Why use real workflows for computer-use agent evals?

Public benchmarks are useful, but they miss company-specific tools, messy handoffs, domain judgment, and the exact UI states where agents fail. Real workflow traces create evals that match the work an agent must actually perform.

What does a Screenpipe workflow trace include?

A trace can include screen frames, app/window context, accessibility text, OCR text, audio transcripts, timestamps, and the final human-readable SOP or expected outcome. The exact data fields depend on the deployment scope.

Can this be used by AI labs or agent startups?

Yes. The SDK and enterprise deployment paths support teams building agent products, eval suites, workflow observability, and desktop automation systems that need real computer-use context.

workflow traces for AI agents

Computer-use agent evals from real workflows.

Screenpipe captures how people actually complete desktop work, then turns that trace into SOPs, prompts, expected outcomes, edge cases, and evals for computer-use agents.

Scope agent eval data Embed the SDK

Capture the human run

Observe the workflow across browser, desktop apps, meetings, ERP, CRM, spreadsheets, and internal tools.

Convert to SOP and spec

Extract steps, inputs, decision points, expected output, edge cases, and acceptance criteria.

Build the eval case

Package the trace into a task an agent can attempt and a grader can judge against a real outcome.

Improve the dataset

Keep examples, failures, variants, and confidence notes tied to the workflow owner and deployment context.

eval artifact

A useful eval starts as a real trace, not a synthetic prompt.

TaskUpdate a CRM opportunity after a customer call.

InputsMeeting transcript, browser state, CRM fields, Slack thread, previous account notes.

Expected outcomeOpportunity stage, next step, owner, call summary, and follow-up date are correct.

Failure modesWrong account, hallucinated next step, missed pricing objection, duplicate record.

Privacy scopeOnly approved fields and redacted excerpts are included in the eval package.

deployment modes

Local-first does not mean one data path.

Screenpipe can run as a local-only personal assistant, a scoped team deployment, or an embedded capture engine. The important question for buyers is not a slogan; it is which data flow they approve.

Local-only

What stays local: Screen capture, accessibility text, OCR output, audio files, transcripts, and the local database.
What may leave the device: Nothing is required to leave the device for core capture and search.
Buyer decision: Best for self-serve use, regulated pilots, and proving value before any cloud path is enabled.

Local + optional cloud AI

What stays local: The raw capture store remains on the endpoint unless the user or organization enables export or sync.
What may leave the device: Selected prompts, summaries, or context snippets may be sent to the chosen AI provider or confidential route.
Buyer decision: Buyer chooses model, provider, retention posture, redaction, and whether local models are required.

Team / enterprise

What stays local: Endpoint capture and local history can stay on managed devices under admin policy.
What may leave the device: Team reports, sync, admin workflows, exports, connectors, and agent outputs depend on deployment scope.
Buyer decision: Buyer defines consent, retention, employee controls, report contents, and admin visibility.

SDK / OEM

What stays local: The embedding app defines the storage path, model path, and user-facing privacy controls.
What may leave the device: Data movement depends on the partner architecture and the contractually agreed processing path.
Buyer decision: Partner owns data-flow design, disclosures, user consent, and downstream model/provider choices.

Review security architecture Plan a team rollout

point of view

Capture should produce decisions, not surveillance dashboards.

Screenpipe's enterprise lane is workflow intelligence for AI adoption: prove which work repeats, what can be automated, what an agent should attempt, and which data paths the buyer approves.

One workflow beats a fleet rollout

A buyer should not start by deploying capture to everyone. Pick one repeated workflow, one owner, one data path, and one expansion decision.

The useful data lives between systems

ERP, CRM, and ticketing logs miss the spreadsheet, tab, message, meeting, and judgment step. That is where the automation target usually hides.

Agents need traces, not vibes

A usable computer-use agent spec needs real inputs, expected outcomes, edge cases, failure modes, and a way to grade the result.

Privacy is part of the deliverable

A workflow report should say what was captured, excluded, redacted, retained, exported, and shared before the team expands deployment.

Privacy is part of the eval design.

Agent data work should define what is captured, what is excluded, what gets redacted, how long traces are retained, and whether raw examples, derived SOPs, or only acceptance criteria leave the endpoint. Screenpipe makes that a deployment decision, not an afterthought.