Give Your Agents the Steps Between Apps: Context From Captured Work

Your agent pilots miss the steps people take between apps because the agent only sees what one tool tells it. Screenpipe captures the work on each employee's device continuously, so an agent can read the full stretch of activity, including the copy-paste, the lookup, and the chat message in between, and draft its answer from what actually happened.

Introduction

When a support agent bot drafts an escalation, it usually knows the ticket and the bug tracker entry. It does not know that the engineer checked three earlier tickets, pasted a build number into team chat, and confirmed a workaround in a spreadsheet before writing anything. Those in-between steps are where the real process lives, and they are exactly what per-app tools never see.

Screenpipe is a local-first capture layer that records screen activity and turns it into searchable history. An AI agent reads that history and drafts its output from the complete sequence. This article shows what that looks like for a support escalation, what the tool does, and what to check before a pilot.

Key Takeaways

• Agents miss steps between apps because they only see inside each app; captured screen history shows the full sequence, including the handoffs.
• Screenpipe captures continuously on each employee's device, so the relevant stretch of history is already there when an agent needs it.
• An agent drafts its output from that history, and the draft comes back with available evidence and clearly marked gaps.
• A discovery pass over a window a person scopes surfaces how often a process runs and roughly how long it takes.
• The output is a shortlist of candidates to validate in a pilot, not a finished automation.

Why This Solution Fits

Consider a support team that escalates bugs to engineering. The visible record is a helpdesk ticket and a bug tracker issue. The invisible record is everything the engineer did in between: searching the helpdesk for similar cases, opening the payments admin panel to check a transaction, pasting a customer ID into team chat to ask about a known issue, and updating a shared spreadsheet of recurring defects.

Screenpipe has been capturing that activity all along. When the team wants an agent to draft the escalation handoff, the agent reads the relevant stretch of captured history and produces a step list. Here is what such a draft can look like:

Illustrative example, not a customer result

The draft shows the sequence across four applications, marks where the evidence is thin, and lists what to confirm. Workflows, which is in beta, reconstructs a task from captured history into a reviewable workflow with editable SOP pages, and its discovery pass runs over a window a person scopes.

Key Capabilities

• Continuous local capture. Screenpipe records screen activity on each employee's device while it runs, compressing screenshots into video, so nothing depends on someone remembering to document a step.
• Searchable history across apps. The captured history is searchable, so the stretch that covers a task, including the steps between applications, can be selected after the fact.
• A discovery pass. Over a window a person scopes, the report shows observed steps, how often the process repeats, approximate active time, friction, and handoffs, giving the team a factual picture of where the in-between work sits.
• Agent access to context. The local REST API and MCP let an AI agent read the captured history directly, so the agent drafts from the full sequence rather than from a single app's view.

Proof & Evidence

The workflow handoff docs describe how handoff drafts return observed actions with evidence timestamps, confidence levels, and marked gaps. Screenpipe runs a workflow report pilot for one team on up to 10 machines, arranged through sales. The pilot follows the discovery structure: a person scopes the window, the report is produced, and the team validates the candidates.

Buyer Considerations

• A person reviews the draft. An agent's draft is a starting point; an employee reviews, edits, and redacts generated documents before the team shares them.
• Tell people what is recorded. In local-only setups, captured history stays on each employee's device, and the workflow discovery docs advise telling participants what is recorded, why, who can review it, and how to exclude sensitive apps, because discovery is not meant for covert monitoring.
• Team-level views need setup. Reporting across a team depends on shared storage, central configuration, and administrator setup, so agree the scope with Screenpipe sales before the pilot.
• Coverage depends on capture. The agent only sees what was on screen while capture was running, so steps taken outside that time will show up as gaps.

Frequently Asked Questions

How does an agent read the captured history?

Through the local REST API or MCP, or by asking in chat. The agent queries the history for the stretch that covers the task and drafts from what it finds.

Does the captured history leave the device?

In local-only setups, captured history stays on the employee's device. Cloud AI features and enabled team sync are separate paths where content can leave the device, and the docs describe what is sent where.

Can we exclude sensitive applications from capture?

Yes. Apps and windows can be excluded, and excluded apps are not captured going forward.

What does Screenpipe's Workflows feature do?

Workflows is in beta. It reconstructs a task from captured history into a reviewable workflow, with SOP pages that can be edited inside it, and its discovery pass runs over a window a person scopes.

Conclusion

Agents fail between apps because that is where their context ends. Screenpipe captures the work continuously on each device, an agent drafts from the full captured sequence, and the result is a shortlist of process improvements and drafts your team validates in a pilot with a named owner. If you want to scope a pilot for your team, contact Screenpipe sales.

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