Trial manifest
Built-in engine
Trialground
Test candidate behavior in controlled worlds and keep every result connected to its evidence.
Run prototype harnesses and weights against benchmark cases and worlds in isolated managed environments. Scale comparable trials, capture complete trajectories, and evaluate them with the same expert-grounded standard.
Starts withCandidates, cases, worlds, rubrics
CreatesComparable behavioral evidence
Why it exists
A score without a reproducible run and inspectable trace is weak development evidence.
Agent behavior depends on prompts, tools, state, environment, and the path taken through a task. When each candidate runs differently—or the score becomes detached from the underlying trajectory—teams cannot explain a result or confidently act on it. Trialground makes the run controlled, the evidence inspectable, and the evaluation part of the benchmark record.
InputCandidates, cases, worlds, rubrics
Comparable runs
Evidence bundle
Reusable outputComparable behavioral evidence
How it works
A controlled path from internal context to development evidence.
Package the candidate
Register the model, weights, harness, tools, prompts, and run configuration needed for a reproducible trial.
Trial manifest
Run controlled parallel trials
Execute candidates against the same benchmark cases and worlds in isolated managed environments.
Comparable runs
Capture behavior, not just answers
Retain responses, tool use, trajectories, state changes, adversarial events, and runtime evidence.
Evidence bundle
Evaluate and compare
Apply expert-grounded rubrics, cluster failures, compare candidates, and preserve the evidence behind every conclusion.
Decision-ready report
Durable outputs
Not another disconnected dashboard. A reusable correctness asset.
Trial manifest
A reproducible definition of the candidate, environment, benchmark slice, configuration, and evaluation policy.
Trajectory evidence
The actions, tool calls, intermediate state, responses, and adversarial events behind the final outcome.
Rubric result matrix
Case-level binary judgments and applicability attached to the exact behavior being evaluated.
Candidate comparison
A grounded view of improvements, regressions, failure clusters, and unresolved judgment questions.
Division of work
Agents scale preparation. People retain judgment and control.
AI engineering team
Contributes
Submits candidate systems, defines constraints, inspects traces, and chooses the changes that move forward.
Gets back
Repeatable evidence for model, prompt, harness, and agent decisions.
Domain specialists
Contributes
Review unresolved subjective outcomes and verify where automated judgments do not capture the intended standard.
Gets back
Focused access to the exact cases and traces that need accountable judgment.
Teammately agents
Contributes
Orchestrate parallel runs, apply rubrics, identify failure clusters, and prepare comparisons and expert escalations.
Gets back
Broader trial coverage without losing the evidence needed for human review.
Decisions it supports
Questions your team should be able to answer with evidence.
Which candidate behaves better—and why?
Compare outcomes and trajectories against the same cases, worlds, and approved rubrics.
What regressed while another metric improved?
Preserve case-level behavior so gains do not hide losses in a different context or standard.
Where did the trajectory become unsafe or ineffective?
Inspect actions, state, tool use, and adversarial behavior rather than evaluating only the final response.
Can this result be reproduced?
Keep the candidate, environment, benchmark slice, and evaluation policy attached to every trial.
Connected infrastructure
Carry the work forward instead of starting over at every stage.
Prove candidate behavior before production becomes the test environment.
Bring one consequential behavior, one expert group, and the development evidence you already have. We’ll help map the correctness workflow around them.