Every service traced from day one, with nothing in your code. When one drifts, Odigos Autofocus captures what the code did while it happens. Your engineers and AI agents open the laptop to the answer.
Each of these becomes a pull request and a wait on the stack you run today. On Odigos, the AI agent gets the answer from live production and moves on to the fix.
The request returned 200. The trace was clean. The value was wrong. An AI agent on Odigos reads what the code did on the failing request, instead of asking someone to add a log line and wait for it to happen again.
Tracing from every service shows where the time goes. When the hot path is inside a service nobody instrumented, the AI agent asks for the calls underneath it and gets them from live production.
Symptoms surface where you look. Causes live where you do not. An AI agent that can follow a request across every service, and read inside any of them, stops at the cause instead of the first thing that looks wrong.
Full distributed tracing with no code changes, across the modern stack and the legacy one, plus the one thing tracing never had: an answer from inside the code.
Distributed tracing across every service and language, and the protocols between them, with no code changes and no redeploys. Context follows the request end to end, even where headers never did. Metrics, traces and logs land in the backends you already run, as OpenTelemetry you own.
Go, Java, Python, Node, Rust, C++, and the stripped, statically linked binaries other tools give up on. Kubernetes, virtual machines and bare metal. Microservices, monoliths and the databases behind them. The legacy estate is where incidents hide, and it is covered on day one.
A trace tells you where the time went. It does not tell you why the discount returned zero. Autofocus senses the drift and captures what the code did on that path, with what inputs, while it is happening. Anything it did not catch, an engineer or an AI agent asks for and gets in seconds. No pull request, no deploy, no waiting for the next occurrence.
Central control, your data on your terms, and capture that moves to the problem on its own. From ten services to tens of thousands without changing how teams work.
One control plane for every fleet. Manage, scale and govern the whole estate without touching application code.
Your telemetry, your rules. Shape every signal in your own cluster and send it anywhere. No vendor owns your data.
It senses where the fire is and looks there first. When a service drifts, capture deepens on that path before anyone asks.
Claude Code, Cursor, Copilot and the agents you build ship faster than anyone can watch. After each deploy they ask the same record what their change did on real requests, so the fix is in the next commit, not the next incident. Same approvals, masking and audit as an engineer.
Odigos runs outside your applications, so depth stops costing you throughput. One Fortune 500 customer benchmarked it against the bytecode agent already in their process, on the same traces, across 1.04 million cores. One of the largest retailers in the world built its own regression-finding AI agent on this data. Security runs on the same sensor and the same install, with no second agent to approve.
// 11 enterprises in production, SOC 2 audited
One record, every purpose. One service, fourteen days, success criteria written first.