AI Agent Workflow: How Tasks Are Discovered and Completed

Lifecycle diagram: discover, filter, execute, track, optimize earning tasks — Strong workflows separate planning from execution and keep humans accountable for attestation steps.

A complete agent-aware workflow mirrors product analytics: ingest available tasks, score them, commit to a shortlist, execute with telemetry, reconcile payouts, then refine heuristics. On Paidwork, the human is the execution layer for anything that requires consent, identity, or gameplay. Paidwork.

Phases explained

Discovery: aggregate new surveys, videos with remaining cap, and fresh installs.
Filtering: remove ineligible offers, unrealistic time boxes, or duplicate game installs.
Execution: user completes tasks; agent tracks timers and screenshots.
Reward tracking: pending vs credited; escalate with structured notes.
Optimization: adjust mix by hourly yield—not by cheating caps.

Metrics that matter

Track completion rate, disqualification rate, average survey length, and offer breakage. AI can chart trends; you choose strategy.

Pro tip

Weekly retrospectives beat daily obsession: 15 minutes comparing planned vs actual tasks prevents burnout.

AI Agent Workflow: How Tasks Are Discovered and Completed

Phases explained

Metrics that matter

Pro tip

Related guides

See also

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