Pay Down Agentic Debt

In a machine-learning system, changing anything changes everything. The debt an agent leaves behind does not sit in one place and wait like the old kind; the intelligence at the centre is a learned thing, and a learned thing entangles whatever it touches. So a surgical, obviously correct fix in one corner degrades a model three hops away you would swear is unrelated, and you are debugging at midnight certain the system has a ghost in it. It does not have a ghost. It has agentic technical debt, and the founders who survive growth are the ones who rank it by interest rate and pay it down without breaking ten things to fix one.

The method

Find it, rank it by interest, pay it down CACE-safe through the eval gate. Full framework: references/debt-method.md. Source: Handbook Chapter 07.

Step 1: Audit the repo for the six debts

Point your agentic-coding tool at the codebase and force it to find the categories below, not a vague "what's messy". An unranked list paralyses; a named taxonomy you can score.

Step 2: Score each item by interest x principal

Interest is how fast the debt compounds; principal is the cost to fix. Rank by interest first, always. See the scoring grid in references.

The debt that screams is rarely the debt that kills you. The quiet leak nobody is complaining about, the broken feedback loop, that is usually item one.

Step 3: Pay it down CACE-safe, one thing at a time

Change one thing. Run the full eval. Measure. Keep or revert. Never bundle. The diff is not the blast radius; the eval is the only thing that knows what moved.

Step 4: Gate every change on the eval suite

No paydown ships without passing the eval that protects the parts you did not touch. If you have no eval suite, that is your highest-interest debt; build it before you refactor anything.

Output

• A ranked debt ledger: each item scored interest x principal, highest-interest at the top.
• The safe paydown order: which item first, and the one isolated change that pays it.
• The eval gate for each change: the assertion that proves you broke nothing downstream.
• Next: log the trade-offs you deferred via capture-learning; re-run this audit monthly.

Constraints

• No big-bang refactor. Rewriting the God Agent in one swing is the faster mess, not the faster company. Extract one specialist, gate it, repeat.
• No fix without an eval. CACE means you cannot reason a change safe, you can only measure it. Trusting the green diff is how the regression ships.
• Pay the highest-interest debt first even when the ugly code is screaming. Especially then.
• Stay theme-agnostic; the founder supplies the domain, you supply the rigour.

Dictionary

CACE · agentic technical debt · entanglement · eval · the God Agent · infinite loop

Copy-paste version

For non-coders: paste into a coding tool with repo access (Claude Code, Cursor, Codex). For the ranking and plan alone, paste into any chatbot (Claude.ai, ChatGPT).

Act as my staff engineer doing an agentic-debt audit. My product: [ONE_LINER]. Domain: [DOMAIN].
The codebase was largely AI-generated and now every change feels risky.
1. Audit for six debts: prompt sprawl (one mega-prompt), entangled context (a signal feeding more
   than one learned component), untested paths (code no eval checks), a God Agent (one agent doing
   many jobs), missing evals (no held-out test set), and dead retrieval / broken feedback loops (a
   signal generated but never stored).
2. Score each by INTEREST x PRINCIPAL. Rank highest interest first: blocks data collection > blocks
   scaling > risks a CACE regression > merely cosmetic. Tell me ugly code that works and never
   changes is almost free; leave it.
3. Give me the safe paydown order: ONE isolated change per item, the eval that must pass before it
   ships, and what breaks if I bundle changes. Remember CACE: changing anything changes everything.
Do not propose a big-bang rewrite. If I have no eval suite, tell me that is debt item one.
End with the single first change to make this week and the eval that gates it.