Generation brief
Built-in engine
Weave
Create the cases and worlds your benchmark needs—not synthetic volume for its own sake.
Weave turns the situations your benchmark needs to cover into targeted cases, controlled variations, supporting materials, and interactive worlds. Every asset exists to test a known behavior or close a known gap.
Starts withCoverage gaps and construction patterns
CreatesTraceable cases and worlds
Why it exists
Undirected generation makes more data, not necessarily a better benchmark.
Synthetic pipelines are excellent at producing plausible volume and poor at deciding what deserves representation. They repeat common patterns, flatten important context, and miss difficult facet combinations. Weave separates the design of benchmark coverage from the act of generation, so agents create against an explicit need and reviewers can see why each case exists.
InputCoverage gaps and construction patterns
Cases and runnable worlds
Variant and artifact bundle
Reusable outputTraceable cases and worlds
How it works
A controlled path from internal context to development evidence.
Accept an explicit coverage target
Start from approved gaps and Case Construction Patterns rather than an undirected request for more synthetic data.
Generation brief
Construct cases and worlds
Generate situations, inputs, constraints, state, and environmental details needed to exercise the intended behavior.
Cases and runnable worlds
Create meaningful variation
Produce response variants, multimodal artifacts, and challenging facet combinations that reveal behavioral differences.
Variant and artifact bundle
Validate purpose and lineage
Check plausibility, diversity, and traceability so every generated item can be explained by a benchmark need.
Benchmark-ready material
Durable outputs
Not another disconnected dashboard. A reusable correctness asset.
Benchmark cases
Structured cases tied to a specific dimension, topic, risk, or missing combination in the coverage plan.
Response variants
Purposeful candidate outcomes that help elicit judgment, expose tradeoffs, and test rubric sensitivity.
Multimodal artifact bundles
Documents, images, messages, records, and other contextual material required by realistic specialist workflows.
Runnable worlds with lineage
Interactive environments agents can act inside, with their configuration and version connected to the benchmark need they were created to test.
Division of work
Agents scale preparation. People retain judgment and control.
AI engineering team
Contributes
Defines system interfaces, run constraints, data formats, and the behavior each generated asset must exercise.
Gets back
Cases that are usable by the real harness rather than detached synthetic samples.
Domain specialists
Contributes
Validate realism, consequence, and the difficult contextual combinations that generic generation tends to miss.
Gets back
Focused review of representative designs instead of manual case production.
Teammately agents
Contributes
Construct cases, variations, supporting materials, and worlds for specific benchmark gaps, then check whether each one adds meaningful coverage.
Gets back
Targeted expansion of the benchmark at far greater breadth.
Decisions it supports
Questions your team should be able to answer with evidence.
Can we test a critical situation we have never observed?
Create a traceable case for rare, emerging, or deliberately adversarial conditions without waiting for production failure.
Does synthetic material actually add coverage?
Measure every generated case against an identified gap instead of counting volume as progress.
Can an agent be evaluated in a realistic world?
Give it the state, artifacts, tools, and constraints required to reveal its behavior over a trajectory.
Do variations expose the intended judgment boundary?
Create controlled variants that isolate the contextual detail changing the expected decision.
Connected infrastructure
Carry the work forward instead of starting over at every stage.
Generate what the benchmark is missing, with purpose and lineage intact.
Bring one consequential behavior, one expert group, and the development evidence you already have. We’ll help map the correctness workflow around them.