How to Separate Active Work From Unattended AI Agent Runtime

An agent session can run for a long time while you are actively directing it, checking its output, working on another task, or away from the computer. Those are different situations. Treating all of them as one block of “work time” makes a record harder to interpret and harder to explain.

The useful question is not whether an agent was running. It is: what was happening around that runtime, what task did it belong to, and what outcome can you actually connect to it?

Separate the observations before making a judgment

Start with four categories. They can overlap within one task, but they should not be silently merged.

• Active work: you are writing, prompting, testing, debugging, planning, or otherwise working directly on the task.
• Agent runtime: the agent is processing a request. This is elapsed session time, not proof of active human effort.
• Waiting or absence: the agent may still be running while you are away, in another application, or attending to an unrelated task.
• Review: you inspect the output, test a change, compare alternatives, correct an error, or decide whether to use the result.

Review deserves its own category because it is often where AI-assisted work becomes understandable. A generated change may be technically complete but unsuitable for the task. Conversely, a short review may confirm that an agent produced a useful starting point. Runtime alone cannot tell you either story.

Keep task outcome separate as well. A session may result in a commit, a reverted change, a useful investigation, an abandoned approach, or an unresolved question. None of those outcomes should be inferred from duration alone.

Read the timeline in context

Review an AI session alongside the surrounding computer-based work and the project context. Begin by identifying the task boundary: what were you trying to change, investigate, or deliver?

Then inspect the period before, during, and after the session. Look for context that helps you form a modest explanation:

1. Before runtime: Was there preparation, such as reading code, defining the task, or writing a prompt?
2. During runtime: Were you continuing to work on the same project, switching to another project, or inactive?
3. After runtime: Did you review output, run tests, make revisions, or connect the session to a commit or task?
4. At the task level: What can you say about the outcome without overstating it?

This sequence avoids a common error: seeing a two-hour AI session and assigning two hours of active developer work to it. The session duration is real evidence of elapsed runtime. It is not, by itself, evidence that you spent every minute actively working, that the task was completed, or that the full interval belongs in a billable total.

A worked example: one task, several kinds of time

Imagine you ask an agent to investigate a failing test suite for a project task. You spend 20 minutes reproducing the failure and preparing the request. The agent then runs for 45 minutes.

During the first 15 minutes of that runtime, you read related code and refine the task notes. For the next 20 minutes, you switch to an unrelated project. In the final 10 minutes, you are away from the computer. When you return, you spend 25 minutes reviewing the proposed changes, running tests, and deciding not to use one part of the output. You keep a smaller fix and commit it.

A careful record might preserve these observations:

• 20 minutes of active investigation before the request.
• 45 minutes of agent runtime.
• 15 minutes of active work alongside the agent.
• 20 minutes that belong to another project, not this task.
• 10 minutes of uncertain or unattended time.
• 25 minutes of review and testing after the response.
• One commit connected to a portion of the work, while another proposed change was not used.

The point is not to force a universal classification of every minute. It is to prevent the 45-minute runtime from swallowing the task context. If you need to discuss the work later, this record explains what happened without claiming more certainty than you have.

Mark uncertainty instead of inventing precision

Some intervals will remain ambiguous. You may see that an agent was active but lack enough context to know whether you were reviewing, waiting, or working elsewhere. Flag those intervals for review rather than assigning them a confident label.

This is especially important when a task spans several tools or projects. A switch away from the agent does not automatically mean idle time. It may be relevant review, parallel work, or an unrelated interruption. The right interpretation depends on the task boundary and the evidence around it.

LogAgent tracks AI-agent session time, token use, cost, and outcomes by tool, project, and task. It also links AI sessions to commits, tasks, projects, and invoice lines. Those connections can give you a stronger review trail, but they do not remove the need for judgment about active effort, waiting, or task outcome.

Before you rely on an AI-assisted work record, use this short checklist:

• Have you separated active work from elapsed agent runtime?
• Did you identify task switches or periods away from the computer?
• Did you review what happened after the agent responded?
• Can you distinguish a commit or other outcome from a session that merely ran?
• Have you marked uncertain intervals instead of forcing an answer?

A fair record preserves the difference between activity, effort, and outcome. That makes it more useful for your own review and more credible when someone needs to understand what the work actually involved.