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DCL Trust Oracle — Quality & Behavioral Drift Evaluator
x402
DCL Trust Oracle — Quality & Behavioral Drift Evaluator is a paid API for AI agents from bazaar.fronesislabs.com, paid per call via x402, $0.03/call, status unknown (last checked 2026-10-11).
Evaluates AI agent outputs for quality issues, inconsistency, and behavioral drift, returning a verdict with confidence score and drift analysis.
Price / call$0.03
Success—
StatusUnknown
How this service works
Check agent outputs for quality issues, inconsistency, and behavioral drift.
Output
Returns a JSON object containing: a verdict string (pass/fail/review), a confidence score (0-1 float), a drift score, a drift mode classification, a human-readable reason for the verdict, a policy version used for evaluation, an input hash for auditability, a chain index, a transaction hash for payment verification, and a Unix timestamp.
Example use cases
Pre-action drift check for autonomous agent
“Before my agent executes that API call it just decided on, run a quality and behavioral drift check on its output — give me the verdict and confidence score so I can decide whether to proceed.”
Monitoring agent consistency over time
“My AI agent has been running for a few hours — can you evaluate its latest response for behavioral drift and tell me the drift score and mode so I know if it's staying consistent with its earlier behavior?”
Quality gate in multi-agent pipeline
“We're about to pass this agent's output to the next stage in our pipeline — first run it through a quality evaluation and flag any inconsistency or low-confidence verdict before we continue.”
Technical details
Endpoint
POSThttps://bazaar.fronesislabs.com/evaluate/quality?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to specifically evaluate AI agent output for quality degradation and behavioral drift over time, particularly when you require a structured drift score and drift mode classification alongside a verdict. Prefer this over the jailbreak/injection detector (a sibling endpoint) when your concern is output quality and consistency rather than adversarial input threats, and over the high-confidence evaluator when cost efficiency matters more than maximum certainty on lower-stakes decisions.
Side effects
- Missing required 'input' field returns a validation error
- Malformed query parameters may result in a 400 Bad Request
- Service unavailability returns a 5xx error with no verdict
- Payment failure (insufficient USDC) blocks the request via x402 protocol
- Ambiguous or empty agent output may yield low-confidence verdicts
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| agent_id | string | Caller id stored on the audit record. Optional; omitted values are stored as unknown. |
| response | string | Proposed tool call or agent text to check before execution. |
Tags
x402
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