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Implementation brief

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Build a Financial Transaction Investigation Agent

Format
Code-aware
Lines
67
Sections
11
Linked challenge
Build a Financial Transaction Investigation Agent

Prompt source

Original prompt text with formatting preserved for inspection.

67 lines
11 sections
No variables
1 code block
## Who Uses It
A compliance analyst investigating transaction alerts.

## What You Will Build
Build an agent that assembles transaction histories, entity records, and supplied alert rules into an investigation case. Show linked evidence, unresolved identity questions, and a draft analyst narrative.

## Inputs
- Transaction ledger
- Entity records
- Frozen fictional alert rules

## Workflow
1. Resolve entities using stable identifiers.
2. Build a transaction timeline and relationship graph.
3. Explain which supplied rules produced alerts.
4. Assemble an analyst case with evidence and unresolved questions.

## Interface
A case workspace with a transaction graph, evidence table, analyst notes, and a report export.

## Working Demo
Build an E1-to-E2 case showing total 110, link T1 and T2, and request analyst review without asserting criminal activity.

## Deliverables
- A working local application with the specified review interface.
- Source code, dependency lockfile, and setup commands.
- A recorded end-to-end demo using the supplied starter data.
- Example outputs, action logs, and automated tests.

## Starter Data
These fictional records are a small development fixture, not a production dataset. Implement the mock services described in the brief.

```json
{
  "entities": [
    {
      "id": "E1",
      "name": "Example A"
    },
    {
      "id": "E2",
      "name": "Example B"
    }
  ],
  "transactions": [
    {
      "id": "T1",
      "from": "E1",
      "to": "E2",
      "amount": 60,
      "day": "2026-09-01"
    },
    {
      "id": "T2",
      "from": "E1",
      "to": "E2",
      "amount": 50,
      "day": "2026-09-01"
    }
  ],
  "exercise_rule": {
    "daily_pair_total_review_threshold": 100,
    "implies_illegality": false
  }
}
```

## Constraints
Use fictional entities and rules. Do not file regulatory reports or make real sanctions determinations.
Choose an available model that supports your implementation. Record its identifier and version.

## Evaluation
Review the complete application, not only the starter-case answers.
- The aggregate is 110.
- The case links both transaction records.
- The narrative distinguishes an exercise alert from a finding of wrongdoing.
Add one normal case, one incomplete-input case, and one failure-recovery case.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Preserve the source structure until you know which part of the prompt is actually driving the result quality.

Tune next

Change domain facts, examples, and tool context first before you rewrite the instruction scaffold.

Verify after

Validate one failure mode at a time so prompt changes stay attributable instead of getting noisy.