GurtOSat Cal Hacks 13.0

Built for the Real World, and the Customer Impact Award

A finance agent with a path to production

The credit desk is a production-minded finance agent for a small wholesaler: a credit analyst agent and a collections clerk agent, with a human controller who signs off on anything large or suspicious. GurtOS is its path to production. Each seat runs as its own Linux user without keys, every action lands in an append-only log, approvals reach a phone, and the box picks up where it was after a power cut.

Proof Scored from the action log against an answer key, the latest live run caught 14 of 14 planted errors with 0 false alarms and decided all 6 credit applications as the key does. A simple rule on the same CRS data approves all six for $153,000, including $20,000 to a fake applicant whose $18,500 rush order would have shipped to a freight forwarder.

Credit desk: the fraud hold waits for the controller. A quick run of the credit desk: CRS (sandbox, live) fails APP-1043's identity, the analyst puts it on fraud hold and waits for the controller, who approves on the box screen; the window then shows the decision and its rules. Scripted seats call the real tools with no model, so they catch 2 of 14 planted errors by design; the live Droid run caught 14 of 14. Test identities only. Silent screen recording, 29.7 seconds.

What Microsoft asked for

From Microsoft's track description and judging criteria, as copied into the project's track plan (docs/tracks.md).

  1. A production-minded AI agent for finance, productivity, healthcare or similar.
    Met

    Finance: trade credit and collections at Fogline Supply Co., a made-up Oakland wholesaler with 26 employees, about $8.1M revenue and 38 accounts on trade credit. Two agent seats (Droid running Claude) and a human controller.

  2. Customer understanding.
    Met

    A small wholesaler's credit desk is one or two people doing credit checks and collections by hand, where one fake customer or one misapplied payment costs real money. The case is built from those everyday failures: a synthetic identity, a payment applied to the wrong customer, a post-dated check booked as cash, an invoice billed twice, a limit nobody reviewed in 29 months.

  3. Measurable usefulness.
    Met

    Every run is scored from its log: planted errors caught out of 14, false alarms, and decisions matched on 6 applications, 11 customers and 6 orders. Three live Droid runs caught 13, 13 and 14 of 14 and decided all six applications right. The simple rule is the baseline on the same data.

  4. Path to production.
    Met

    It runs on GurtOS: each seat is its own Linux user with its own copy of the files, keys stay in a root-only vault, every action is in an append-only log, approvals reach a phone, the box resumes after a power cut, and the OS updates as a whole and rolls itself back. The rules are a file, so the controller changes policy without code.

  5. Responsible design.
    Met

    The agent cannot approve its own large proposals, clear a fraud flag, or decide before the checks run. It never sees the answer key. Every decision names the rule and the data behind it. It pulls only what trade credit needs (no criminal or eviction searches) and never tells an applicant which check failed.

  6. End-to-end function.
    Met

    Application in, seven CRS checks, a decision by rule, the controller's approval on a phone, the decision in the log, the run scored. The merge gate runs this path on every merge, live against CRS and Nessie.

  7. Customer Impact Award.
    Met

    The customer is the controller at a small business: the desk does the checking and the paperwork, stops the one application that would have cost $20,000 of credit, and brings only the decisions that need a person to their phone, with the reason and the data attached.

Screenshots

The Credit desk window: 2 of 14 planted errors caught in a quick scripted run, 1 needs you, the analyst and clerk seats, and the applications table with APP-1043 highlighted amber, where the simple rule approves $20,000 and the desk asks the controller to put it on fraud hold, with Deny and Approve.
The Credit desk window during a Quick run (scripted seats, live CRS and Nessie, about 10 seconds). APP-1043 waits for the controller; the simple rule beside it would approve $20,000. Approve here, on a paired phone or in Notification Center: the first answer wins, and the log names who gave it.
The recorded decision: fraud hold, decided by the controller on the phone, rules FRD-1 and FRD-2, seven CRS request ids.
The decision in the log: what, why (rule ids), who approved, and the data it rests on.

Evidence

Measured: three live runs

Run Rules Planted errors caught False alarms Credit decisions Collection steps Orders Time, model cost
88d75b 0.1 draft 13 of 14 1 6 of 6 9 of 11 6 of 6 9 min, about $1.50
6f5c0b 0.2 (Mana) 13 of 14 1 6 of 6 9 of 11 6 of 6 9 min, about $3.54
73c47b 0.2, seats told to record follow-ups 14 of 14 0 6 of 6 9 of 11 5 of 6 13 min, about $4.67

The baseline: the simple rule in the rules file approves all six applications for $153,000. Following the rules, the desk approves $8,000 itself, sends $83,500 to the controller at reduced limits, and stops $20,000 as fraud.

What the controller saw

In run 73c47b the analyst asked the controller five times. Each question carries the decision, why, and the figures. The fraud referral, shortened:

Credit desk [APP-1043 fraud hold 0]: fraud referral. Put Pacific Crest Provisions LLC (APP-1043) on fraud hold,
no credit, cash in advance only? The analyst found: Four independent fraud stops: (1) FRD-1, TransUnion identity
returned code 2201 (SSN not associated with name) and code 2202 (DOB mismatch). (2) FRD-2, Fraud Finder risk
score 88. (3) FRD-3, business registered 2026-09-01 (52 days before the case date) but the application claims
4 years in business. (4) FRD-4, six compound signals present (threshold is two) ...
Rules: FRD-1, FRD-2, FRD-3, FRD-4, FRR-1, FRR-2, FRR-3, GEN-1, CHK-1, CHK-3.

Production checks on every merge

The merge gate, ./demo, runs the whole box on a fresh test VM before anything reaches main. The Oct 24 run passed 22 sections and 175 checks with 0 failures, in 102 seconds on the x86 test VM. The parts this agent leans on:

moment 2: the booby-trapped page
  ok    it could not read the vault: root only
  ok    no secret anywhere in its environment or files
moment 3: a pause for your OK
  ok    another task kept running and finished while P waited
  ok    only one of the two parallel $0.60 payments went through: $0.60 spent, not $1.20 (atomic budget)
moment 4: pull the plug
  ok    the long job carried on from where it was: no step done twice, none skipped
  ok    the deploy is still waiting for its OK, live in SpacetimeDB
track credit-desk
  ok    the fraud referral paused the analyst until the controller answers
  ok    the analyst cannot approve $40,000 on its own: above its $10,000 authority, nothing is recorded until the controller answers
  ok    the clerk cannot release a credit hold; that needs the controller
  ok    no CRS or Nessie key is anywhere in the log

How it works

How it works, step by step
  1. YouA credit application arrivesA case folder: the ledger, applications, bank statements.
  2. TaskThe analyst and the clerk work itDroid running Claude, two Linux users, no keys, no network.
  3. GurtOS coreTools hold the keys and the rulesCRS and Nessie calls run in the core. A decision must cite rules that exist.
  4. OutsideCRS and NessieCredit, identity and fraud data; the bank's deposits.
  5. GurtOS corePause for the controllerAbove $10,000 or on any fraud hold. Other work keeps running.
  6. YouThe controller says yes or noOn the portal or a phone.
  7. GurtOS coreLogged and scoredEvery call, decision and approval in an append-only log; each run scored against the answer key.
Task
its own Linux user, its own copy of the files, no keys
GurtOS core
the GurtOS service on the box, root, the only part that reads the vault
Outside
a service outside the box
You
a person, on the portal, a phone, Jira or a chat

Why the box is the path to production. A finance agent that runs as a script on someone's laptop has the laptop's keys, can write anywhere, and leaves no record a controller can audit. On GurtOS the same agent:

Production need What GurtOS does
Least privilege Each seat is its own Linux user with no sudo, its own copy of the files, and only its job's tools. Segregation of duties is tool access.
Secrets CRS and Nessie keys live in a root-only vault. Tools that need them run in the box's core; the seats get results.
Human approval The tool returns "needs controller"; the box pauses only that task and sends the question to the portal and phone.
Audit One append-only, fsynced action log: every call, result, approval and cost. Decisions carry rule ids and CRS request ids.
Reliability After a power cut the box rebuilds every task from the log and resumes it. Updates install to a second system slot and roll back if unhealthy.
Change control The rules are a YAML file. Swap it per run with {"rules": "/path/to/rules.yaml"}; the tools refuse rule ids it does not have.
Regression testing Every run is scored against an answer key from its log, so a prompt or rules change is measured, not guessed.

Try it

On a box with CRS_USERNAME, CRS_PASSWORD and NESSIE_API_KEY in its vault:

curl -s -X POST localhost:7700/t/credit-desk/run -d '{"case": "fogline-2026-10"}'
curl -s localhost:7700/approvals
curl -s -X POST localhost:7700/approvals/<id> -d '{"approved": true, "by": "controller"}'
curl -s -X POST localhost:7700/t/credit-desk/score -d '{"run": "<run>"}'

Any case folder that passes check-case.ts runs the same way with {"dir": "/path/to/case"}. The Hyphenate page has the steps for writing your own.

Live, test and simulated

PartStateDetails
Agent seatsLiveDroid running Claude in the scored runs. The merge gate uses scripted seats that call the same real tools.
CRS and NessieLiveBoth sandboxes, called live. CRS's own test identities only.
The controller in scored runsSimulatedA script answered yes so runs can be scored unattended, and the log says so. On a live box a person answers.
Business credit fileSimulatedThe CRS sandbox has no business credit route; the file is made up and labeled.
The companySimulatedFogline Supply Co. is fictional. No money moves: the desk decides credit and records collection steps.

Limits

  • The scorer checks decisions field by field, but a finding counts as caught by matching its rows, wording and rule ids, not by checking that its conclusion is right.
  • Two collection steps differ from the key in every run: the clerk put two over-limit customers on credit hold (rule COL-3) instead of the aging step the key lists.
  • The business credit file is simulated, and the bank is read, not written.
  • The controller is one role. Routing approvals by amount to different people is planned for GurtOS Business.
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