Every request returned 200 OK, the task still failed. This is the problem in one sentence. A single user intent now fans out into multiple model calls, tool invocations, MCP (Model Context Protocol) interactions, policy executions, retries, and agent handoffs. Each call can look healthy on its own while the task as a whole hallucinates, times out, or quietly drains your budget. Request-level monitoring can't tell you which, because the answer isn't in any single request.
**AI observability is the practice of tracing a full AI task, from first prompt to final answer, across every model, tool, and agent involved, and measuring what it cost and how long it took.**
A call comes in, a response goes out, and you measure status, latency, and error rate. That works because "did the service do its job" maps cleanly onto one exchange.
AI goes beyond the simplistic and requires a deeper perspective.
Where API observability tells you whether your infrastructure responded. AI observability tells you whether the task worked, what it cost, and which model, agent, or tool made the difference. Same discipline, different unit of measurement.
The session is the new unit of observability. A session is the full journey behind one outcome. A turn is one exchange within it. See the session and you can see why a task was slow, wrong, or expensive.
Platform teams are the right owners for this. You run the path every AI call takes, so that path is where you see what those calls add up to.
Agent observability and LLM (large language model) observability share a core set of signals. With signals that need to be aggregated out of headers, payloads, security tokens and then correlated across the session, not just captured per call.
Kong AI Observability traces the full journey through models, tools, agents, and policies, directly in the connectivity path. No assembling telemetry across separate systems and providers. Watch the overview, then read on for the highlights.
The Overview shows your entire AI estate. See top consumers, performance, and cost, and break down traffic and spend by model, agent, MCP tool, team, or environment. LLM usage, agentic activity, and MCP interactions show up together.
Prebuilt dashboards give you instant visibility into key metrics, consumer behavior, and performance. When you need something specific, add custom widgets and organize by total spend, request count, or input and output tokens. Group and filter by user, team, environment, application, or business unit. Runaway bills and latency spikes show up before they become incidents.
Share dashboards and insights with specific people or teams. Role-based access control (RBAC) decides who sees what, so cost and performance data stays protected while developers still get what they need to tune their applications.
**Answer one-off questions with Explorer**
Sometimes you don't need a dashboard. Explorer lets you spin up a single visualization and slice telemetry by dimensions like application, behavior, or cache status. Export the result to share, or add it to an existing dashboard when it's worth keeping.
Search and filter connectivity logs, and toggle between a condensed summary for scanning and the raw data view for troubleshooting. Every call that runs through Kong lands in one place. Nobody reconstructs a conversation by hand.
**Trace the session, turn by turn (Coming Soon)**
This is where Kong AI Observability goes furthest. A single conversation can touch multiple models, tools, and agents before it produces an answer. Kong traces it as one session, so you see the whole path. Drop into the turn where something went wrong and inspect the inputs, outputs, token consumption, cost, and latency, plus the sequence of interactions that produced the result, from policy checks to tool calls.
**Keep your existing stack**
Kong AI Gateway also exports AI telemetry through OpenTelemetry and metrics exporters, so the same data can feed the observability tools your team already runs. See the[ _Kong AI Gateway docs_](https://developer.konghq.com/ai-gateway/) _Kong AI Gateway docs_ and how to[ _monitor MCP traffic with OpenTelemetry_](https://developer.konghq.com/ai-gateway/monitor-mcp-traffic-with-otel/) _monitor MCP traffic with OpenTelemetry_.
Session tracing connects to Kong's AI Cost Management capabilities. See how tokens and latency accumulate across a task, then control where the spend goes. For the finance side of the conversation, read the guide to[ _AI showback and chargeback_](https://konghq.com/blog/enterprise/llm-cost-management-ai-showback-and-chargeback) _AI showback and chargeback_.
See what happened across every model, tool, and agent, and fix it at the turn where it broke.
[ Talk to your Kong team](https://konghq.com/contact-sales) Talk to your Kong team
**What is AI observability?**
AI observability is the practice of tracing an AI task end to end and measuring its quality, cost, and performance. It adds sessions, tokens, and tool activity to the logs, metrics, and traces you already collect. For a longer primer, read[ _What is AI observability?_](https://konghq.com/blog/learning-center/guide-to-ai-observability) _What is AI observability?_
**How is AI observability different from API observability?**
API observability measures individual requests. AI observability measures the session, the full journey behind one outcome. A request can succeed while the session fails, so you need both views.
**What is session-level tracing?**
Session-level tracing follows one conversation or task across every model call, tool call, policy, retry, and agent handoff. You can drill from your whole AI estate into one session, then into the exact turn where something went wrong.
**Does Kong AI Observability cover MCP tools?**
Yes. Dashboards break down traffic and spend by MCP tool, and session traces include the tool calls behind each result. See how to[ _monitor MCP traffic with OpenTelemetry_](https://developer.konghq.com/ai-gateway/monitor-mcp-traffic-with-otel/) _monitor MCP traffic with OpenTelemetry_.
When an AI application fails, knowing that an individual model call returned a 200 isn’t enough. You need to know what led to it. Kong stitches the interactions associated with an AI session together, giving developers a view across the sequence of
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