ReliAgent intercepts and validates AI agent tool-call responses, running nine independent detectors against each call: sycophantic gap-filling, hallucination, context degradation, repetition loops, confidence collapse, parameter drift, timeout, schema violation, and response-contract integrity. When a supplied schema relies on a JSON Schema keyword outside the supported subset, ReliAgent returns an honest partial-validation finding instead of a silent pass. Returns a structured reliability report with per-detector confidence scores and remediation guidance.
HDGForge develops ReliAgent, a tool designed to intercept and validate AI agent tool-call responses by running nine independent detectors to identify issues such as hallucinations, sycophantic gap-filling, context degradation, and schema violations. The company focuses on ensuring reliability and integrity in AI agent operations by providing structured validation reports with per-detector confidence scores and remediation guidance. ReliAgent distinguishes itself by returning honest partial-validation findings when schemas use unsupported JSON Schema keywords, rather than silently passing validation. The platform serves organizations looking to improve the safety and reliability of their AI agent deployments.
