How is this different from observability tools like Datadog, LangSmith, or Fiddler?
Observability tools tell you what happened. Aitonoma governs what happens next. Fiddler monitors model outputs and flags anomalies. LangSmith traces LLM calls for debugging. None of them intercept an AI action before it executes, score its risk, route it for human approval, and then log the approved outcome with a verifiable audit trail. That is the gap Aitonoma fills — and the gap Gartner named "Guardian Agents" in their February 2026 Market Guide.
What AI systems can you connect to?
Any system that takes actions via API — LLM-powered agents, workflow automation tools, clinical AI platforms, customer-facing AI, and custom-built models. Phase 1 maps what you have and configures the connectors.
What if we don't have a complete picture of our AI systems?
That is the standard starting point. The Instrument phase exists to surface what your teams are running that IT doesn't have visibility into. Shadow AI and ungoverned automation are the norm, not the exception.
How does the approval workflow operate?
You define risk thresholds during configuration. Actions below the threshold execute automatically and are logged. Actions above the threshold are held in the approval queue until a designated approver signs off — with a full record either way.
How quickly can we be live?
Two weeks is the standard timeline from kickoff to a running control plane. Week one instruments your systems. Week two configures governance rules. The platform is monitoring and logging from that point forward.
What does the audit trail look like for regulators?
Every action is logged with timestamp, system, action type, risk score, approval status, approver identity, and outcome — with cryptographic attestation. The record is structured for EU AI Act Article 12 (record-keeping for high-risk systems), NIST AI RMF (Manage function), SR 11-7 (banking model risk), NAIC insurance audit requirements, and FDA TPLC documentation for AI/ML medical devices. Export is available in structured formats for regulatory submission.
Why not build this internally?
Most internal teams are focused on building AI systems, not governing them. Aitonoma provides the governance layer as a running platform — configured for your environment, live in two weeks, without diverting engineering resources.
