Yes. Legacy repositories often benefit from stricter no-agent zones, clearer test commands, dependency rules, and smaller reviewable changes.
Which tools do you support?
We review workflows around Cursor, GitHub Copilot, Claude Code, Codex, Gemini CLI, OpenHands, and similar coding-agent tools.
What does the final report contain?
The report includes findings, risk areas, recommended controls, tool/workflow recommendations, measurement approach, and a 30-day rollout plan.
Can you help implement the recommendations?
Yes. Implementation support can cover repo instructions, PR policy, CI gates, security scanning, regression improvements, and developer onboarding.
How do you measure whether AI coding is helping?
We focus on practical signals such as cycle time, review time, test failures, revert rate, defect leakage, PR size, tool cost, and developer experience.
How do you handle security-sensitive code?
We define explicit approval boundaries for authentication, billing, cryptography, secrets, infrastructure, migrations, and other sensitive areas.
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