SPARK Lab · a live lab for the SPARK AI Network

Before you let an agent do real work, you need to know it will catch its own mistakes.

SPARK Lab measures exactly that. We give agents a task that has known answers and a few deliberately hidden errors, then watch how they do under different conditions. The first question, and the one most enterprises are stuck on: does having agents check each other’s work actually make them reliable, and what does it cost?

See it for yourself

same task, two ways of working

The task: read an invoice and pull out the numbers. 4 errors are hidden in the math.

One agent, working alone

Reads the document and answers in a single pass. No second look.

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Got right

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Errors caught

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Cost

press Run to start

A swarm that checks its own work

One agent answers, a second re-checks the math and flags what is off.

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Got right

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Errors caught

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Cost

press Run to start

Why this is the measurement that matters

Reliability, not vibes

Every task has a correct answer. We score the result against it, field by field. No guessing whether it “seemed good.”

The cost of oversight

Checking work isn’t free. We show exactly what the extra reliability costs, so you can decide how much oversight is worth it.

Reproducible & auditable

Every run can be replayed and every decision inspected. Trust is evidence you can re-run, not a score we hand you.

Runs on your work

The invoice is just the first task. Bring one from your own domain and we measure reliability on the work you actually care about.

This summer, with SPARK

This is a lab the members build together.

The strongest version of this comes from your domains. A reconciliation that has to be exact. A security review where a missed detail is the whole point. An infrastructure check, a research dataset. You bring the task and the right answers; the lab measures which forms of oversight make agents trustworthy on it. The results are something we can publish together.

How the measurement works →Propose a task from your domain