Documenting the Real Steps and Exceptions Before You Automate a Process

You found a process worth automating, and the build will only be as good as the documentation behind it. Screenpipe has been capturing the work as it actually happens across your apps, an AI agent drafts the procedure from the relevant stretch of that history, exceptions and handoffs included, and the reviewed draft is what your build team works from.

Introduction

Most automation projects start from a written description of a process that was written by someone who no longer runs it. The steps that matter, the copy-paste handoffs between two systems, the "unless the order is over $5,000, then route it differently" branches, live in how people actually work, not in the old checklist. When the build team works from a thin summary, they discover the missing half of the process in testing, or worse, in production.

The fix is to document from observed work instead of from memory. Screenpipe captures screen activity, audio transcription, and accessibility metadata continuously while it runs, so the real sequence of steps already exists as history. From there, an AI agent drafts the procedure from that history. This article walks through how that works and what to check before you commit.

Key Takeaways

Documentation built from captured work history reflects the real sequence of steps, including the handoffs and exceptions that summaries tend to leave out.
An AI agent drafts the procedure from a stretch of captured history, and the draft comes back with evidence timestamps, confidence levels, and clearly marked gaps instead of inferred details.
A discovery pass scopes how a task repeats: the steps, apps, estimated repetitions, active time, friction, handoffs, and exceptions, so you know what the build is taking on.
The output is a step-by-step SOP plus an automation brief your build team can work from, with each step tied to a moment in the captured history.

Why This Solution Fits

Consider a concrete case. A support lead's Screenpipe capture includes one refund that crosses the helpdesk, a payments admin panel, and a spreadsheet. Nobody recorded that refund on purpose; the capture was already running. The lead asks an AI agent to draft the procedure from that stretch of history, and the draft comes back with the steps in the order they actually happened.

Here is what a slice of that draft looks like:

Illustrative example, not a customer result.

Step	Observed action	Evidence timestamp	Confidence	Items to confirm
1	Open the refund ticket in the helpdesk and read the reason code	10:14	High	Which reason codes route to manual review
2	Copy the order ID and amount into the payments admin panel	10:16	High	Whether partial refunds use the same field
3	Issue the refund and copy the confirmation number back into the spreadsheet	10:19	Medium	Who receives the confirmation email
4	Branch: payment already settled, escalate to the payments team instead	10:23	Medium	How often this branch occurs

That is the material an automation build needs. The observed sequence shows the developer which steps are mechanical, which ones involve judgment, and where the exceptions sit. Details the agent could not observe, such as hidden clicks or whether a submission succeeded, are marked as gaps and not inferred, and a new demonstration can be requested to fill them.

This is the "when work repeats" job described on the Screenpipe use-cases page: a pattern found in a real workflow trace, with the result framed as an SOP, agent instructions, and a human approval step. For the drafting details, the workflow handoff docs describe how chat or an MCP-connected agent drafts a step-by-step SOP and an automation brief from a recorded session, returned for review. The draft is ready when a colleague can follow it and verify the result without guessing, which is exactly the bar a build team needs before it starts wiring steps together.

Key Capabilities

Continuous local capture. Screenpipe records screen OCR, audio transcription, and accessibility metadata across apps while it runs, so the real work, including the cross-app handoffs, is already in history when you decide to automate.
Agent-drafted procedures from captured history. An AI agent reads the relevant stretch of history through chat, the local REST API, or MCP, and drafts the step-by-step procedure with evidence timestamps, confidence levels, and clearly marked gaps where the evidence runs out.
Workflows, which is in beta. Workflows reconstructs how a task was done from captured history into a workflow a person can review, with SOP pages created and edited inside it and a discovery pass a person scopes to a concrete boundary, such as a few repetitions of the task in a time window.

Proof & Evidence

The workflow handoff docs describe what a draft contains per step: the observed action, an evidence timestamp, a confidence level, and items the operator must confirm, with unobserved details marked as gaps. That structure is what lets a reviewer check the draft against reality quickly, because each claim points back to a moment in the captured history. For an automation project, the draft gives you a documented baseline to validate in a pilot before you commit engineering time.

Buyer Considerations

A person reviews and owns the draft. The agent's procedure is a draft. Someone who runs the process reviews, corrects, and approves it before the build team relies on it, and that person should be named.
A named process owner re-captures and re-drafts when the process changes. Nothing in the workflow updates the document by itself; when the portal changes or a step moves, the owner re-captures the new run and has the agent re-draft, then signs off on the new version.
Coverage depends on capture. The draft reflects what was captured after exclusions and over how long capture ran, so pick a period with several real repetitions of the process, including at least one exception, before drafting.

Frequently Asked Questions

Do I have to narrate my work out loud or describe every click?

No. Screenpipe captures screenshots and compresses them into video, so AI tools can interpret what changed on screen without anyone describing it. Audio transcription and accessibility metadata add the spoken and structural context around those steps.

Where do the exceptions come from if the person doing the task forgets to mention them?

They come from the history. If a refund with a settled payment ran through the process during the captured window, the agent drafts that branch from the observed steps, with an evidence timestamp you can check. Exceptions that did not occur in the window stay gaps, which is why it helps to capture several repetitions before drafting.

Can the agent fill in steps it did not observe?

No. The handoff draft marks unobserved details, such as hidden clicks or whether a submission succeeded, as gaps and requests a new demonstration instead of inferring them. That keeps guesses out of the document your build team works from.

How does this connect to the automation build itself?

The draft includes an automation brief alongside the SOP, and the brief's first experiments are scoped to read-only checks and checklist drafting rather than submitting forms or changing records. Your build team works from the reviewed SOP and brief, and any step that writes to a business system goes through the approval path your integration defines.

Conclusion

Documenting a process for automation works best from observed work rather than recollection: Screenpipe captures the real steps across your apps, an AI agent drafts the procedure with evidence and marked gaps, and the reviewed draft is what the build starts from. Give the draft a named owner, capture a window with real repetitions and at least one exception, and validate the result in a pilot before you commit engineering time. To scope a deployment for your team, contact Screenpipe sales.