Astra and Grok Bot: what changes when agents carry the work

What this release changes in practice

The useful question after a model release is what you can now ask an agent to finish. A stronger model may take a task further, use more of your existing tools, or need fewer corrections. Your workflow still needs a way to tell whether it reached the result you wanted.

OpenAI’s GPT-6 Astra announcement describes improvements in computer use, software engineering, and following user intent. Its model guidance also describes steering work while it is underway and continuing independent work while tools run.

Grok Bot presents delegation through persistent agents that use apps, coordinate work, and run routines. Its documentation explains how people provide tasks, context, and access, then review results and provide corrections.

Those are provider descriptions. This note proposes a practical evaluation; it does not report a ReactorJet benchmark of either product.

Give the agent a finish line

Try a small browser bug: a form submits twice when a person clicks quickly. Give the agent a concrete brief:

Fix duplicate form submissions. One action should create one submission. Preserve keyboard access, validation, and recovery after a failed request. Prepare the patch and evidence for review; leave production unchanged.

The agent can inspect the repository and choose an implementation. A clear brief gives it room to work while preserving the decisions you have already made. Record any later change to the requirements in the same brief so the evaluation follows the current task.

Keep the check outside the proposal

Before the agent edits code, save a browser test that reproduces the failure. Keep that acceptance check outside the files the proposing agent may edit. Run it separately against the candidate, alongside checks for keyboard submission and retry behavior after a network error.

This is what a workflow that verifies itself should mean: verification is part of the system, with evidence that can be inspected independently. The agent’s own completion message is one account of the work, not the acceptance decision.

Use a few clear parts

• An intent brief records the expected result and permitted changes.
• An independent evaluator checks the candidate against the agreed behavior.
• A policy gate decides which results can advance and which need review.
• An evidence packet keeps the patch, checks, and decision together.

For a task that uses several apps, use the same pattern. Specify the finished deliverable, limit access to what the job needs, and check the result in the system where it matters. A scheduled report might need source reconciliation; a support draft might need a policy check before sending.

Try it, then compare the outcome

Run the same bounded task with your current setup and the new one. Keep the starting state, acceptance checks, and permissions consistent. Record whether the acceptance checks pass, how often a person intervenes, the total time, and the cost of unsuccessful attempts as well as successful ones.

Use a small set of repeatable tasks before drawing conclusions. One polished run can show a possibility; repeated results tell you whether to change how you work.

Start with the independent-check tutorial, then trace a failed run to see whether your workflow also explains the cases that do not finish.