AI Governance & Observability
Continuous visibility into what every agent did, why, and whether it stayed inside policy.
What breaks without this
- AI usage often expands faster than an organization's ability to monitor or audit it.
- Model behavior can drift after a provider update, with no automatic detection.
- Compliance and risk teams lack a consistent evidence trail when an AI-assisted decision is questioned.
Monitors AI usage, output quality and policy adherence across the Foundation and Application layers, giving risk, compliance and engineering teams a shared, evidence-based view of AI behavior in production.
What it does
Centralized dashboards for usage, cost, latency and error rates across every registered agent.
Automated evaluation pipelines that score output quality, factual grounding and policy adherence on a sampled or continuous basis.
Drift detection comparing current model output patterns against established baselines.
Red-team and adversarial test suites run on a recurring schedule against production agents.
Immutable audit logs of prompts, outputs, tool calls and human overrides, retained per policy.
From request to result
Instrument
Agents and gateways emit structured telemetry — requests, outputs, tool calls, overrides — to the observability layer.
Evaluate
Sampled interactions are scored against quality, safety and policy rubrics, automatically or with human review.
Detect
Drift and anomaly detection flags deviations from baseline behavior for investigation.
Report
Findings feed governance dashboards and scheduled compliance reporting.
Built to be audited, not just used
- Provides the audit evidence needed to demonstrate AI risk controls to internal risk committees and external regulators.
- Supports model-risk-management frameworks by documenting evaluation methodology and results.
- Findings can trigger automatic throttling or suspension of an agent pending review.
Fits existing infrastructure
Exposed to agents through the Model Context Protocol (MCP) for tool and data access, and Agent-to-Agent (A2A) messaging for multi-agent handoff — so third-party and custom agents can integrate without proprietary connectors.
Outcomes to expect
Qualitative, directional outcomes. We do not publish unverified performance figures — see the case studies section for engagement-specific, authorized results.
- Risk and compliance teams gain continuous, evidence-based oversight instead of point-in-time reviews.
- Model drift and quality regressions are caught before they reach significant scale.
- Audit and regulatory inquiries are answered from existing records rather than reconstructed after the fact.
Works alongside
Enterprise AI Gateway
A single, governed entry point for every model, prompt and AI service used across the organization.
FoundationLLMOps
The lifecycle discipline for evaluating, versioning, deploying and rolling back models and prompts safely.
Security & ComplianceAI Compliance Co-Pilot
Maps obligations to controls, tracks evidence, and flags gaps ahead of an audit or review.
Evaluate AI Governance & Observability for your environment
Talk to an architect about integration into your existing stack, or request a scoped demo against a representative use case.