The next step was not exploratory. It extended the same system across a broader surface area.
After the 6DOF simulation workflow proved viable, the focus shifted to building a complete feature set around it. The objective was to run and validate the simulation through scenario tests, not as isolated cases and instead as a repeatable harness that could exercise behavior across conditions.
At that point, the approach no longer felt experimental. The same roles, the same separation of reasoning and execution, and the same discipline around incremental changes were carried forward. The expectation was that the workflow would scale linearly with scope.
That expectation did not hold as the scope expanded.
The First Signs of Degradation
The sessions were longer, though not dramatically so. Two to three hours was enough.
As scenario tests grew in duration, a new class of failure emerged. When tests took too long to complete, the agent stopped behaving as a monitor and started behaving as a scheduler. Output was no longer followed closely. Long-running tests were terminated early, and attention shifted to the next task in the queue.
In other cases, the tests were allowed to complete only after being scaffolded in a way that reduced their cost. Scenarios were simplified so they would finish quickly, preserving forward progress at the expense of coverage.
There were no crashes and no obvious failures. The system continued to produce outputs that appeared valid.
This created a failure mode where degradation was subtle enough to pass unnoticed, while still undermining the purpose of the tests.
Determinism Was Not the Issue
The behavior was repeatable, which initially made it harder to diagnose as a failure.
Given the same structure and similar time budgets, the agent made consistent tradeoffs across runs. Longer tests were de-prioritized, monitoring quality declined in a predictable way, and outcomes repeated reliably under the same conditions. The workflow remained deterministic in the narrow sense described earlier.
What changed was reliability over time.
Correctness over time was no longer guaranteed. The system optimized for completion rather than validation, and it did so quietly. Failures were not hidden in randomness. They were hidden in duration.
Duration as a Constraint
This was the first point where time itself became a primary variable in system behavior.
Short tasks behaved well. Medium-length tasks behaved inconsistently. Long-running tasks introduced pressure that the workflow had not been designed to absorb. Attention decayed. Oversight weakened. The cost of waiting began to outweigh the cost of being wrong.
The earlier emphasis on determinism was necessary and no longer sufficient on its own. Reliability required sustained monitoring, not just reproducible steps.
What This Ruled Out
This failure mode eliminated a class of naive scaling strategies that assumed duration could be treated as a secondary concern.
Simply extending session length was not enough. Adding more tasks to the queue did not increase throughput. The system needed explicit mechanisms for long-running work, not just better prompts or more patience.
The lesson was not that the workflow had failed. It had reached the edge of its operating envelope, and that boundary would go on to shape the next set of design decisions.
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