Evaluate AI agents with deterministic checks, semantic judgment, trajectory integrity, security controls and evidence-backed release gates.
A single quality score cannot establish that an AI agent behaved correctly. Shyena separates observable facts, semantic behaviour, execution integrity and security into explicit evaluation layers.
Validate exact facts that should never be delegated to probabilistic judgment: business rules, authorization, required states, tool calls and invariants.
Use LLM-based judgment where interpretation is required, while preserving the input evidence, criterion, score, threshold and reasoning used by the evaluator.
Evaluate whether the system took an acceptable path to the intended outcome, including tool use, routing, handoffs, loops and termination.
Security findings become part of the release evidence chain rather than a detached security report.
It should measure the dimensions relevant to the system: deterministic business correctness, semantic quality, execution integrity, security and the evidence required to support the release decision.
No. Semantic judgment is useful for interpretation, but deterministic facts, execution integrity and security boundaries require explicit controls.
Yes. Evaluation signals can be combined into governed release gates with evidence attached to each decision.
Bring one business journey and see how Shyena maps, tests, evaluates, secures and connects the evidence to release decisions.
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