Platforms That Draft AI Agent Instructions From How Your Team Actually Works

The instructions an AI agent needs are already sitting in the work your team does on screen. Screenpipe captures that work continuously across apps, an AI agent drafts the procedure from the relevant stretch of captured history, and your team reviews the draft before anyone follows it. A discovery pass can also surface which repeated tasks are worth documenting first.

Introduction

Most teams carry procedures that were never written down: the refund that crosses three systems, the monthly reconciliation, the checklist one specialist keeps in their head. When you hand that work to an AI agent, the agent needs instructions that match the real sequence of clicks, copy-paste handoffs, and judgment calls, not an idealized summary written from memory after the fact.

Screenpipe is a local-first platform that turns computer history into context AI agents can use. Because it has been capturing screen activity, audio, and accessibility metadata while your team works, the history behind those instructions already exists. An agent drafts the procedure from the captured history. This article explains how that drafting loop works, what the platform does well, and what to check before you buy. The short version: the capture already happened while the work was done, the agent does the writing, and the judgment about whether the procedure is right stays with the people who run the process.

Key Takeaways

Screenpipe captures work continuously across apps, so the history behind a procedure exists without anyone narrating or recording a session on purpose.
An AI agent drafts SOPs and agent instructions from a stretch of captured history.
A discovery pass can identify repeated, cross-app tasks worth documenting, with estimated repetitions and active time from a small observed sample.
Drafts come back with available evidence and clearly marked gaps, so you can see which steps are confirmed and which need a human to fill in.
The output is a step-by-step SOP plus an automation brief, ready for the team to check before it is shared or automated.

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. An agent drafts the steps from that stretch of history, including the copy-paste handoffs between systems, and the lead edits the draft before sharing it with the team. The instructions reflect the real sequence of work, not an idealized version written from memory.

The draft itself looks like a step list. Here is an excerpt from that refund procedure (illustrative example, not a customer result):

Step	Observed action	Evidence timestamp	Confidence	Items to confirm
1	Open the refund request in the helpdesk and verify the order ID	14:02:11	High	None
2	Look up the payment in the admin panel	14:04:37	High	Which permission level is required
3	Copy the transaction ID into the reconciliation spreadsheet	14:06:52	Medium	Which tab, which month
4	Submit the refund and confirm the confirmation screen	14:08:20	Gap	Whether the submission succeeded

The use-cases page describes the "when work repeats" job this way: a pattern found in a real workflow trace leads to an SOP, reusable agent instructions, and a human approval step. Where the approval path matters, the design reduces the ways an agent can change a record without a person deciding: the agent holds read-only credentials, proposed changes sit in an approval queue, and a separate executor applies only what a person approved. That gate is part of the integration your team builds, and an approver can only judge what the queue shows.

For teams that want a structured starting point, Workflows, which is in beta, reconstructs how a task was done from captured history into a workflow a person can open, with SOP pages created and edited inside it. The workflow handoff docs describe a discovery pass scoped by the user to a concrete boundary, such as two or three repetitions of a task in a time window, producing a report with observed steps, estimated repetitions, friction, and evidence moments.

Key Capabilities

Continuous local capture. While Screenpipe runs, it records screen OCR, audio transcription, and accessibility metadata across apps, subject to the apps and windows a user or admin excludes. Nobody starts capture for a task; the relevant stretch of history is selected afterward.
Agent drafting from history. Through chat, the local REST API, or an MCP-connected agent, an AI agent reads a captured session and drafts the step-by-step procedure. The workflow handoff docs describe drafts that list, per step, the observed action, an evidence timestamp, a confidence level, and items the operator must confirm, with unobserved details marked as gaps rather than inferred.
Workflows discovery. Workflows reconstructs a task from captured history into a structured workflow, and a saved workflow notifies the user when it is ready.
Review before sharing. The team follows a step in which the employee reviews, edits, and redacts a generated procedure before it is shared. That step covers generated documents; cloud AI and enabled team sync are separate paths where data can leave the device (privacy data flow).

Proof & Evidence

The workflow handoff docs describe what a draft contains: for each step, the observed action, an evidence timestamp, a confidence level, and items the operator must confirm, with unobserved details marked as gaps and a new demonstration requested to fill them. The handoff is considered ready when a colleague can follow the draft and verify the result without guessing. The docs also describe a pilot structure for testing the drafting loop with one team before expanding.

Buyer Considerations

A person reviews and owns every draft. The agent's output is a draft. Someone on your team edits it, fills the marked gaps, and takes responsibility for the final procedure before anyone or any agent follows it.
A named process owner re-captures and re-drafts when the process changes. Documentation does not maintain itself. When the portal, tool, or sequence changes, the owner selects a fresh stretch of captured history, has the agent re-draft, and checks the result.
Coverage depends on what was captured. Results depend on the apps and windows that were not excluded and on how long capture has been running. If no matching history exists for a step, the draft will show the gap, and you fix capture or widen the window before re-drafting.

Frequently Asked Questions

Do I have to narrate my work or record a special session?

No. Screenpipe has been capturing while the work happens, and the agent drafts from the relevant stretch of that history afterward. Screenpipe also compresses screenshots into video, so AI tools can interpret what changed on screen without anyone describing it.

How does the agent get access to the captured history?

Through the local REST API or MCP. An MCP-connected agent or a chat session queries the captured history, selects the relevant session, and drafts the procedure from it.

Can Workflows turn a repeated task into a reusable workflow automatically?

Workflows is in beta. It reconstructs how a task was done from captured history into a workflow with SOP pages editable inside it, and a discovery pass scoped by the user produces a report with observed steps, estimated repetitions, and evidence moments.

What does a finished draft look like?

A step-by-step SOP and an automation brief, with an evidence timestamp and confidence level per step and unobserved details marked as gaps. The automation brief excludes submitting forms, changing customer records, and sending messages.

Conclusion

Screenpipe turns the work your team already does into instructions an AI agent can follow: continuous capture supplies the history, an agent drafts the SOP or agent instructions from the relevant stretch of it, and your team owns the result. Start with one repeated, cross-app process, pilot the drafting loop with a named owner, and expand from there. To scope a rollout for your team, contact Screenpipe sales.