See agents as agents.

Understand runs, prompts, models, tools, policies, tokens, latency and behavior through an observability layer built for agentic systems.

Infrastructure monitoring wasn't built for agents.

Traditional monitoring tells you whether the infrastructure is healthy. Operating agents means understanding what the agent actually did: which path it took, which tool it called, which rule was evaluated and what it cost. A healthy server can be running an agent that gets things wrong.

Here the trace is not added instrumentation: every agent step already is the span, the event and the checkpoint. One step feeds the trace, the cost ledger, the trail and the memory.

Track the signals that describe agent behavior.

Agents

Which agent ran, on which version and under which owner.

Runs

Every run from start to outcome, with the path it took.

Tasks

The units of work inside a run, including subagents.

Prompts

The exact prompt that was sent, not the template that produced it.

Models

Which model responded, with which parameters and at what cost.

Tools

Which calls went out, with which arguments, and what came back.

Policies

Which rules were evaluated and which one blocked.

Tokens

Consumption per run, agent, team and environment.

Latency

Where the time went, step by step, not just in total.

Outcomes

The outcome of the run, and whether it delivered what was asked.

Waits

Every pending wait, for a document, a callback or an approval, visible in the console. The wait nobody sees is the 3 a.m. incident.

A fleet, not a pilot.

Runs, cost and latency together

Active runs, success rate, p99 and token spend per agent and per tenant. Telemetry tied to money, not just logs.

Fleet health per agent

A sick agent shows up in its error rate and cost before it becomes an incident, with incidents and budget alerts next to the metrics.

Approvals are an operational signal

Pending four-eyes requests are counted alongside the metrics. Governing is part of running the fleet.

The run that ended well and cost ten times more.

01

The agent returned the right answer, and as far as traditional monitoring is concerned, all is well.

02

Along the way it ran the same search eleven times.

03

And escalated to an expensive model for a trivial task.

04

Seeing agents as agents means treating the run as the unit. That's where 'why was this so expensive?' gets an answer.

Knowing something happened isn't enough. Understand what the agent did.

The AgentOS for regulated operations.

An Agent Kernel that schedules, isolates, persists, protects and accounts for every agent running on it, at massive scale, including in the institution's own cloud.

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