Observability for agent traffic lands

An artificial intelligence agent is less like a single chatbot and more like a tiny call center: one model writes text, another tool fetches data, and an application programming interface sends the result somewhere else. The hard part starts when one customer request turns into 12 hidden steps across three vendors. (modelcontextprotocol.io) (opentelemetry.io) That is why a new software category is forming around observability, which is the practice of watching a system’s traces, errors, latency, and costs while it runs. OpenTelemetry, a widely used monitoring standard, has already added generative artificial intelligence conventions so teams can record token usage and model metadata the same way they already track web services. (opentelemetry.io 1) (opentelemetry.io 2) One new ingredient is the Model Context Protocol, which is an open standard for letting language models call outside tools in a consistent format. In plain English, it is a universal plug shape for things like “search the database” or “create an invoice,” so developers do not have to hand-wire every connection from scratch. (modelcontextprotocol.io 1) (modelcontextprotocol.io 2) Gravitee has been building around that protocol by turning existing Representational State Transfer application programming interfaces into Model Context Protocol servers at the gateway layer from an OpenAPI file. That means a company can expose an old internal service as an agent-ready tool without rewriting the service itself. (documentation.gravitee.io 1) (documentation.gravitee.io 2) Now Gravitee is adding the missing dashboard layer on top of that plumbing. Its April 2026 release shows one view for Model Context Protocol tool usage, one for large language model token consumption and cost, and one for overall agent traffic moving through the gateway. (gravitee.io) (gravitee.io) The practical problem is simple: a team can know an agent answered a user, but still not know which tool call failed, which model burned the budget, or which step added two extra seconds. Gravitee’s new templates are built to break that black box into separate panels for protocol usage, tool distribution, gateway performance, token trends, and request patterns. (gravitee.io) (gravitee.io) That fits a wider shift in the market. Langfuse, one of the better-known specialist companies in this area, now pitches agent observability around tracing, evaluation, and production monitoring for frameworks like LangGraph, OpenAI Agents, CrewAI, and n8n, which shows how fast buyers have moved from “can we build an agent” to “can we operate one.” (langfuse.com) The reason cost shows up next to traffic is that large language models charge by tokens, which are the chunks of text models read and write during a request. If an agent loops through extra tool calls or sends bloated context to a model, the bill rises even when the user sees only one short answer. (opentelemetry.io) (gravitee.io) Gravitee is also tying this to control, not just visibility. Its access management product already treats a Model Context Protocol server as a protected resource secured with OAuth 2.1 and optional fine-grained authorization, so the same company selling the dashboard is also selling the gate in front of the tools. (documentation.gravitee.io) (documentation.gravitee.io) That combination is what enterprises usually buy: first expose an internal service as a tool, then decide who can call it, then watch every call, delay, error, and token charge in one place. Gravitee’s launch is a sign that agent infrastructure is starting to look less like experimental chatbot software and more like ordinary production software with logs, budgets, and dashboards attached. (documentation.gravitee.io) (documentation.gravitee.io) (gravitee.io)