Agentic Codebase Readiness Audit
Last verified: June 2026· engagement
We map your codebase against what actually makes it AI-coding-ready — module boundaries, test coverage, type strictness, docs, CLAUDE.md / rules files, and MCP potential — and quantify how far off you are.
- Outcome
- A scored report + prioritized remediation roadmap
- Timeline
- 2–3 weeks
- Pricing
- $10–25k audit (often leads to remediation)
- Buyer
- VP Eng, Head of Platform
The problem
Every engineering leader suspects their codebase isn’t ready for agentic development, but can’t quantify how bad it is or where to start. So the AI-coding rollout stalls on vibes.
What we do
- Score module boundaries, test coverage, type strictness, and doc quality.
- Check for CLAUDE.md / rules files, spec coverage, and MCP integration potential.
- Map where agents will succeed today vs. where they’ll thrash and burn tokens.
- Prioritize remediation by impact so the first fixes unlock the most agent leverage.
What you get
Built on our open source
openclawOS — An OS-like architecture for AI assistants — a kernel-based design with process-isolated apps.
Common questions about this engagement
Do you work under NDA?
Yes — we sign your mutual NDA before any data or repo access. For audits we prefer read-only access to start; for builds we work in a clean repo under your ownership.
Will we own what you ship?
Always. You own the code, the runbooks, the dashboards. We are explicitly set up to hand off and transition out, not to create dependency.
Vendor-agnostic — what does that mean in practice?
We integrate with what you already run — OpenAI, Anthropic, open-weight models on your cloud, your CI/CD, your observability stack. If a hosted vendor solves it, we will not reinvent it; if a self-hosted tool is the right answer, we will not pretend the hosted one is.
How is this different from a Big Four consulting deck?
We are the engineers doing the work, not analysts handing recommendations to a different team. The deliverable is working software in your repo, not a slide deck.
Can you work with our in-house AI team instead of replacing them?
Yes — most of our engagements pair with an internal owner and ramp them up to run the system after we leave. Many of our best engagements start with "we hired an AI team, help us get them productive."
Guides for this work
Is your codebase ready for AI agents?
Agents don't fail randomly on a repo — they fail predictably on the same things that slow down human engineers: fuzzy boundaries, missing tests, undocumented conventions. Readiness is just those weaknesses paid down deliberately.
Agentic codingWhat is agentic coding?
Agentic coding is letting an AI coding agent plan, edit across files, run tools and tests, and iterate in a loop toward a goal — powerful for the first 80% of a change, and quietly expensive on the last 20% if you skip standards and review.
Let’s scope it on a call
Thirty minutes with an engineer. We’ll tell you straight whether this is the right first move for your team.