Build a Financial Transaction Investigation Agent

A compliance analyst investigating transaction alerts.

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.

Transaction ledger

Entity records

Frozen fictional alert rules

Resolve entities using stable identifiers.

Build a transaction timeline and relationship graph.

Explain which supplied rules produced alerts.

Assemble an analyst case with evidence and unresolved questions.

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

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

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.

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 } } ```

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.

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.