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Debashish Ghosal reports reducing a live test from 2,490 AI-agent tool-call runs to 206 by separating scenario breadth from decision-type depth. The approach depends on an earlier layer of deterministic tests and on an important assumption: the gate engine and framework adapter behave independently. It is a useful design example, not an independently validated benchmark or a guarantee that the same reduction will work for another test suite.
How do you decide where your deterministic tests stop and your real-agent tests begin? Ghosal’s September 22, 2026 DEV Community account offers one practitioner’s answer: use deterministic assertions to exercise engine paths, then make a smaller set of live calls to check that scenarios and framework-level decisions work with real agents.
What the 206-run design covered
Ghosal describes a project called agent-tooltrust, an open-source gate for AI agent tool calls. Its full field-test matrix paired 83 agents with 30 scenarios, yielding 2,490 possible live runs. Instead of filling every agent–scenario cell, he divided the live-test goals into breadth and depth:
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- Depth: run enough cases to exercise each of four decision types—allow, audit, escalate, and deny—within each framework.
In Ghosal’s account, Plan A used 83 runs and produced 83/83 results. Plan B used 123 runs and produced 116/123 results, or 94%. Together, the plans amounted to 206 live runs rather than 2,490. These counts and coverage outcomes are the author’s report from one project, not independently replicated measurements.
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How the two plans differ from a full cross-product
| Test approach | Scenario breadth | Decision-type depth | Agent–framework interactions | Live model calls | Cost and review implications |
|---|---|---|---|---|---|
| Full cross-product | Every agent is paired with every scenario. | The matrix contains all agent–scenario combinations, but the account does not state a separate decision-type coverage result. | Can expose specific agent–framework combinations if the matrix includes them. | 2,490 possible runs in Ghosal’s 83-by-30 example. | Ghosal estimated 30–80 seconds per real LLM call and about 2.7 hours for the full set at 10 workers, before debugging overhead. These are his project estimates, not general timings. |
| Breadth-and-depth covering design | Plan A exercises all 30 scenarios at least once across the agents, frameworks, and classes reported. | Plan B targets all four decision types within each framework. | Can miss a failure that appears only in a particular agent–framework pairing. | 206 runs in Ghosal’s report: 83 for breadth and 123 for depth. | Fewer live calls can reduce run time, but the source does not quantify comparative debugging or review cost. |
The comparison is about the live field test, not the whole test strategy. Ghosal says 2,490 deterministic assertions had already exercised every engine decision path without LLM calls. That is his description of the project’s existing suite; it does not establish that deterministic tests can replace real-agent checks in other systems.
What the seven Plan B failures indicate
Ghosal says all seven unsuccessful Plan B runs were “not-available” outcomes: the model did not call the guarded tool. He distinguishes those from unexpected-decision errors, in which the engine returns the wrong verdict, and reports none of the seven as an unexpected decision.
That distinction is useful when designing test reports. A model that never invokes a tool presents a different diagnostic question from a gate that makes an incorrect allow, audit, escalate, or deny decision. Combining both into one undifferentiated failure count can obscure whether the issue lies in tool invocation or gate behavior. Ghosal’s example involved a small local 4B model given five tools that sometimes responded in prose instead of making a tool call; it is an example from this project, not evidence about 4B models generally.
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When reducing the matrix is risky
The covering design rests on an independence assumption: the engine and its framework adapter do not have cross-cutting interactions that require testing every agent–framework combination. Ghosal says this held in his project because its engine was framework-agnostic. If your application has behavior that varies with a particular agent and framework together, scenarios tested separately may not reveal the interaction.
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- Use a broader cross-product when agent–framework pairings contain unique code, configuration, or tool-call behavior.
- Before reducing combinations, establish whether the engine is genuinely separated from adapters and whether the adapters share relevant behavior.
- Keep deterministic tests as the first line of defense; Ghosal describes live field tests as a second line and says the $0 assertion-failure result is trustworthy only if code review has already caught actual bugs.
The author does not offer a general proof that this sampling design transfers to other suites. He also acknowledges that he has no principled universal rule for deciding which properties only a real agent can prove and which deterministic tests can establish; he calls his boundary a judgment call.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical way to adapt the idea
Use the design as a planning framework rather than copying the 206-run figure. First define what the live calls need to demonstrate, then decide which combinations are safe to omit.
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- List deterministic responsibilities. Identify engine paths that can be tested without model calls, including each expected decision type.
- Specify live-test goals separately. Write down which scenarios must be seen with real agents and which framework-level decision outcomes must be surfaced.
- Map the combinations. Record agents, scenarios, frameworks, and any agent classes your system actually uses. Choose assignments that meet the stated breadth and depth goals.
- Check the independence assumption. Look for behavior that depends on a specific agent–framework pairing. If such interactions matter or remain uncertain, retain those combinations or run the full cross-product.
- Classify outcomes. Report tool non-invocation separately from an incorrect gate decision so a failed run points to the right diagnostic path.
- Review the boundary. Treat the smaller plan as a risk-based choice, not proof of equivalent coverage. Add cases when code review, failures, or system changes reveal interactions the covering plan did not exercise.
Ghosal’s report links a v0.1.1 field-test report with the scenario-to-agent mapping, as well as project code, a field-test plan, and design decisions. Those materials provide context for his account, but the reported equivalence has not been independently established.
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