> ## Documentation Index
> Fetch the complete documentation index at: https://docs.archetypeai.app/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Start with /introduction/getting-started. Use the Direct Query API (POST /query) with the Newton Fusion model (text, image, and video reasoning) or the Newton Omega encoder (time-series embeddings). ATAI_API_ENDPOINT must include the version path: /v0.5 for most APIs, /v0.6 for the Fine-Tuning Service. Pages whose descriptions are marked (Archived) document the legacy Lens runtime — do not use them for new projects.

# Task Verification Agent

> Verifies work is performed according to standard operating procedures by observing video data. Returns a pass/fail verdict with a short description and the timestamp it came from, plus a summary across the run.

The Task Verification Agent verifies whether an observed task execution matches the defined
Standard Operating Procedure (SOP) and determines if the task was completed successfully and
correctly.

* Compares the observed actions, sequence, and outcomes against the expected SOP steps.
* Identifies completed, skipped, incorrect, or out-of-order steps.
* Validates whether critical requirements, dependencies, and success criteria were met.
* Detects deviations, inconsistencies, or operational errors.
* Assesses overall task completion status and execution quality.

## Use Cases

* **Detect task deviations and quality issues:** Marks each SOP step passed, failed, or missing
  against the video, with the timestamps and a reason for every call.
* **Verify SOP compliance:** Give it the SOP and a video and it returns a per-step result
  zero-shot, with no training or setup, so compliance is the set of steps that passed.
* **Reduce manual quality inspections:** Runs the same step-by-step check on every recorded
  video instead of a sampled few, each result timestamped so review jumps straight to the
  failures.
* **Support operator training, onboarding, and consistency:** Returns a reason for every passed,
  failed, or missing step, so recurring mistakes across runs show where operators need coaching.

## Configuration

For best results, use input videos whose duration is no more than five minutes.

## See an Interactive Example

Try out the Task Verification agent with a predefined data set using our interactive example.

<Card title="Task Verification: Manifold Cap Assembly Agent" icon="list-check" href="https://agents-playground.archetypeai.workers.dev/?agent=tva">
  Verifies a recorded task against a reference procedure. Watches the manifold cap assembly
  and marks each SOP step as completed, out of order, or missing.
</Card>

## Using the Archetype AI Python Client

How to use the [Archetype AI Python client](https://github.com/archetypeai/python-client/) to
work with the Task Verification agent.

<Card title="Task Verification Agent Example" icon="python" href="https://github.com/archetypeai/python-client/tree/main/examples/agents/agent_tva.py">
  An example that runs a Task Verification agent on a task-execution video using a canonical
  blueprint and a newly created organization-owned bundle.
</Card>