Agent evaluation systems
Custom tasks, resettable environments, deterministic verifiers, baseline runs and reviewed failure analysis.
Evaluations for agents that take action
Find where your AI agent fails before your users do.
Shapd AI builds realistic evaluation environments, verifiers and expert-reviewed datasets for AI systems that use tools, change state and operate across complex workflows.
- Resettable environments
- Independent verification
- Reviewed model trajectories
Evaluation run Verified evidence
WORKFLOW
Stateful agent operation
Investigate context → choose target → take action → verify final state
Reference behaviorPASS
No actionREJECT
Wrong targetREJECT
Duplicate actionREJECT
Verifier reads resulting stateNot self-reported success
Evidence you can inspect
22Live MCP tools
422/422Environment tests
18Verified stateful scenarios
2/8Passes on a difficult terminal task
Internal Shapd AI demonstrations using synthetic data. Results are scoped to the named environments and model runs; they are not client outcome claims.
Capabilities
Evaluation infrastructure, human judgment and training signal.
Built for agents whose outputs are actions, state changes and multi-step outcomes—not only text.
RL environments and rewards
Executable environments and outcome-grounded reward signals for tool-using, coding and terminal agents.
Expert data operations
Domain-expert task authoring, response evaluation, preference data and quality-controlled production workflows.
Safety and regression testing
Suites that reject no-action, wrong-target, duplicate-action, shortcut and reward-hacking behavior.
Selected work
Proof for both engineering rigor and model difficulty.
We keep these claims separate, so every result says exactly what the evidence supports.
Financial operations through tools
A Stripe-like environment where agents investigate ambiguous context, change financial state and preserve unrelated records.
22tools
422tests
18scenarios
The current baseline passes this pack. We present it as reproducible regression evidence, not frontier difficulty.
Replay a verified runCrash-consistent storage recovery
A coding task where a visible repair can still lose reachable data or publish the wrong generation after an interrupted compaction.
19/19static
28/28task checks
2/8model passes
All eight calibration artifacts were reviewed; the six failed runs were genuine code failures.
Inspect the failed runMethod
Built around the failure that matters.
A useful evaluation begins with the costly mistake to detect—not a generic task count.
Map
Document the workflow, tools, state, dependencies and costly failure.
Define
Specify plausible wrong behavior and the invariants it would break.
Build
Create a resettable environment with representative evidence.
Verify
Measure resulting behavior independently from the agent's report.
Attack
Run no-op, wrong-target, duplicate and adversarial controls.
Explain
Review trajectories and turn failures into the next experiment.
Agent Evaluation Sprint
Start with one important workflow.
A focused pilot turns one production-relevant workflow into a repeatable evaluation and clear failure analysis.
Discuss an evaluation pilotRECOMMENDED INITIAL SCOPE
- 5–10 costly and plausible failure modes
- 10–25 independently verified scenarios
- One resettable environment with tools and state
- Oracle, negative and adversarial controls
- Selected baseline or customer-agent runs
- Failure taxonomy, report and next experiment
Human expertise
Expert judgment where it matters.
Technical, financial, legal, medical, STEM, language and multimodal programs supported by structured authoring, review and quality control.
How our expert network worksFor domain experts
Help shape better AI systems.
Contribute to evaluation, task authoring, technical review and domain-specific AI work through flexible contract opportunities.
Explore expert opportunitiesTell us the agent action you cannot afford to get wrong.
We will help define the smallest evaluation that can reproduce it, measure it and make the result useful.