Tool and MCP agents
Multi-step workflows involving APIs, business objects, permissions, dependencies and consequential actions.
Agent evaluations
Evaluation systems for agents that use tools.
Static question-and-answer tests are not enough when an AI agent searches records, calls APIs, edits files or changes operational state. We evaluate the action, resulting state and invariants that must remain true.
What we build
Complete evidence, not only a score.
A Shapd AI result links the task, environment version, agent configuration, trajectory, final state, verifier result and reviewed failure category.
Realistic, resettable environments
Tasks derived from costly and plausible failures
Deterministic or independently grounded verifiers
Oracle, no-op, wrong-target and adversarial controls
Baseline model and customer-agent runs
Reviewed trajectories and failure taxonomies
Technical reports and prioritized next experiments
Evaluation types
Built for the environment your agent actually operates in.
Terminal and coding agents
Repository and systems tasks where correctness depends on discovery, execution and preservation of deeper invariants.
RL environments
Executable task worlds with stable transitions, reset behavior and outcome-grounded reward signals.
Human evaluation programs
Expert-authored tasks and rubrics, response review, preference collection and targeted error analysis.
Initial engagement
Start with one workflow where a wrong action is expensive.
We will propose the smallest useful evaluation, the evidence required to verify it and a delivery model that fits your infrastructure.
Discuss an evaluation pilot