Hyperscaling

The agent must verify the product as a user would use it.

Run the final product end to end through the user’s interface and permissions. Confirm the feature works, or replay the original failure and verify the fix. Passing tests and code review support this evidence; they do not replace verifying the actual outcome.

Give the agent browser automation, computer-use tools, or end-to-end tests that exercise the actual interface. For CLI or API products, test through those interfaces. Include startup and reset commands, test accounts, and realistic data so the agent can run the flow and inspect the result without routine human help.

Define the expected outcome.

Give every ticket an observable result, relevant context, clear constraints, and a way to verify success. Let the agent choose the implementation within those boundaries.

Test the uncertain part with the smallest useful experiment before expanding the implementation. More generated code is not the goal; a verified improvement is.

Make failures easy to diagnose.

Expose logs, errors, requests, and stored state so the agent can trace a failed user action to its cause. Measure critical workflows, not just process health. A running service can still be failing its users.

When evidence or access is missing, the agent should identify the gap and ask for help. It should not turn an untested explanation into a verified diagnosis.

Make recovery routine.

Save progress and provide safe retries, resets, and rollback. Check whether an operation succeeded before repeating it. Make it easy to recover from a failed experiment without losing unrelated work.

Keep parallel work independent.

Give agents distinct questions or workstreams, clear module boundaries, and isolated test environments. The owner combines the findings and resolves conflicts. Verify the integrated product—not just each agent’s piece—before marking the work complete.

Make each agent’s learning useful to the next.

Every agent should build on what other agents have learned. Use Forum through Cosmos to find prior work and submit reusable lessons, failed approaches, and evidence for review—not routine status updates. Make Sesh and the coding-session tracker accessible to agents so they can recover the context behind those lessons instead of starting over. Verify current state at the source.

Remove recurring intervention.

Turn repeated setup, debugging, and human corrections into tested tools and workflows. Check that each improvement reduces future intervention while preserving outcome quality. Measure verified results and recurring blockers, not agent activity alone.

Move the testing loop to the agent.

The agent should iterate on implementation and testing before asking a person to review the result. Human review starts with evidence, not a request to discover whether the feature works.

The agent supplies screenshots, recordings, or logs showing the user’s actions and the resulting state. It states what was tested, in which environment, and what remains unverified. The human reviews that evidence and approves the result; delegation does not transfer accountability.