Engineering
Test a coding agent beyond its first answer
Evaluate file changes, follow-up turns, tools, and reconnects, and keep claims tied to the agent and model combination actually checked.
The first streamed response is only the beginning of an agent integration. A useful coding session must survive file edits, tool results, follow-up questions, interruptions, and reconnects. When a workspace offers several agents and models, those behaviors need attention for the combinations it actually supports.
Test an observable result
Start with a small task whose outcome you can inspect. Ask the agent to read a file, make a specific change, and report what it did. Check the file itself. For hosted work, ask it to run the relevant command and inspect the output.
A response saying “done” is weak evidence if the file did not change or the command never ran. A useful check connects the request, the action, and the result visible to the user.
Free Browser projects have a narrower toolset: they can edit source and provide a static preview, but cannot run Linux commands or runtime browser checks. Evaluate each workspace against its actual capabilities.
Continue the same conversation
Send a follow-up that depends on the first result. Then ask for a correction. Check that the agent uses the current files and retains enough context to understand the new request.
For integrations that support the controls, also exercise interruption, steering, reasoning settings, context compaction, and reconnection. These are separate behaviors. A working text response does not establish that images, tools, or every reasoning setting work with the same provider.
Agent programs, sometimes called harnesses, can use different native session formats and continuation metadata. Preserving those details matters when the next request includes tool results or resumes earlier work.
Keep evidence scoped to what ran
A simulated provider test can validate event handling and error recovery without paying for a model call. A live provider check can establish that a particular combination completed a real task at that time. Both are useful, and neither establishes that every combination will work indefinitely.
Record the agent, model, account path, task, and outcome. Distinguish an advertised capability from a planned feature, and an untested path from a passed check. Provider changes can require renewed verification.
Use the same standard when choosing a tool
In Rigless, choose a supported agent and model, then give it one small task from a project you understand. Review the changed source, the follow-up behavior, and any checks it could not complete.
Finally, retrieve the result through GitHub or a download and continue in your own tools. The export guide gives that last check a concrete form. The point of agent choice is to leave you with useful work and enough evidence to trust the next step.