When an agent-certification result changes from run to run, it is not a dependable admission gate. In a HivePlane field test, Debashish Ghosal found that a local model sometimes classified a bug-fix task as changelog, making certification nondeterministic. He replaced that variable judgment with deterministic test subjects to check whether the control plane itself behaved consistently—while explicitly leaving real-model quality and drift unproven.
Why a certification gate needs a repeatable signal
A certification gate is useful when its result reflects a meaningful property of the system being tested. If the same task and setup can pass or fail because an LLM judge changes its classification, the gate is measuring judge variation alongside agent behavior. A fixed threshold cannot make an unstable signal reliable.
Ghosal summarized the problem this way: “A certification result that changes run to run isn’t a certification.” In his HivePlane test, a local model misclassified a bug-fix task as changelog. The result was run-to-run variation, not a sound basis for deciding whether an agent should be admitted.
Portability failures came before certification
The first obstacle was not the judge. Ghosal reports that three agents he had already built were not immediately runnable in the HivePlane environment. His examples included a missing dependency, an expected package or module absent from the downloaded tree, and an external OpenAI SDK that was absent by design.
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These failures point to workload portability: an agent that works in its home repository may depend on a particular package layout, installed libraries, or provider access. They do not, by themselves, show that the control plane is defective. Registration and certification can only test an agent once its execution interface and dependencies are available in the target environment.
What the deterministic test did—and did not—test
Ghosal shifted the target from evaluating live model judgment to testing the control plane’s registration, admission, execution, and audit path. His Tier 1 subjects were deterministic real agents connected through thin adapters.
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The test boundary matters. The knowledge base (KB), tools, and judge’s opinion were mocked. The adapter dispatch and control-plane plumbing remained real; according to Ghosal, identity binding, the provider seam, and the model-swap gate stayed in the tested path. That design can reveal whether the control plane handles a known fixture consistently without allowing a changing judge opinion to dominate the result.
| Test approach | What it can answer | What it cannot establish by itself |
|---|---|---|
| Deterministic agents and mocked KB, tools, and judge opinion; real adapter and control-plane path | Whether the tested registration, admission, dispatch, and related control-plane behavior is repeatable under the fixture | Whether a live model classifies tasks reliably, whether agent quality generalizes to real workloads, or how model drift affects results |
| Live-model evaluation | How the selected model behaves on the evaluated tasks and setup | Repeatability beyond the observed runs; broader generalization or future drift without further evidence |
The distinction is central: mocking the judge is appropriate for isolating control-plane behavior, but it changes the question being answered. A deterministic fixture is not evidence that the live model is dependable. Ghosal describes a cloud-profile run with real prices and drift as future work, not a result of this test.
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In Ghosal’s 2026 account, S1 passed identically in three consecutive stack runs: the tasks, verdicts, and pass rates matched. At a 0.90 threshold, he reports these results:
| Certified subject | Reported result | Reported p95 latency | Threshold |
|---|---|---|---|
| support-agent | 6/6 | 67 ms | 0.90 |
| eval-judge | 4/4 | 126 ms | 0.90 |
These are project results reported by Ghosal, not independent benchmark findings or a prediction of production performance. They show repeatability for the named S1 fixture and stack runs; they do not show that other agents, models, workloads, or environments will achieve the same results. The report also says the passes carried signed Ed25519 attestations: a signature records the certification result, but does not make an unstable evaluation trustworthy.
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Why the live stack still matters
Deterministic fixtures do not replace testing environmental behavior. Ghosal describes a live-stack test that exposed an egress allowlist omission missed by unit tests: a PagerDuty acknowledgement was denied because api.pagerduty.com was not in the allowed-host manifest.
He also reports restarting the API while a LangGraph run was paused. After the restart, the event log contained 8 events, the durable checkpoint remained intact, and the run resumed to completion. These examples exercise conditions that a mocked unit test may not reproduce, including outbound policy and persisted workflow state.
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For long-running scenarios, Ghosal’s operational lesson is to emit progress evidence before an interruption or boundary. If a run aborts, that evidence helps establish how far it got and what state survived.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical way to design a useful gate
- Make the question explicit. Decide whether a test is evaluating agent or model behavior, or the control plane’s registration, policy, execution, and audit loop. Avoid treating one as proof of the other.
- Stabilize the fixture when testing plumbing. Use deterministic subjects and control variable judgment so repeated runs can meaningfully be compared.
- Document the boundary. State what is mocked and what remains real. In this account, the KB, tools, and judge’s opinion were mocked while adapters and control-plane plumbing stayed in the path.
- Check portability separately. Confirm dependencies, package structure, and provider access in the target environment before interpreting a failed run as a certification or control-plane failure.
- Exercise live conditions that mocks hide. Test egress policy and restart recovery on the stack, and preserve progress evidence for long-running workflows.
- Keep the conclusion within the evidence. A repeatable fixture supports a claim about that tested path and setup. Claims about live-model quality, generalization, or drift require evaluation that actually tests those properties.
HivePlane’s public repository describes it as an open-source, local-first control plane for agent fleets and discusses reproducible benchmarks and fleet operations: HivePlane on GitHub. That project context does not independently reproduce the field-test results.
Ghosal’s first-person field-test account was published September 27, 2026: A Certification That Changes Every Run Is a Coin Flip With a Signature.
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