Governance
Every agent interaction is tested before deployment, monitored in production, and evaluated continuously so your AI workforce improves without manual oversight.
Every agent held to the same standard
Every agent held to the same standard
Behavioral standards, made machine-checkable
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.
Business objectives, made measurable
Define outcomes agents are accountable for, including resolution rate, containment, escalation conditions, and more, so success is tracked the same way behavior is.
Calibrated with real examples
Enrich each northstar with positive and negative examples from production so accuracy always improves over time.
Priority-driven governance
Assign low, medium, or high priority to each rule based on business impact, so audit results reflect real operational risk.
Test agents against challenging scenarios pre-production
Test agents against challenging scenarios pre-production
Adversarial tests
AI-powered mock users actively attempt to break your agents pre-deployment through detailed scenario generation including prompt injection, topic derailing, and data extraction.
Custom tests
Manually design scenarios to test for a specific agent response against expected behaviors and tool calls to validate edge cases or specific business requirements.
Regression tests
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
Catch issues without reviewing every conversation
Behavioral audits
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.
Node error tracking and manual flags
Technical workflow failures are automatically captured with error deduplication, occurrence counts, and direct links to affected sessions.
Audio quality monitoring
Every voice session is evaluated for transcription accuracy using Word error rate (WER), Text-to-speech (TTS) quality, conversation flow, acoustic conditions, and latency.
Every audit, correction, and human feedback feeds back into the system
Every audit, correction, and human feedback feeds back into the system
Closed-loop feedback
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.
Observability & alerting
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 12-week window.
A/B testing across versions
Split production traffic between workflow versions, measure impact on defined metrics, and validate prompt changes, tool configurations, or tone variations against real interactions before rolling out 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
Use intelligence to create and run tests
Connect your systems
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.
Generate and run tests automatically
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.
Turn issues into improvements
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.
