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Retrieve and analyze simulation results from a Coval run. Use when user wants to review evaluation outcomes or debug agent behavior.
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SOC 職業分類に基づく
| name | get-results |
| description | Retrieve and analyze simulation results from a Coval run. Use when user wants to review evaluation outcomes or debug agent behavior. |
| argument-hint | [run-id or simulation-id] |
Retrieve results for $ARGUMENTS.
List all simulations for the run:
coval simulations list --run-id <run_id> --format json
Get detailed simulation data:
coval simulations get <simulation_id> --format json
This returns:
For each simulation, extract:
| Field | Description |
|---|---|
status | COMPLETED or FAILED |
test_case_id | Which test case was run |
transcript | Full conversation |
has_audio | Whether audio is available |
error_message | Failure reason (if any) |
## Results for Run <run_id>
| Simulation | Status | Test Case | Audio |
|------------|--------|-----------|-------|
| sim_abc123 | COMPLETED | tc_xyz | Yes |
| sim_def456 | FAILED | tc_uvw | No |
### Failed Simulations
- sim_def456: "Connection timeout"
### View Details
`coval simulations get <sim_id>`
### Download Audio
`coval simulations audio <sim_id> -o output.wav`
List simulations with filters:
coval simulations list --run-id <run_id> --filter "status=FAILED"
coval simulations list --run-id <run_id> --filter "has_audio=true"
End-to-end Coval adversarial / red-team testing workflow. Builds one adversarial test set (~10 bad-actor scenarios, each with an expected-behavior checklist), creates a persistent "Adversarial User" persona and a Composite Evaluation metric that scores each scenario against its own expected behaviors, launches a multi-iteration run against the agent (voice or chat), polls for completion, builds a per-scenario pass/fail scorecard, and creates a saved report grouped by Test Case. Use when a user wants to follow the Adversarial & Red-Team Testing cookbook (https://docs.coval.dev/guides/adversarial-red-team-testing) without doing each step by hand. Triggers: "adversarial test set", "red team my agent", "jailbreak / prompt-injection testing", "test my agent against bad actors".
Analyze a Coval adversarial / red-team testing report and turn it into an agent-hardening plan. Use when a user provides a Coval report URL, report export, run IDs, screenshots, or a per-scenario scorecard from an adversarial sweep and wants evidence-backed next steps such as prompt/guardrail changes, refusal hardening, verification fixes, escalation routing, or expanded attack coverage.
Derive a SET of simulation personas for an agent from product artifacts — backend payloads, UI screenshots, journey/product docs, and sample real user messages — instead of designing one persona by hand. Identifies who actually interacts with the agent and how they behave, then creates the personas via the CLI. Best for text/chat agents and for new agents with no interaction history. Use when the user says "make personas from these screenshots/payloads", "who are my users", "create a set of personas", "derive personas from my product", "build a persona library", or "I have backend data, turn it into personas".
Turn a large dataset (an existing oversized Coval test set, an export of past conversations, or a CSV/JSON of cases) into a small, high-signal Coval test set by removing duplicates, identifying unique scenarios, and selecting a representative, failure-weighted subset — then bulk-loading it with no row cap. Use when the user says "I have thousands of cases", "dedupe my test set", "my test set is too big", "turn this dataset into a test set", "pick representative scenarios", or "my CSV import only kept 10 / uploaded everything".
Analyze a Coval accent testing report from runs across different speaker accents. Use when a user provides a Coval report URL, report export, run IDs, screenshots, or metric summary and wants evidence-backed next steps such as prompt changes, STT/confirmation adjustments, accent-robust routing, or expanded accent coverage.
End-to-end Coval accent testing workflow. Creates one persona per accent (each using a distinct accent voice and mirroring your Standard Customer behavior), launches one run per accent against the same voice agent + test set + metrics, polls for completion, builds a per-persona comparison table from the results, and creates the saved multi-run report (grouped by Persona) via the public API. Use when a user wants to follow the Testing Across Accents cookbook (https://docs.coval.dev/guides/testing-across-accents) without doing each step by hand.