NEW! Data443 Acquires Vaikora – Real-Time AI Runtime Control & Enforcement for AI Agent
AI observability is the practice of logging and monitoring AI systems after they execute. It captures inputs, outputs, model versions, token usage, latency, errors, tool calls, and user activity, and feeds that telemetry into dashboards, alerting, and downstream security systems. It is complementary to AI runtime control: observability shows what the agent did, runtime control determines what the agent is permitted to do next.
Most AI deployments started with observability tooling because logging is the lowest-effort first step. Teams instrumented their LLM calls, captured traces, set up dashboards, and shipped data into their existing SIEM. The visibility was valuable but it was not enforcement. Observability told the team that an agent had emailed sensitive data to an outside address; it did not stop the email.
In 2026 most enterprises run observability for AI alongside runtime control. The observability layer feeds the policy engine with context (prior actions, user history, anomaly signals) and the policy engine writes decisions back to the observability layer so analysts can see the full action lifecycle.
Observability is post-execution. Pre-execution enforcement is the structural opposite. SIEM platforms ingest AI observability telemetry alongside the rest of the security stack. AI observability is sometimes called AI monitoring or LLM observability.
An observability platform records that a customer support agent processed 4,217 conversations last week, with an average latency of 312ms, a hallucination rate of 1.2 percent, and one outlier conversation that triggered an internal escalation. A second example: an AI security operations agent logs every action it took along with the policy verdict the runtime control layer returned, allowing analysts to reconstruct the agent’s reasoning during incident review.
Yes. Observability gives you visibility into what happened. Runtime control determines what is permitted. Most enterprises run them together; they are different layers of the agent governance stack.
Several. The category includes Helicone, Langfuse, Arize, Datadog LLM Observability, and the observability features in AI gateways like Portkey. Vaikora forwards policy verdicts and action receipts into your existing observability pipeline.
Observability data becomes context for the policy engine. Anomaly signals, prior policy hits, and behavioral baselines can all be inputs to a runtime decision. The flow is bidirectional: observability informs policy, policy decisions land in observability.
Not for security-sensitive workloads. Observability alone gives you a record of what happened, which is necessary for audit but not sufficient for prevention. The cost of a wrong action by an AI agent is realized at execution time, not at log-review time.
Last updated: 2026-05-20.
