# Harness Matters, Part 1: How to test the public-data tools behind an AI research agent

> Public-data tools rarely throw an error; they hand your agent a believable wrong number. Here is the three-step test we ran on Matterfact's FINRA, economic and court-record tools, what it caught, and four questions to ask of any data tool behind an AI agent.

Midway through testing, our FINRA tool reported NVIDIA's short interest at 12.81% of shares outstanding.

The right number was about 1.2%.

The tool had divided by NVIDIA's share count from before its 10-for-1 split in June 2024. It raised no error, and the row looked clean. A research agent reading it would have told a PM that NVIDIA was a crowded short.

That's the problem nobody warns you about when you point an AI agent at public data: it almost never breaks loudly. Tickers get reused. Splits change share counts. APIs stop at 50,000 rows. A court search that misses says "no record" instead of failing. The agent can't tell any of this from a right answer, so it reasons carefully from a wrong input and hands the analyst a confident wrong conclusion.

Matterfact's research agent reads public records directly: FINRA short interest and short volume, economic statistics from FRED, BLS, Census, EIA, FDIC, CFTC and UN Comtrade, and federal court dockets from CourtListener. In October we put the tools behind those three sources through the same test we use for our SEC filings and options-chain tools. It has three steps:

1. **Check the tool against the agency's own files**, never against itself.
2. **Have domain experts try to break it**, and your tests.
3. **Teach the tool to say "I don't know"** instead of guessing.

It found 115 problems across the three sources. A few were crashes; most were plausible wrong answers. Nearly all are fixed, and the few we left alone are documented with the reason. If you're putting an agent on top of public data, this is the test I'd run on every tool it reads.

## Public data needs careful handling

A failed request is easy to spot. The errors that hurt are the requests that succeed and return the wrong rows. Every one in Exhibit 1 came back as a well-formed table.

Exhibit 1\
**Every one of these came back as a clean table, with no error.**

| Request | What the tool returned | Right answer | What the agent would conclude |
|---|---|---|---|
| NVIDIA short interest after the June 2024 split | 12.81%, from the pre-split count of 2.46 billion shares | About 1.2% | NVIDIA is a crowded short |
| COR short interest, 2019 to 2021, when the ticker was CoreSite Realty's | 0.66%, from Cencora's 208 million shares | 3.74% | CoreSite was barely shorted |
| A year of daily short volume for 80 large caps | The first 50,000 rows: Apple lost 43 of its 252 days | Every day, or a warning | It has the full year |
| CPI for 2024 | January only | All 12 months | The data is running late |
| A Central District of California docket written `EDCV 24-1234` | "No record" | The docket | The litigation check is clean |

*Note: Five of the 115 defects found. Some were in the tools before this work; others came in during the build and were caught by a later round. All five are fixed, each with a regression test.*\
*Source: Matterfact hardening reports for FINRA, economic data and CourtListener, October 2026*

None of these would trip an alarm. A short interest of 12.81% is high but possible. A CPI series that ends in January looks like a late release. And "no record" on a court docket reads like good news.

## Step 1: Check the tool against the agency, never against itself

The easy way to test a data tool is to check that its output looks right. That's exactly how you miss everything in Exhibit 1. So every check we run compares the tool with a source it didn't produce:

- **FINRA:** FINRA's own bi-monthly and daily files, its archive downloads, Nasdaq Trader's symbol directory and the SEC's XBRL share counts.
- **Economic data:** each series read two ways. BLS against FRED, EIA's API against its downloadable workbooks, FDIC's API against its bulk files, and a FRED vintage against ALFRED's archive.
- **CourtListener:** the SEC's litigation releases, which give each case's number, court, filing date and defendant.

Each check has a pass bar, and we wrote the bars down before the first run. They didn't move afterwards. The one exception, FINRA's symbol-mapping check, was redefined after it failed, and the test report says so next to the result.

Exhibit 2\
**On every row we compared, the tools now match the agencies' own files.**

| Source | Compared against | Sample | Match |
|---|---|---|---|
| FINRA short interest | FINRA's bi-monthly files, 8 cycles from 2018 to 2026, 7 markets | 320 rows | 100% |
| FINRA short volume | FINRA's daily files, 15 trade days | 597 symbol-days | 100% |
| FINRA threshold list since 2016 | FINRA's archive download | 2,719 days | 100% |
| BLS series | The same series on FRED | 3,602 observations | 100% |
| EIA prices and stocks | EIA's downloadable workbooks | 10,986 observations | 100% |
| FDIC bank financials | FDIC's bulk files | 4,060 rows | 100% |
| CFTC commitments of traders | CFTC's annual files | 37,152 values | 100% |

*Note: The largest comparisons only. Also checked: FINRA days to cover and change against FINRA's documented definitions (4,346 rows, 100%), and UN Comtrade against a second official source, Census trade data (72 month-partner pairs, 95.8% within 1%, against a bar of 90%).*\
*Source: Matterfact hardening reports, October 31, 2026*

Today every check clears its bar: 35 of 35 for FINRA, 23 of 23 for economic data and 21 of 21 for CourtListener.

That sounds like the end of the job. It wasn't.

## Step 2: Have domain experts try to break it, and your tests

This is the step I'd least want to skip.

FINRA's tool passed every bar on its first full run. By the usual standard, it was done. We then handed the tool and its tests to Matterfact's finance domain experts and agent harness engineers, with one brief: break both. They went after odd live records, revisions, pagination, holidays, injected failures, and whether our ground truth was really independent.

Their first review found 17 problems. Two were serious, and every one of our checks had passed them, because no check pulled enough data to reach them:

- **Silent truncation.** The tool stopped at 50,000 rows and returned them as the full date range. That's how Apple lost 43 trading days, and how the threshold list since 2016 quietly ended in August 2023.
- **A word match.** The short-interest screen dropped Preferred Bank, a common stock, because its name contains "Preferred".

We fixed what they found and sent the new code back for another review. Then we did it again (Exhibit 3). The count didn't fall the way you'd hope.

Exhibit 3\
**The third review still found seven serious problems, including the NVIDIA split error.**

| Source | Round 1 | Round 2 | Round 3 | Total |
|---|---|---|---|---|
| FINRA | 17 (2 high) | 18 (4 high) | 8 (3 high) | 43 |
| Economic data | 14 | 11 (3 high) | 12 (2 high) | 37 |
| CourtListener | 11 (1 high) | 13 (1 high) | 11 (2 high) | 35 |
| **Total** | **42** | **42** | **31** | **115** |

*Note: Severity as graded by the reviewers. The first economic-data review did not grade severity.*\
*Source: Matterfact hardening reports, review rounds 1 to 3*

A tool that has passed its own tests has only been checked for the failures its authors thought of. Each new review found failures nobody had thought of yet.

The reviewers read the tests as closely as the tool, and some of the worst problems were in our tests:

- **A check that trusted the tool.** FINRA's screen completeness counted a dropped row as accounted for whenever the tool said it had dropped it. It now checks every dropped row against Nasdaq Trader's directory and fails if a common stock went missing.
- **A crash that counted as a pass.** A metric that was never measured read "n/a" and didn't fail the run. Now it does.
- **A regression that couldn't fail.** Renaming CourtListener's search filter breaks every lookup, yet the check still scored 100%, because each failed lookup counted as an honest "no record". A "no record" now has to survive an independent search before it counts.
- **A rounding rule that hid errors.** A published 250.0 accepted a value off by 0.5. Checks now compare at the decimal places the agency published.

A test suite that passes a broken tool is worse than none, because it turns a guess into a claim. CourtListener's tests now include self-tests that replay these regressions and must fail.

## Step 3: Teach the tool to say "I don't know"

Most of the fixes didn't make the tools return more answers. They made them return fewer wrong ones, and say why.

When FINRA's tool can't be sure a share count belongs to the security in the row, it now leaves the percent blank and writes a note explaining the reason. In the final run it did that on 577 rows of reassigned tickers, and on every ADR, warrant, preferred and exchange-traded product line sampled. Exhibit 4 shows the rest.

Exhibit 4\
**Where a number would be doubtful, the tools now leave it out and tell the agent why.**

| Case | What the tool does now |
|---|---|
| A ticker now used by another company | No percent; the note names both companies |
| An ADR, warrant, preferred, unit or ETF line | No percent; the note names the line type |
| A stock split between the share count and the cycle | No percent until a post-split count is filed |
| A share count under 1,000, or under the short interest | Not used; the note names the filing |
| A pull larger than the tool can return in full | Refused, or cut with a warning that gives the source's total |
| A docket whose filings aren't in CourtListener's archive | Says the docket text is all there is, and not to describe the filings |
| "No record" on CourtListener | Says CourtListener holds only part of PACER, and to check PACER before concluding the case doesn't exist |
| FDIC's failures index older than the latest failure | Warns with the index date and points to FDIC's failed bank list |

*Source: Matterfact FINRA, economic-data and CourtListener tools, October 31, 2026*

Failures get the same treatment. We plant them on purpose: a server that hangs past the timeout, a 503, a 429 with `Retry-After`, a 403 block page, malformed JSON, a response cut short, a refused key. Each one now produces a single plain line written for the agent that reads it, never a stack trace. For example: "FDIC is rate-limiting our requests (HTTP 429); wait about 5 minutes before trying again, and do not retry in a loop."

Each tool also carries a trust table from its test run, so the agent knows which outputs were measured and how well.

## Fair questions

**Isn't this just testing?** Partly. But normal tests check the failures you already thought of. FINRA's tool passed all of ours on its first run, and the review still found 17 problems.

**How do you know the findings are real?** Each fix comes with a regression test, and later rounds brought in new ground truth: the first split-adjusted trading day of eight stock splits, from the companies' own 8-Ks, and five ticker reassignment dates from FINRA's issue names and the public record. When we ran the third round's new economic-data cases against the previous version of the tool, 16 of 19 failed. The other three guard against a fix going too far.

**Doesn't a blank make the tool less useful?** Less complete, yes. But a blank with a reason tells the analyst exactly what to check. A wrong number tells them nothing is wrong.

**What haven't you checked?** CourtListener's daily API quota is small, so its live check replays a five-call run on two SEC cases. Other courts, bankruptcy lookups and long dockets are covered by tests on recorded responses but not yet checked live. Across the three tools, 520 offline tests run on recorded data.

## A cheat sheet for any data tool behind an agent

Every source an agent reads is a place a confident wrong answer can get in. Before you trust one, ask four questions:

1. **What is its output compared against, and did the tool produce that ground truth?**
2. **Were the bars written before the first run, and has anyone moved them?**
3. **What happens on the 50,001st row, the day after a split, or the ticker that changed hands?**
4. **When it can't know, does it say so, or does it return a number?**

We're running the same test now on short-sale records, openFDA, and federal agency and data.gov records, with ClinicalTrials.gov, USAspending and NHTSA next. Each will report its bars the same way, including the ones it doesn't meet yet.

[Part 2](/blog/harness-vs-model-finance-agent-results) asks whether a leaderboard points you to the right model. On Vals AI's finance tasks, Claude Opus 5.5 went from 10th on the leaderboard to first of four in our agent.

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