Use case
Finance & Reporting Agents
Your AI analyst produces a clean, confident summary. No error indicators. No uncertainty signal. The board deck has the wrong numbers — last quarter’s forecast applied to this quarter’s actuals. You find out when someone asks a question the deck can’t answer.
Where things go wrong
Wrong file in context window (forecast vs. actuals)
The agent picks up last quarter’s forecast file instead of the current actuals because the file names are similar and the context assembly wasn’t instrumented. The output is internally consistent and numerically plausible. It’s just based on the wrong data.
Board deck or investor update contains numbers from the wrong period; discovered in a high-stakes meeting rather than in the pipeline.
Transaction miscategorization (CapEx vs. OpEx)
The agent categorizes a batch of vendor payments as capital expenditure instead of operating expense. The P&L looks better than it is. Your CFO is on a call with auditors before the pipeline flags anything.
Audit exposure; manual reclassification required across a full quarter’s worth of transactions.
Confident output with no error indicators
The model produces a complete, well-formatted report. Every number is wrong, or several key numbers are wrong, but the output carries the same confident tone as an accurate report. There is no uncertainty signal, no low-confidence flag, nothing that would prompt a reviewer to check the underlying data.
The report moves downstream — to the CFO, to the board, to the investor update — based on the assumption that a clean-looking output is a correct output.
Metric drift across report runs
Two consecutive runs of the same report produce different numbers for the same period because the context window picked up different source files on different runs. The discrepancy is caught in a review meeting, not by the pipeline.
Loss of confidence in the AI-generated reports; engineering time spent on a post-hoc audit instead of forward work.
Eval + control loop
What happens when a rule fires
The response
How TruLayer closes the loop
- Faithfulness
Finance agents are where faithfulness scoring earns its keep. The faithfulness evaluator checks whether the agent’s output is grounded in the source data that was provided as context — the actual transaction ledger, the correct actuals file, the current period’s data. A faithfulness score below threshold means the output has drifted from its grounding context, which is exactly what happens when the wrong file ends up in the context window. This catches the "wrong quarter’s forecast" failure mode inline, on every run, before the report is generated — not after the CFO is on a call with auditors.
When a faithfulness rule fires, the control loop acts on the next call on the same failure path. For finance agents, the typical action sequence is: retry with a corrected context (explicitly specifying the correct source file), fall back to a stricter prompt that names the required fields and time period, or route to a human review queue if the retry also fails eval. The max cascade depth controls how many retries the loop attempts before escalating — for a board-deck generation agent, you likely want a low depth (1–2 retries) and then mandatory HITL review before the report is released.
The per-trace before/after delta surfaces exactly where the eval score dropped and what the remediation action changed. For finance pipelines, this is the audit trail your engineering team needs when a CFO or auditor asks how a wrong number got into a report. The trace shows: which span produced the failing output, what the faithfulness score was, what remediation action fired, and whether the corrected output passed. That’s the answer to the question — not a Slack thread, not a post-mortem hypothesis.
See it in practice
Instrument your finance & reporting agent in two lines.
Wrap your LLM client. Every span from this trace is captured and scored by every built-in evaluator. Eval rules and control-loop actions are configured in the dashboard.
import { TruLayer } from '@trulayer/sdk'
import OpenAI from 'openai'
const tl = new TruLayer({ apiKey: process.env.TRULAYER_API_KEY })
const openai = tl.instrument(new OpenAI())
// Every span from this client is captured, scored by all 25
// built-in evaluators, and surfaced in the reporting project.
// Eval rules + control-loop actions are configured in the dashboard,
// not in your application code.
const response = await openai.chat.completions.create({
model: 'gpt-4o',
messages: [{ role: 'user', content: task }],
})Ship reliable finance & reporting agents.
Free tier includes 1M spans / month · No credit card