Your AI agents are processing applications and recommending decisions at enterprise scale. Agent Observability makes every action visible, every decision auditable, and every behaviour accountable.
Without governed agent observability, these problems compound silently with every new data source and AI deployment.
Agents are operational but their reasoning is not visible. What data did the agent consume? What confidence did it assign? Invisible between input and output.
An agent at 95% accuracy at launch may silently decline to 80% over time. The enterprise discovers degradation only when consequences have materialised.
What each agent costs per decision, per workflow, per day — data consumed, compute used, API calls made — remains an unanswered question.
Every agent registered with observability framework — decision logging enabled from deployment
Continuous monitoring of decision patterns, data consumption, and resource utilisation
Normal behaviour established — decision rate, confidence distribution, data access patterns
Deviations from baseline flagged — before consequences reach the business
Full decision log available for regulatory examination — agent action to data source, reconstructable
Every agent decision logged — what data was consumed, what confidence assigned, what reasoning followed, what output produced.
Continuous comparison against deployment baseline — drift detected before consequences materialise, not after.
Data accessed, compute consumed, tokens used, API calls made — per agent, per decision, per workflow. Cost and resource visibility.
Agent observability linked to data lineage — trace any agent decision back through its data consumption to the source system that informed it.
See how Agent Observability works within the Tantor governed intelligence platform.