Branch objective
Optional add-on
Coevolve
Explore stronger directions in parallel—and return newly discovered gaps to the people who can resolve them.
Coevolve lets evolutionary agents pursue competing harness hypotheses, test candidates against your benchmark, and continue from stronger branches. It connects coding agents to domain experts through the correctness questions development actually uncovers.
Starts withObjectives, constraints, benchmark evidence
CreatesStronger branches and patch guidance
Why it exists
Single-threaded iteration makes one assumption at a time—and often optimizes the wrong thing.
A team can spend weeks tuning one prompt or harness direction without knowing whether a different assumption would perform better. Ungrounded coding agents compound the problem by patching toward anecdotes or aggregate scores. Coevolve explores multiple branches while keeping selection tied to cases, traces, rubrics, and the specialist judgment behind them.
InputObjectives, constraints, benchmark evidence
Hypothesis tree
Branch scorecards
Reusable outputStronger branches and patch guidance
How it works
A controlled path from internal context to development evidence.
Define the improvement objective
Choose the benchmark slice, behavioral target, constraints, and acceptable change surface for an evolutionary run.
Branch objective
Spawn competing hypotheses
Evolutionary agents pursue different prompt, harness, tool, policy, or implementation assumptions in parallel.
Hypothesis tree
Test every branch
Run candidates through Trialground and compare them against the same expert-grounded benchmark evidence.
Branch scorecards
Continue, patch, or ask
Preserve stronger branches, direct coding agents to grounded failure points, and return missing correctness to experts.
Next-best development path
Durable outputs
Not another disconnected dashboard. A reusable correctness asset.
Hypothesis tree
A traceable map of the assumptions, candidate changes, and branches explored rather than a single opaque iteration history.
Branch scorecards
Comparable benchmark evidence showing where each direction improves, regresses, or reveals unresolved behavior.
Patch guidance
Failure-grounded instructions that help coding agents focus on the part of the harness most likely to matter next.
Expert opportunity queue
New coverage gaps and correctness questions discovered during iteration, routed back for focused specialist input.
Division of work
Agents scale preparation. People retain judgment and control.
AI engineering team
Contributes
Sets objectives and constraints, reviews branch evidence, and decides which candidate changes are acceptable to adopt.
Gets back
Broader exploration without surrendering architectural or release control.
Domain specialists
Contributes
Resolve newly surfaced correctness questions and validate behavior when branches expose an unmodeled distinction.
Gets back
Small, high-value requests tied directly to a development decision.
Evolutionary agents
Contributes
Generate hypotheses, create candidates, run controlled trials, preserve stronger branches, and synthesize grounded patch directions.
Gets back
Parallel exploration that remains attached to the benchmark rather than optimizing a detached score.
Decisions it supports
Questions your team should be able to answer with evidence.
Which development direction is worth pursuing?
Compare competing hypotheses with the behavior and evidence that matters to your team.
What failure persists across very different branches?
Identify structural weaknesses rather than repeatedly tuning around one local symptom.
Which missing standard blocks confident selection?
Turn ambiguous branch results into a focused correctness question for the right specialist.
Where should the coding agent patch next?
Connect failure clusters and traces to a specific component, assumption, or behavior to investigate.
Connected infrastructure
Carry the work forward instead of starting over at every stage.
Give every improvement branch a benchmark—and every benchmark gap a path back to experts.
Bring one consequential behavior, one expert group, and the development evidence you already have. We’ll help map the correctness workflow around them.