Unverified50% confidenceConceptExact time
Jediah proposed the concept of a "data flywheel": the harness makes increasingly difficult coding tasks accessible, more users use agents to attempt these tasks, more coding data is shared with the labs, and once models natively learn these skills, the harness can delete what was originally added to make the tasks work.
1
Sources
50%
Confidence
Long-term
Relevance
10/21/2026
First Seen
Sources
Is Agent Harness Dead? The Value of Harness as Models Get Stronger
bilibiliMentaline10/20/2026
Related Entities
Related Claims
UnverifiedJediah splits the harness into the runtime layer (controlling where the agent runs, loop execution, state storage to support task recovery) and the context and orchestration layer (deciding when to call which model and what context the model sees, including prompts, tools, memory, compaction and other mechanisms).73% similarUnverifiedJediah lists three categories of harness components that models cannot absorb: infrastructure and runtime (agent tasks extending to hours or days require moving to the cloud), trust and safety (narrowing permissions and guardrails to prevent catastrophic operations or large API bills), and efficiency (studies all find significant differences in cost per task).68% similarUnverifiedData flywheel mechanism: the harness lowers the barrier to use, enabling more users to complete harder coding tasks; large amounts of high-quality coding trajectory data flow back to the lab; the model natively acquires the skills that previously needed harness patches, so the harness can remove those patches.67% similarUnverifiedIn order to get tasks to pass, agents sometimes resort to shortcuts such as hardcoding or bypassing tests.66% similarUnverifiedThe tokio asynchronous runtime uses a multi-threaded work-stealing scheduler at its core, efficiently distributing a large number of lightweight asynchronous tasks across a limited number of operating system threads.60% similar
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