On a ten-year-old codebase, agents make things worse — not just less helpful.
On greenfield, agents shine. On your oldest system, the rules that matter live in old postmortems, chat threads, and the head of someone who resigned. So the agent codes confidently against a system it cannot see — and confident and blind is worse than slow.
The benchmarks agree with your gut.
Agents look strong on curated tasks. Move them onto real professional repos — the kind you actually run — and the numbers collapse.
Solve rate on real professional repos
Frontier models solve 65%+ of curated bug tasks on SWE-Bench Verified — but only ~23% on SWE-Bench Pro, the contamination-resistant benchmark built from real professional repos. The demo was the easy part.
of agent failures happen before business logic
On enterprise systems, more than 95% of agent failures occur before the agent even reaches your business logic. It gets lost in the structure first.
AI adoption vs delivery stability
AI adoption has a measured negative relationship with delivery stability. More AI, less stable delivery — measured, not felt.
“The results were bad. We had to diff through the code base the old way to confidently accept/reject what Claude was telling us.”
Install the knowledge the agent is missing.
The agent doesn’t fail because it’s weak. It fails because your system’s rules aren’t written anywhere it can read. Timo writes them down — in your repos, cited, and kept true by the gate.
Timo Docs
Full technical and functional documentation, generated from the code itself — modules, data flows, screens, the parts nobody has touched in years. Kept true by the gate: when the code changes, the docs are checked, not trusted.
- What each module does and why it exists
- How data actually moves through the system
- Written for engineers and readable by new hires
Timo Memory
The compact layer agents load before they touch a file: what broke before, the rules that aren't in the code, the gotchas that used to live in one person's head.
- What broke before, and why
- The invariants agents must not violate
- Every claim cited to file:line — or flagged
The system becomes something agents can navigate and new hires can read.
How it works, what it costs to run, what it looks like in a session — Timo Memory & Docs →
We’ve done this on real legacy code.
Not a demo repo. A 5,500-file legacy Java codebase, documented end-to-end — and an 875K-line production Flutter app through the same disciplines.
tables documented
screens documented
modules documented
secrets leaked
Every claim in those docs is cited to file:line or flagged— that’s the discipline, not a feature toggle. See who we work with →
Start with your hardest repo, not your easiest.
If Timo helps on the repo everyone avoids, the rest is easy. The first step is free and read-only.
1 · Run the Agent-Readiness Scan
Point it at your oldest, meanest repo. Read-only. You get six scored dimensions and a fix plan — exactly where agents will fail and why. See the CLI →
2 · Install as a PR
timo init opens a pull request with memory, docs, standards, and gates. Your team reviews it like any other change.Nothing lands that you didn’t approve.
3 · The program
For the deep end — the systems nobody wants to own — two of our people work inside your team: an engineer and a program lead.Academy cohorts run on your own codebase, not slides. That’s how a team becomes AI-native on the code it already has. The Academy →
Find out what your hardest repo looks like to an agent.
Free, read-only, five minutes. Six scored dimensions and a fix plan — before you let an agent near the code.