run 4806 · 33 stepsMatches baseline
Correctness for production agents
Your agents ran 50,000 times last week. Twenty of them need you.
Send us your past runs. We come back with the runs that went wrong, what caused each one, and the fix — tested against your real history before you ship it.
Analysis first · no integration · runtime when you're ready
What you get
Three outcomes, from one export.
20
Runs worth reading, instead of fifty thousand. Ranked, with the failing step named.
4
Fewer engineers tied to customer-specific fixes. We propose the change; your team approves it.
$3–4 → $1
Cost per run, cut by right-sizing models and context — with proof that correctness held.
01 · The problem
Correct is undefined
No benchmark describes an agent built for one customer's edge cases. So nobody can say whether last week's runs were right.
02 · The solution
Build the definition instead
Your successful runs, your engineers' calls, and your downstream outcomes already encode it. We assemble them into one graph and keep it current.
Why we can do this
Your own runs are the only benchmark that matters.
We connect every run, step, prompt, model and outcome into one structure — your correctness graph — so a judgment your team makes once applies everywhere it's relevant.
Built from
Runs you already have
Execution history, clustered by what each run was trying to do.
Labeled by
Your engineers
Confirmed, dismissed, corrected. The only ground truth your workflows will ever have.
Confirmed by
Your systems
Retries, escalations, edits before sending, reopened tickets.
Result
Fewer false flags each month
Thin on day one, specific to you by month three, and not portable to a competitor.
The suite
Find it. Explain it. Prove the fix.
01
We learn what your good runs look like
Past runs go in and get grouped by what each one was trying to do, so nothing is compared against an unrelated workflow.
02
You get a list you can finish
Twenty runs to read instead of fifty thousand, ranked by how far they sit from the graph.
03
Each one comes with a cause and a fix
Context, prompt, model, or step design — with the evidence behind the call. Replay the change against real history before it ships.
04
Next week’s list is shorter
Confirm, dismiss, or correct. Dismissed runs become known-good variants and stop surfacing, so the list shrinks toward the ones that matter.
Day one
Three failures we catch before we know anything about you.
No baseline needed, no semantics, no model in the loop. These are countable from the trajectory, so they show up in the first report.
01
A tool call nobody asked for
The agent invoked something no comparable run has ever called at that step, with nothing upstream licensing it. Usually a prompt that stopped constraining after a model change.
Control: a per-step tool allow-list derived from your own history.
02
The same failure, over and over
Identical tool, identical arguments, identical error — retried until it locked the account or burned the budget. Pure counting, so there is no false-positive tax.
Control: a circuit breaker that halts, rolls back, and escalates.
03
The run that goes nowhere
A step that produces neither an action nor an answer, or a run that never reaches the end state its neighbors reliably reach. It stalls, and nothing errors.
Control: termination checks and a defined exit instead of an open loop.
Independent research puts numbers on these: across 1,600+ annotated traces, step repetition appeared in 15.7% of failures, failure to recognize a termination condition in 12.4%, and disobeying the task specification in 11.8%.
Where this goes
The export is the door, not the room.
Start read-only. Move into the execution path when the findings are worth acting on.
Today · Analysis
Send an export
Findings on your real runs in days. Nothing touches your execution path, so nothing needs a security review.
Then · Runtime
Route through us
Live capture, replay, safe recovery and rollback. Fails open — if we're down, calls go straight to your provider.
Ongoing · Adaptive
Decisions at runtime
Model and context chosen per step from your graph, not frozen at deploy. Your engineer still approves what ships.
Why it compounds
One structure underneath, so the tools get better together.
Drift detection, diagnosis, replay and model routing all read from the same graph — which is why a verdict you give in one place improves the others.
Why not the tools you have
Every layer answers a different question.
| Layer | Question it answers |
|---|---|
| Tracing | What happened |
| Observability | Is the system healthy |
| Evaluation | Did it pass someone's benchmark |
| Guardrails | Did it break a policy |
| SuperCollective | What does correct mean here, and did this run meet it |
FAQ
The questions we get first.
Most of my runs look correct. The problems are edge cases — how would you find those?
Population comparison alone would flag every rare case, so it is never the deciding signal. Structural checks work at n=1 — the output claims a record was updated and there is no write in the trajectory — and so does context integrity, where empty retrieval preceded a confident answer. For a genuinely novel case with nothing comparable and nothing structurally wrong, the honest answer is that your team finds it first; that intervention is exactly the label we capture, so the graph knows next time.
Do I have to route traffic through you?
Not to start. Send an export of past runs and we return findings before anything touches your execution path. Routing through us later unlocks live capture, rollback, and runtime model routing.
How is this different from an eval platform?
Evals score against test sets a person wrote. The graph is built from what your agents actually did and what your engineers actually said about it, so the definition of correct is yours rather than a benchmark's.
What if you flag runs that were fine?
You dismiss them, and the dismissal becomes a known-good variant that stops surfacing. We report our precision on your traffic every month, whether or not it flatters us.
What data do you need?
Execution history for one agent, plus any outcomes you already track — retries, escalations, edits before sending, reopened tickets. Redact whatever you need; the trajectory structure carries most of the signal.
Does it work across model providers?
Yes, and that is the point. A graph spanning providers is what provider-native tooling cannot build by construction.
Send us one week of past runs.
We'll come back with what your successful executions have in common, the few that stopped matching, and what we think changed. If we find nothing, we'll tell you that too.
Start a design partner conversation