AI observability is the live view of what every assistant, agent, and model call is doing: who triggered it, which model ran, what data or service it reached, and what it cost. For company AI, logs are not enough. You need cost and reach attributed to the actor behind each call, so adoption can keep moving without turning into an opaque bill or an unknown access path.
See it on a live graph — no signup →The important moments are where AI crosses a boundary: an LLM call, a database read, an external API, or an agent handoff. Latten records those crossings (each time your AI touches data, a model, or a tool) and renders them as one live authority graph.
A token total is only a bill. Latten attributes input, output, cached, and reasoning tokens to the actor, model, and data domain behind the call, so cost has an owner.
Architecture diagrams drift. Latten maps reach from the traffic your AI actually generates, read-only and out of the request path.
No. LLM monitoring usually focuses on prompts, latency, and token usage. Company AI observability also shows who acted and what company systems the AI reached.
No. It is observe-only and reads traffic without changing how the app behaves.
Yes. Use @latten/sdk directly or the @latten/installer MCP to place the SDK in a reviewable pull request.