How Employees Review and Edit a Generated Procedure Before It Reaches the Team

When a procedure is drafted for your team, the person who runs the process should get the final say. Screenpipe has been capturing the work while it runs, an AI agent drafts the procedure from the relevant stretch of that history, and the draft lands on editable pages the employee can correct before it is shared.

Introduction

A procedure written from memory tends to miss the handoffs: the copy-paste between the helpdesk and the spreadsheet, the field everyone forgets, the exception that happens twice a week. A procedure drafted by an AI agent from real captured history picks up those details, but a draft is still a draft. Before it goes to the rest of the team, the person who actually runs the process needs to read it, correct it, and strip out anything that should not be shared.

That review step is where Screenpipe focuses. Screenpipe is a local-first platform that captures cross-application work history and makes it available to AI agents, and its documented workflow for procedures is explicit about who does what: the agent drafts, the employee owns the result. This article walks through how that review and editing works, using one concrete example.

Key Takeaways

Screenpipe has been capturing work while it runs, so the raw material for a procedure already exists when someone asks for it.
An AI agent drafts the procedure from the relevant stretch of captured history, and the draft shows what the agent observed and where it could not see.
A discovery pass can find the repeated task first, scoping it to a few repetitions in a time window.
The draft is a document the employee edits and maintains, not a record that maintains itself.
The output is a step list the reviewer can check against the captured session, step by step.

Why This Solution Fits

Consider a support lead whose Screenpipe capture includes one refund that crosses the helpdesk, a payments admin panel, and a spreadsheet. Nobody started a recording for this. Screenpipe has been capturing in the background, so the full run is already in history.

The lead asks an AI agent to draft the procedure from that stretch of history. The agent reads the captured session and produces a step-by-step draft, including the copy-paste handoffs between the three systems. Per step, the draft lists the observed action, an evidence timestamp, a confidence level, and items the operator must confirm. Details the capture could not see, such as hidden clicks or whether a submission succeeded, are marked as gaps rather than guessed.

The draft the lead receives looks like this (illustrative example, not a customer result):

Step	Observed action	Evidence timestamp	Confidence	Items to confirm
1	Open refund ticket in helpdesk	10:14:02	High	None
2	Copy order ID into payments admin panel	10:15:37	High	None
3	Select partial refund, enter amount	10:17:10	Medium	Whether the amount matched the policy cap
4	Log refund in spreadsheet	10:19:44	High	None
5	Send confirmation email	Gap	Gap	Whether a template was used

The lead then edits the draft: fixing a step order, adding the exception the agent could not observe, removing anything sensitive. When the draft is something a colleague can follow and verify without guessing, the lead posts it to the team. That is the loop: capture that already happened, an agent draft grounded in available evidence and clearly marked gaps, and a named person who decides what the team finally sees. The workflow handoff docs describe this draft format, including the evidence timestamps and confidence levels per step.

For teams that want to find which repeated task to document first, a discovery pass scopes the search to a concrete boundary, such as two or three repetitions of a task in a time window, and produces a report the user reviews. In the Screenpipe app, Workflows, which is in beta, reconstructs how a task was done from captured history into a workflow a person can review, with SOP pages that can be created and edited inside it and steps edited inline. A saved workflow notifies the user when it is ready for review.

Key Capabilities

Continuous local capture. Screenpipe records screen OCR, audio transcription, and accessibility metadata across apps while it runs, subject to the apps and windows a user or admin excludes, so the history behind a procedure exists before anyone asks for it.
Agent drafting from captured history. An AI agent reads the relevant stretch of history and drafts the procedure from what was actually done, not from an interview held after the fact.
Workflows. In the Screenpipe app, Workflows reconstructs how a task was done from captured history into a workflow a person can review; SOPs can be created and edited as editable pages inside it, steps can be edited inline, and a saved workflow notifies the user when it is ready for review.
Evidence and gaps in the draft. Handoff drafts return available evidence and clearly marked gaps per step, so the reviewer knows which steps are confirmed by capture and which need a human answer.

Proof & Evidence

The Screenpipe use-cases page describes the "when work repeats" job: a pattern found in a real workflow trace, with the result framed as an SOP, agent instructions, and a human approval step. The workflow discovery docs describe the discovery pass that feeds this loop, including how a person scopes it and what the resulting report covers. In the support-lead example, the proof is in the draft itself: each step carries an evidence timestamp the reviewer can check against the captured session, and anything the agent could not observe is flagged as a gap.

Buyer Considerations

A person reviews and owns the draft. The agent's output is a draft; the employee who runs the process reviews, edits, and redacts it before sharing, and owns the result afterward.
A named process owner re-captures and re-drafts when the process changes. Screenpipe has no documented feature that notices a process changed. The owner learns about the change from the people who run the process, then re-captures and re-drafts the update.
Plan the rollout conversation. For team-level use, central configuration and shared storage are part of enterprise rollout, and it is worth scoping the setup with Screenpipe sales before deploying.

Frequently Asked Questions

Do employees have to narrate their work out loud for the procedure to be accurate?

No. Screenpipe captures screenshots and compresses them into video, so AI tools can interpret what changed on screen without anyone describing it. The agent drafts from the captured session itself, and the employee's job is to correct the draft, not to re-explain every click.

Where does the review and editing actually happen?

Inside the Screenpipe app for Workflows, where SOPs are editable pages and steps can be edited inline, and in whatever document the agent returns for chat-based drafting. Both paths return a draft for the team's review step.

Can the draft include things the capture did not see?

The handoff draft marks unobserved details, such as hidden clicks or field values, as gaps rather than inferring them, and it requests a new demonstration for those steps. The reviewer fills the gaps or confirms the items the draft flags.

Is Workflows a finished feature?

Workflows is in beta, so its scope and behavior may change. It reconstructs a task from captured history into a reviewable workflow, with SOP pages that can be edited inside it.

Conclusion

For procedures that need an employee's sign-off before they reach the team, Screenpipe covers the full loop: continuous local capture supplies the history, an AI agent drafts the procedure from the relevant stretch of it, Workflows presents the result as a reviewable workflow with editable SOP pages, and the draft is reviewed before the team adopts it. If you want to see how this fits your team's processes, contact Screenpipe sales to scope a rollout.