Enterprise AI Observability | Track LLM Token Usage with Jarvis Registry
As AI agents scale across planning, reasoning, tool calling, and cross-workflow collaboration, token usage becomes harder to monitor and connect to business outcomes. Jarvis Registry introduces LLM token usage tracking that captures consumption across agents, chat, and MCP context automatically, so teams finally know where tokens go, which agents drive usage, and whether the spend justifies the ROI.
Every agent registered in Jarvis Registry has its token usage tracked automatically, no matter how it's invoked, through chat, API, or workflow. In this demo, a Bedrock spending agent is called through a natural-language chat request, and Jarvis Registry captures its LLM token usage in real time, showing a call that consumed 12.8K tokens alongside a full history of prior agent usage.
Token data is exported through standard OpenTelemetry, making it ready to visualize in Grafana, Datadog, New Relic, or any OTEL-compatible platform. Beyond token counts, Jarvis Registry gives engineering teams visibility across the full agentic flow, including tool calls, traces, metrics, authentication activity, latency, and execution status, turning every step of an agent's run into something measurable and accountable.
As AI agents scale across planning, reasoning, and tool calling, token usage becomes harder to monitor and tie to business outcomes. Without visibility, token spend becomes a blind spot in the enterprise AI stack.
Jarvis Registry introduces LLM token usage tracking that captures consumption across agents, chat, and MCP context. Data is collected through standard OpenTelemetry for use in Grafana, Datadog, New Relic, or any OTEL-compatible platform.
Every agent registered in Jarvis has its token usage tracked automatically regardless of how it's invoked, whether through chat, API, or workflow. The demo invokes a Bedrock spending agent as an example.
A user asks Jarvis, from within a tool like Claude, about this week's Amazon Bedrock spending in plain language. Jarvis understands the question, finds the right registered tool, and runs it to return an answer.
As the agent runs, Jarvis Registry captures its LLM token usage in real time. In Grafana, the Bedrock spending agent's call shows 12.8K tokens used, alongside a full record of prior agent usage.
Jarvis Registry tracks more than tokens, covering tool calls, traces, metrics, authentication activity, latency, and execution status. This makes token usage visible, standard, and measurable, helping teams understand cost, ROI, and optimize agent workflows.
