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AI OBSERVABILITY

AI observability that leads to operational action

AnoSys connects traces, model calls, tool behavior, evals, cost, user experience, and business KPIs so teams can explain production AI behavior with evidence.
Agent tracesEvalsCostBusiness KPIs
AI Observability
Trace TimelineAgent turns, model calls, handoffs, and tool latency in one execution view
48 spans · 6.16s
checkout-agentgpt-4o4.2s
tool.search_inventoryRESTretry · 1.1s
llm.completionresponse312ms
handoff → support-agentowner: CX Ops59ms
Why It Matters

Traditional monitoring sees symptoms

AI systems can return a response while quality drops, cost spikes, tools retry, users abandon flows, or business outcomes regress. Logs and dashboards alone rarely explain why.

AnoSys Answer

Observability plus operational intelligence

AnoSys joins AI telemetry with application, infrastructure, cost, eval, user, and business context so teams can move from observing failures to explaining and fixing them.

What AI observability should include

A complete view of production AI behavior, not a partial dashboard.

01

Trace

Capture prompts, completions, model calls, tool calls, retries, and handoffs.

02

Evaluate

Connect quality, safety, relevance, latency, and policy checks to production behavior.

03

Attribute

Map cost, user impact, SLA risk, and business KPIs to the exact workflow.

04

Act

Route incidents, generate summaries, trigger pipelines, and close the loop.