Agentic Codebase Readiness Audit
Last verified: October 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.
What the audit looks at, and what it produces
The audit is deliberately empirical before it is analytical. We start by giving an agent several small, real tasks in your repository and watching what happens: which files it pulls into context looking for something that should have been obvious, where it invents a convention because it could not find yours, whether it can tell if its own change worked. That transcript is the most honest readiness signal available, because it is the same friction a new hire hits, compressed into an observable session.
Then we walk the dimensions and mark each one strong, workable, or weak. Module boundaries, because a change with an unknowable blast radius cannot be delegated safely. Test speed and legibility, because a suite an agent can run and read is the difference between delegation and supervision. Type strictness at the interfaces agents cross most. Whether written intent exists anywhere outside people's heads. Whether a context or rules file exists and whether it is still true. Whether internal systems are reachable over MCP or whether agents have to guess around the gap. Scoring is deliberately qualitative — a numeric composite invites arguing about the number instead of fixing the weakness.
The deliverable is a report plus a sequenced roadmap, and the sequencing is the part that earns its keep. Readiness work compounds in a specific order: a context file makes every subsequent task cheaper, tests make boundary refactors safe, clear boundaries make types easier to add. We also scope the assessment to the surfaces you actually intend to hand over, because readiness is local — a repository can be excellent in the service you have maintained for years and hostile in the one nobody understands, and an organisation-wide average hides both facts.
When this is not the right engagement
An audit is worth buying when you do not already know the answer. It is a poor fit when:
- You already know the answer and want help executing. Skip the audit and go straight to the remediation work.
- The repository is about to be replaced or substantially rewritten for unrelated reasons. Assess the thing that will still be there.
- You want a numeric score to put in a board pack. We will give you a defensible qualitative read and a roadmap, not a single figure.
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."
Do you need write access to our repository?
No. The audit runs read-only, and we prefer it that way — it keeps the security conversation short and it does not change what we can see. We do ask to run an agent against the repository on a handful of small real tasks, in a throwaway branch or a clone, because watching where it thrashes is the most honest readiness signal available. No change we make during the audit is intended to be merged.
Will you give us a number we can report?
We will give you a defensible read per dimension and a sequenced roadmap, not a single composite score. A number invites arguing about the number instead of fixing the weakness, and any score we invented would carry a precision it does not have. What you can report is the shape: which surfaces are ready for delegation today, which are not, what the first three fixes are, and roughly what each costs.
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 codingContext engineering for coding agents
Context engineering is the work of deciding what a model sees on each turn: what goes in, in what order, and — the part teams skip — what gets left out. On coding agents it is usually the difference between an agent that lands a clean diff and one that thrashes, because almost every agent failure that looks like a reasoning failure is really a context failure.
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.