One platform. Every layer of AI system quality.
Shyena is built to evaluate any AI system — live today for conversational and voice AI, covering the full stack from the personas that generate the test signal, to the execution that keeps it real, to the verdict that can't be gamed. RAG is next; see what's already built for it below.
Capability 01
Agentic Test Personas
Tests are written as a user goal, a persona, and a behavioral playbook. The executor improvises turn-by-turn, choosing the next message based on what the agent actually said — not a fixed script.
- Goal-driven scenarios, not brittle click paths
- Personas carry emotion, language, and intent
- Playbooks adapt when the agent goes off-script
Capability 02
Real Conversation Execution & Dynamic Generation
A real browser or voice session drives your production agent over the same channel your customers use — and the conversation itself is generated live, turn by turn. Within the goal and persona you define, the executor decides what the simulated customer says next based on how the agent actually responded, not a fixed script.
- Live agent, real channel — no mocked APIs, no fake states
- Every turn generated in response to the agent's actual reply
- Retry and backpressure built in
- Captures full transcript and metadata
Capability 03
LLM-as-Judge Evaluation
Every turn and the full conversation are scored against your quality pillars by an LLM judge. The reasoning is stored alongside the score, so you can debug a verdict instead of debating it.
- Turn-level and full-run scoring
- Customizable quality pillars
- Reasoning attached to every score
Capability 04
Deterministic Assertion Contracts
Hard-fact checks that must be true regardless of how friendly the conversation felt. Expected fields, exact values, state transitions, and policy clauses are validated deterministically.
- Field presence and value matching
- State machine transitions
- Redaction and compliance checks
Capability 05
Execution-Integrity Hard Gate
A failed, truncated, or timed-out conversation is capped at FAIL no matter how well it scored on the turns it completed. The raw score stays visible for diagnosis, but it can never be reported as a pass.
- Integrity evaluated before quality
- Truncated runs cannot pass
- Raw score preserved for debugging
Capability 06
Full Audit Trail
Every LLM call, assertion, retry, and judge decision is logged and retrievable. A verdict is always explainable, never a black box, and ready for a release review or audit.
- Full prompt and response history
- Retrievable by run, turn, and verdict
- Exportable for compliance reviews
On the roadmap
What's coming next
Test specs are hand-written today, deliberately — an agent's behavior is too important to hand entirely to a generator. Here's what we're building to make writing them faster without giving that up.
Automatic scenario generation
Generating candidate test scenarios and personas from your agent's real conversation logs, for your team to review and approve before they run — human sign-off stays in the loop, but the first draft won't be a blank page.
RAG evaluation
The same LLM-judge model already includes five RAG-specific dimensions — faithfulness, retrieval quality, calibration, multi-document coherence, and answer completeness. We're finishing the direct-API execution path so you can run them against a RAG pipeline without a live conversational UI.
Built for scale
Evaluation infrastructure that stays reliable under load.
A quality platform is only useful if it runs consistently across thousands of conversations. Shyena's job architecture is designed for large regression suites without overwhelming the agent under test.
Retries & resilience
Transient failures are retried with exponential backoff and circuit-breaker logic. Runs that cannot succeed are routed to a dead-letter queue for inspection, not silently dropped.
Backpressure-controlled concurrency
The runner adapts its concurrency to the target environment's latency and rate limits, so large regression suites don't overwhelm your agent or infrastructure.
Observable job architecture
Every job, worker, and queue is observable by design. You can trace a run from schedule to verdict, identify bottlenecks, and tune throughput without guessing.
See it evaluate your own agent
Bring one real scenario. We'll run it against your live conversational AI agent and walk through every judged turn with you.