El código es el log.Legible para humanos. Hecho para agentes de IA.

Este es el ejercicio del que salieron esos informes. Una diferencia: en nuestras ejecuciones le dimos al agente una copia de la documentación; la tuya lo apunta al repositorio público. Dos archivos, tres pasos.

1. Guarda la especificación como TASK.md en un directorio vacío.
2. Inicia tu agente en ese directorio y dale el prompt.
3. Lee su FINDINGS.md — y si lo publicas en las Discussions del runtime que usaste (Java · TypeScript · .NET · Python), también lo leeremos.

El prompt

You are building a Java application from a written specification.

The specification is in `TASK.md` in the current directory. Read it, then build the
application it describes. Build it properly: it should compile, its tests should
pass, and the demo should run.

A library called **NarrativeTrace** is available to you for this project. Its
documentation is in its public repository:

- https://github.com/narrativetrace/narrativetrace-java — start with the README
- https://github.com/narrativetrace/narrativetrace-java/blob/main/documentation/llms.txt
  — a short orientation written for AI agents
- https://narrativetrace.ai/java/llms-full.txt — the full documentation written for AI agents

Read that documentation first, then use the library in the application you build.
Its artifacts are published on Maven Central under the group `ai.narrativetrace`;
the Gradle plugin id is `ai.narrativetrace`. Take the version from the install
block in llms.txt; the documentation explains which artifacts you need and how to
wire them up.

Rules for this exercise:

- Work **only** inside the current directory. Do not read, search, or modify
  anything outside it, and do not clone the library's source code — the
  documentation is what you have.
- You may use the network to read that documentation and to resolve build
  dependencies.
- You choose the design, the libraries, the project layout, and the testing
  approach. There is no house style to match and no reviewer to please.
- Work in the foreground. Do not background the build or the tests and then end
  your turn — run them, wait for them, and read the output.
- When the build and tests are green and the demo runs, write `FINDINGS.md` as
  the specification describes. In addition to what the specification asks for,
  cover what using NarrativeTrace was actually like: what the documentation got
  right or left you guessing about, what integration cost you, and whether the
  output it produced was of any use to you while you worked.

Finish by reporting, in your final message: whether the build and tests are
green, what you built, and where things stand.

La especificación — TASK.md

# Build task — Claims intake service (Java 17)

Build a small, self-contained Java 17 application called **ClaimFlow** that processes
insurance claims through a chain of collaborating services. It must build and test with
Gradle, and run as an executable demo.

This is a business-logic exercise, not an infrastructure one: everything is in-process
and in-memory. No database, no HTTP server, no external service, no network access.

## The domain

A claim is submitted and passes through five collaborating services. Each is its own
interface with its own implementation — do not collapse them into one class.

1. **PolicyService** — looks up a policy by `policyId`. A policy has a holder name, a
   coverage limit in whole cents, a deductible in whole cents, and an active flag.
2. **ClaimValidator** — rejects a claim that names an unknown policy, an inactive
   policy, a non-positive amount, or an incident date in the future.
3. **FraudScreen** — scores a claim from 0–100. Any claim scoring above 70 is referred
   rather than paid. Scoring may be simple and deterministic, but it must consider at
   least the claim amount relative to the coverage limit, and whether the same policy
   has had a prior claim in this run.
4. **ReserveLedger** — reserves funds against a policy's remaining coverage before
   payment, and can release a reservation. Reserving more than the remaining coverage
   fails.
5. **PayoutService** — pays an approved claim, given a payment token. Payment can fail;
   your demo must exercise at least one failing payment.

A **ClaimProcessor** orchestrates them and returns a `ClaimOutcome` describing what
happened: paid (with amount and transaction id), referred, or rejected (with a reason).

## Required behaviour

- Payout amount is `min(claimAmount, coverageLimit) - deductible`, never below zero.
- A claim must be validated, then screened, then reserved, then paid — in that order.
- **If payment fails after a reservation was made, the reservation must be released.**
- A referred claim is never paid and never reserves funds.
- Two claims against the same policy must respect the remaining coverage.
- The payment token is a credential and must never appear in any output your program
  produces.
- The claim holder's name is personal data; treat it accordingly in whatever your
  program writes out.

## Deliverables

1. A Gradle project that compiles under Java 17 and runs `./gradlew test` green.
2. Unit tests covering the behaviour above, including the failure paths.
3. A runnable demo (`./gradlew run` or a `main`) that exercises at least five scenarios:
   a straightforward paid claim, a claim above the coverage limit, a referred (high
   fraud score) claim, a rejected claim, and a claim whose payment fails after a
   reservation.
4. A short `FINDINGS.md` in the project root — see "What to report" below.

## What to report in FINDINGS.md

Write it for another engineer, not for us. Cover:

- What you built and how you verified it works.
- Anything that surprised you, went wrong, or took longer than expected.
- How you satisfied yourself that the compensation rule (release the reservation when
  payment fails) actually holds — what evidence do you have?
- How you satisfied yourself that the payment token never leaks into output.
- What you would want before running this in production.
- Anything about the tools or libraries you used that helped or got in your way.

Be honest and specific. A report that says everything went perfectly is less useful
than one that names what was awkward.