Gym - agent training inside Agent Hive
Describe the job. Gym trains the agent.
Define the capability your agent needs to improve. Gym turns that goal into practice, model training and held-out proof, then prepares the proven version for governed release inside Agent Hive.
Evaluation evidence
Base model versus trained model
Base model
Gym-trained model
Results pending approval
Approved task, model and held-out evaluation methodology will be published here.
Why Gym
Deployment shouldn’t be the finish line.
Operational work changes. Exceptions emerge. The agent that was useful at launch still needs a disciplined way to improve against the work it now faces.
Work changes
New exceptions and operating patterns expose gaps over time.
Prompt changes are not proof
A change needs evidence from comparable work before it returns to operations.
Improvement needs a path
Gym connects a defined goal to training, evaluation and governed release.
The Gym training program
Describe, practice, train, prove, deploy.
Gym carries one defined improvement goal through a repeatable program. The business outcome stays visible; the technical work follows behind it.
- 01
Describe
Tell Gym what the agent needs to get better at.
Start with the operational outcome in plain language. The goal gives the training program a clear target.
Input: one defined improvement goal
- 02
Practice
Build representative work around that goal.
Gym develops practice tasks that move from common work to the harder cases where the agent needs to improve.
Training program: representative practice
- 03
Train
Improve the underlying model.
The model works through the practice, learns from feedback and carries the improvement into the next version.
Change: model weights are updated
- 04
Prove
Test the change on work it never trained on.
Base and trained versions are compared side by side on the same held-out cases, so the difference is measurable.
Evidence: comparable held-out evaluation
- 05
Deploy
Prepare the proven version for governed release.
The evaluation evidence and release decision stay connected to the agent as it moves back into operational use.
Output: a release-ready agent version
Real model training
The improvement is built into the model.
Gym trains the underlying model, not simply another prompt. Practice and feedback inform the next version so the improvement carries into the work the agent is asked to do.
Improvement objective
Define the capability
Give Gym a clear operational outcome to improve.
Gym improvement loop
Practice, training and proof stay connected to the same objective.
Next agent version
Carry the improvement forward
Move a proven version toward the governed deployment path.
Proof before release
Measure it on work it never trained on.
Base and trained versions are evaluated side by side against the same held-out cases. Gym makes the evidence visible before a version moves back into operations.
Same held-out evaluation
The comparison uses work the new version did not train on.
Visible comparison
Base and trained results are reviewed before a release decision.
Results pending approval
Approved metrics and methodology will be added when publication is cleared.
Improvement with control
A better agent still needs a governed release.
Gym prepares the improved version. Agent Hive keeps the evaluation evidence, review decision and deployment controls connected to the work.
Evaluation evidence
A version earns the right to move forward by showing its result.
Human review
An accountable team can inspect the evidence before a consequential release.
Controlled deployment
The release follows the same access and deployment boundaries as the rest of Agent Hive.
One platform
Gym is part of Agent Hive.
Gym improves the agent. The rest of Agent Hive grounds it in enterprise context, puts it to work and keeps the outcome controlled.
01
Enterprise Context
Grounds the agent in the business it serves.
02
Agent Orchestration
Puts the improved agent into coordinated work.
03
Evaluation
Records whether the new version earned its release.
04
Governance
Keeps review and deployment controls in place.
Bring us one agent capability to improve.
We’ll show how Gym could turn the goal into a training, proof and governed-release path inside Agent Hive.