Task mining data: desktop interaction logs as an agent data source

What task mining captures, and how it differs from process mining

Task mining records what people do on their desktops, while process mining reconstructs business processes from system-of-record event logs. IBM describes task mining as collecting clicks, keystrokes and navigation paths through software installed on employee machines or screen recording, then using OCR and machine-learning clustering to group low-level events into tasks such as submitting a purchase order [1]. Process mining starts from ERP, CRM or ticketing logs where each event carries a case ID, activity and timestamp, increasingly in the object-centric OCEL 2.0 format that links one event to several business objects [5].

The difference matters for agent builders. Process mining tells you that an invoice moved from "received" to "approved" and how long it took; task mining tells you how often the clerk switched between an email client, a PDF viewer and an SAP GUI screen to get there. If you need the case-level view, start with process mining event logs; this page covers the desktop layer.

Typical task-mining captures include:

• Application and window events: process name, window title, foreground and background switches, durations.
• Input events: clicks with coordinates or UI element references, keystroke counts or masked keystrokes, copy and paste between applications.
• Screen evidence: screenshots sampled on events or intervals, often with OCR text extracted and the image itself discarded or blurred.
• Derived structure: clustered tasks, variants, step labels and time-per-step metrics produced by the vendor's models.

Why task-mining logs are rarely full-fidelity agent trajectories

Task-mining logs usually lack the per-step state, action targets and outcomes that computer-use training requires, so treat them as weak supervision until a sample proves otherwise. Agent benchmarks such as OSWorld evaluate agents in real Ubuntu, Windows and macOS environments with execution-based checks of final state [6], and web datasets such as Mind2Web pair each task with a full crowdsourced action sequence on real sites [7]. A trajectory you can train or evaluate on needs an observation, an action and a verifiable result at every step; see what every computer-use step record must contain.

Common fidelity gaps to test for:

• Sampled, not continuous, screens. If screenshots are taken every few seconds or only on window change, you cannot see the state immediately before most clicks.
• Coordinates without element identity. A click at (812, 344) on a 1920x1080 display is not reusable without the accessibility tree, DOM node or control ID it hit.
• Masked text input. Privacy settings commonly drop or hash keystrokes, so field values, search strings and formulas are missing.
• No outcome signal. The log shows the clerk stopped working on an invoice, not whether the posting succeeded. You may need to join to system-of-record status, as described in task success labels for agent trajectories.
• Vendor-derived labels. Task clusters and step names come from the vendor's models, so label noise is inherited and undocumented unless the vendor explains its method.

Realistic benchmarks were built to expose the gap between human and agent performance; WebArena's v4 paper (2024) reported 14.41% end-to-end success for its best GPT-4-based agent against 78.24% for humans [8], and newer agents score higher, so check current leaderboards as of October 2026. Real desktop logs are valuable precisely because they show how humans close that gap, but only if they retain enough detail to replay or grade.

Where task-mining data is strong for agent teams

Task-mining data is strongest where breadth across many employees matters more than per-step precision. Because deployments run across whole teams for weeks, they can show real frequency and variant distributions: which invoice-exception paths actually occur, how often staff fall back to spreadsheets, and which applications appear together. Vendors market these baselines for measuring agent ROI and designing automations [2][4].

Practical uses, ranked by how much fidelity they need:

1. Task selection and scoping (low fidelity). Pick the workflows worth automating by volume, duration and application mix.
2. Evaluation suite design (medium). Build realistic task distributions and long-tail variants for agent evaluation task suites, then recreate them in a sandbox.
3. Human baselines (medium). Time-per-task and rework rates feed human baseline data for agent evaluation.
4. Imitation or trajectory training (high). Only feasible where the capture kept dense screens, element-level actions and joinable outcomes.

For applications without DOM or accessibility hooks, such as mainframe terminals and Citrix sessions, task-mining screenshots and OCR may be the only record that exists; compare with legacy desktop and terminal interaction data.

Rights and notice questions before reuse for AI training

Licensing task-mining data turns on whether the original collection purpose, employee notice and tool contract permit a new use. Many deployments were justified internally as process improvement, and the monitoring notice may say exactly that.

Several US states require notice of workplace electronic monitoring. New York requires employers to give written notice of monitoring of employee telephone, email and internet use, effective May 7, 2022 [9], and Delaware requires notice before monitoring employee telephone, email or internet use under 19 Del. C. 705 [10]. These notice laws do not by themselves grant or deny a training use, but counsel will compare the notice text and any internal policy against the proposed purpose. FTC staff have also warned that quietly adopting more permissive data practices, such as using data for AI training, could be unfair or deceptive [11].

Questions to clear before a deal:

• Notice scope: Does the notice or acceptable-use policy limit use to productivity analysis or process improvement? Was it updated, and how?
• Third-party content: Screens show customer names, account numbers and supplier documents, not just employee behavior. Those records carry their own contractual and privacy restrictions.
• Tool vendor terms: The task-mining or analytics vendor may host the data and restrict raw export or derivative use. See whether SaaS vendor terms allow exporting data for licensing.
• Ownership of derived labels: Clustered tasks and variant models may be the vendor's work product, not the customer's.

For the related question of logs produced by deployed agents rather than people, see agent deployment logs and training rights.

This page is general information, not legal advice. Confirm requirements with counsel for your jurisdiction and use case.

Redaction and privacy for screen-derived records

Screen-derived task-mining data needs redaction across both pixels and extracted text, and buyers should expect residual risk. A sampled screenshot of an ERP vendor master or a CRM contact view can expose bank details, emails and phone numbers that never appear in the event stream. OCR output, window titles and clipboard contents are frequent leak points because window titles often embed file names, customer names or ticket subjects.

Ask the supplier to document which fields and regions were masked, how screenshots were handled (blurred, cropped, dropped), and what sample review was done. For detailed methods, see PII redaction for screen recordings and agent trajectories. ISO/IEC 5259-4 provides a process framework for documenting data quality in training and evaluation data, which is a useful structure for the supplier's preparation notes [12].

Task-mining dataset assessment checklist

Use this checklist to decide in one review whether a company's task-mining archive is worth pursuing, and for which use.

Illustrative example: invented to show structure; it does not describe an available dataset.

An illustrative normalized event record a buyer might request looks like this:

Illustrative example: invented to show structure; it does not describe an available dataset.

{
  "session_id": "s-7f21",
  "user_pseudonym": "u-0193",
  "ts": "2026-03-04T14:22:07.412Z",
  "app": "saplogon.exe",
  "window_title": "[REDACTED] - Display Vendor",
  "action": "click",
  "target": {"control_id": "btnSave", "role": "button", "bbox": [812, 344, 880, 368]},
  "screenshot_ref": "frames/s-7f21/000418.png",
  "ocr_text_ref": "ocr/s-7f21/000418.json",
  "business_key": {"type": "invoice", "id": "INV-REDACTED-01"},
  "vendor_task_label": "Post vendor invoice",
  "label_source": "vendor_clustering_v3"
}

If a sample cannot fill target, screenshot_ref and business_key for most rows, scope the purchase to discovery and evaluation design rather than trajectory training.

How task-mining data fits a broader agent data plan

Task-mining data works best combined with system-of-record logs, SOPs and outcome records. Pair desktop events with cross-system workflow records to get outcomes, and with SOP-to-execution pairs to compare documented and actual work. Vendor comparisons show tools vary widely in what they capture, so a buyer's specification should name required fields rather than a tool category [3]. For an overview of the whole category, start at the agent training data hub or the workflow and screen activity data and computer-use agent pages.

SourceX sources operational datasets, including workflow and screen activity data, from US companies on request. Nothing is held in stock and a request does not guarantee a match. You can describe the desktop activity data your agents need by fields and use, not by company.

Sourcing task mining data for AI agents

SourceX looks for US businesses that hold the data you describe, reviews ownership and consents, and delivers only under a license that defines records, uses, term and delivery, after the supplying company approves the release. Personal details are removed or replaced before delivery, with the method recorded and a sample checked, though no method is perfect. Start by describing the task-mining or desktop activity data you need.