The Course
The sim puts you on a real hole. Your environment puts the agent on a real case from your queue.
Hookshot™ Environments
Every agent needs a practice round
A golf simulator puts you on a real hole and shows exactly where the ball landed.An environment does the same for one job in your company.Score today's agents, then train one that beats them.
How It Works
Every attempt gets scored.
Every score makes the next one better.
Score the agents you're running today.
Train a better one once the score holds up.
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Environment
We wrap one job in a practice setup, so the agent sees the same cases and can take the same actions your team does.
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Verifiers
Multiple checks can score a single attempt. If your team looks at the outcome, the steps, and the format, so does the environment.
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Score
Point the agents you already run at it. You get a straight answer on which ones do the job and where they fail.
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Train
Every attempt comes back scored. The model practices on those scores and improves. That's reinforcement learning.
Scroll or drag horizontally, or use the arrow keys, to view all four steps.
Use Cases
Where an environment pays off
The judgment calls your people already make.
We turn those into verifiers: checks for the outcome, the steps, and the format.
Document Review
Your reviewer reads a packet and decides: approve, flag, or escalate.
The agent reads the packet like your team does. It makes the call, writes the summary, and gets scored on all three. Just like a human reviewer would be.
- Outcome Was the claim decision correct?
- Process Did it cite the right sections?
- Format Is the summary in the template?
Why Hookshot™ Environments
A golf sim only helps because every practice shot gets objective feedback.
Take the measurement away and LLM performance is just anecdotal evidence.
The Sensor
The sim measures exactly where the ball landed. A verifier measures exactly how the agent's answer landed.
A Thousand Swings
Nobody gets better off one shot. Cheap, measured attempts are what make practice pay.
Who This Is For
Start here if the work takes judgment.
Three things make this work: a scoped workflow (one job or a connected set of jobs), real cases, and someone who knows a good outcome when they see it.
Fit
- One job you can point at: a review, a routing call, a path through internal software
- Real cases, with the outcome your team landed on
- Someone who can say whether an answer was right
- Willing to read the verifiers before trusting the score
Not a Fit
- No record of what a good outcome looked like
- The work runs on a timer or a fixed rule, with no judgment in it
- Wants a generic score, not a number tied to real checks on real work
- Only a handful of cases, not enough to train against
FAQ
Common Questions.
A practice setup for a scoped workflow: one job, multiple jobs, or a connected sequence. The agent takes actions on real cases, and verifiers say whether they were right. Use that to score the agents you run today, then train a better one.
Multiple checks that decide if an attempt was right. You might check the outcome, the steps, and the format. Together they score it the way your team would.
No. This work stays inside your company. Hookshot™ Data is a separate licensing product if you ever want records to leave.
No. Your environment and your verifiers stay yours. There is nothing public to join.
It's expensive, slow, and you can't verify it on your cases. Like using a laser cutter on a cake. You need a butter knife instead: a small model trained on your work and scored by your verifiers.
In the environment while it practices. Once the score holds up, you can serve it from an API or run it in Hookshot™ Proteges under your approval rules.
Talk to our sales representative to get a personalized quote.
Guide
How to Train Agents on Real Work
Reinforcement learning with verifiable rewards (RLVR) trains agents by scoring real attempts against a check you can defend.
9 min read Read the Guide
Contact
Start with a scoping call.
We look at where the agent would run and what a verifier could score.
Book a call
A look at the systems your agent can reach. We come back with what would hold up as a grade.
Write us
Legal or IT questions that should start in writing.