Governance
Every agent interaction is tested before deployment, monitored in production, and evaluated continuously so your AI workforce improves without manual oversight.
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Every agent held to the same standard
Define how agents should communicate, what to never say, which tools to use, and in what order, extracted directly from the agent prompt and applied automatically.
Define outcomes agents are accountable for, including resolution rate, containment, escalation conditions, and more, so success is tracked the same way behavior is.
Enrich each northstar with positive and negative examples from production so accuracy always improves over time.
Assign low, medium, or high priority to each rule based on business impact, so your team focuses on what matters most and audit results reflect real operational risk.
Test agents against challenging scenarios pre-production
AI-powered mock users actively attempt to break your agents pre-deployment through detailed scenario generation including prompt injection, topic derailing, and data extraction.
Manually design scenarios to test for a specific agent response against expected behaviors and tool calls to validate edge cases or specific business requirements.
Every real production failure becomes a test case, built directly from live conversation transcripts so resolved issues are automatically validated against every future release.
Catch issues without reviewing every conversation
Live runs are automatically sampled and evaluated against northstars using an AI judge with configurable sampling rates to focus on the sessions that matter most.
Technical workflow failures are automatically captured with error deduplication, occurrence counts, and direct links to affected sessions. Manually flag specific transcript moments with structured issue types.
Every voice session is evaluated for transcription accuracy using Word error rate (WER), Text-to-speech (TTS) quality, conversation flow, acoustic conditions, and end-to-end latency broken down by component.
Every audit, correction, and human feedback feeds back into the system
Thumbs-up or down on any audit result automatically adds it as a calibration example to the relevant northstar so future evaluations continuously reflect real human judgment.
Real-time dashboards track session outcomes, audit pass rates, node error trends, and custom workflow variables with alerts for error rate spikes, audit failure patterns, and usage anomalies baselined against a rolling 12-week window.
Split production traffic between workflow versions, measure impact on defined metrics, and validate prompt changes, tool configurations, or tone variations against real customer interactions before rolling out more broadly.
Governance built into every deployment
Forward Deployed Engineers (FDEs) can accelerate deployments by helping define northstars, build evaluation suites, and configure audits from day one. Unlike other platforms, your team has full access to run, adjust, and own all of it — no black box, no dependency on vendor teams to make changes.
Use intelligence to create and run tests
Describe your agent's goals and the intelligence layer suggests northstar rules to match, extracting behavioral standards and business objectives directly from your operating procedures.
Describe the scenarios you want to test and the intelligence layer builds custom, regression, and adversarial test suites ready to run immediately without manual configuration.
The intelligence layer surfaces audit failures, flags behavioral regressions, and proposes concrete fixes such as a prompt adjustment, a new northstar, or even a regression test to lock in the correction.