How Many Test Cases Do I Need for an AI Eval Harness?

When designing an AI evaluation harness, one of the most frequent questions from engineering leads and product managers alike is: "How many test cases should we run per use case?" The short answer I’ve learned over my 10 years in marketing ops and AI workflow design is: around 50 test cases per use case, minimum. But it's not just a magic number — it’s a data-driven starting point for evaluating model reliability, managing hallucinations, and optimizing costs through specialization and routing.

In this post, I’ll explain why 50 test cases per use case form a practical baseline, how to architect your eval harness setup leveraging planner and router agents, and why focusing on AI regression tests must include verification and disagreement detection to reduce surprises in customer-facing work.

Why Test Cases Matter in AI Evaluation

Unlike traditional software, AI models are probabilistic: they never produce the exact same output for the same input, and the quality depends on both the data and model architecture. An AI eval harness is like a safety net — it ensures that changes in models or prompt templates haven't introduced regressions or introduced hallucinations that undermine reliability.

Establishing the right number of Helpful hints test cases helps you answer:

Is the AI consistently performing well on critical use cases? Are hallucinations controlled, and can they be detected early? Is the routing between specialized models working as intended? Are costs under control when scaling or switching AI providers?

Why 50 Test Cases Per Use Case? The Statistical Perspective

Fifty test cases per use case balances statistical confidence and practical resource constraints. Here's why:

Enough variation to detect meaningful changes: AI outputs can vary even on the same prompt due to stochastic sampling or fine-tuned randomness. Fifty samples give you enough coverage to catch discrepancies. Early detection of regression: If a new model shows degradation in 10% of cases compared to 2% previously, 50 samples help detect that difference statistically, alerting you fast. Cost-effective evaluation: Running hundreds or thousands per use case is expensive and slow. Fifty test cases strike a practical balance.

That said, teams operating in regulated or high-stakes domains might need more depending on risk tolerance.

Eval Harness Setup: Planner and Router Agents for Scalable Testing

Complex AI stacks rarely rely on a single model. Instead, designers implement pipelines with components like planner agents and router agents:

Planner agent: Breaks down high-level queries into sub-tasks and decides sequences of actions. Router agent: Selects the best-fit specialized model for each sub-query or task.

To evaluate these systems reliably, the harness must mimic these multi-agent interactions:

Simulate the planner generating sub-tasks. Invoke routers to pick models for each sub-task. Collect outputs from specialized models. Verify outputs with human or automated checks, cross-checking answers.

This setup tests not only individual AI models but the orchestration logic critical to multi-agent workflows — a must for reducing hallucinations and improving specialization.

Example: Call Center AI Workflow

Agent Role Function Model Types Planner Parse customer query into intents: billing, technical support LLM planner (e.g., GPT-4) Router Route sub-tasks to best specialist model Intent classifier or rules-based router Verifier Cross-check outputs for inconsistency or hallucinations Secondary model for verification or heuristics

Testing 50 representative queries per intent ensures the planner breaks intents correctly, routers assign tasks effectively, and verification catches hallucinations before hitting live calls.

Hallucination Reduction: Retrieval and Disagreement Detection

One of the most critical eval goals is reducing hallucinations — AI fabrications of incorrect or misleading information. The harness achieves this by:

Retrieval-augmented generation: Feeding the AI relevant documents filtered by a retriever model and verifying the groundedness of outputs. Disagreement detection: Running the same sub-task through multiple models or prompts and flagging outputs that substantially diverge.

Regularly testing these techniques on your 50 test cases per use case creates a safety net that automatically surfaces hallucinated answers to human reviewers before customers see them.

Specialization and Routing: Leveraging the Best-Fit Model

Multi-agent AI architectures often split workloads by specialties: summarization, sentiment analysis, database queries, etc. Routing agents select appropriate models based on the task.

Running AI regression tests with 50 varied cases per use case helps you evaluate:

Whether the router correctly chooses specialized models. If specialization delivers higher accuracy than general models. How the system behaves when models degrade or overfit.

Without sufficient test coverage per use case, it's easy to miss subtle routing bugs that route sensitive data to generic, less accurate models — a recipe for errors in production.

Cost Control and Budget Caps in Test Harnesses

Scaling AI tests can become costly, especially when evaluating multiple models and agents concurrently. Here's how a well-designed harness controls budget:

Use 50 test cases per use case as a guideline to minimize calls while maintaining test quality. Cap API calls and parallel runs: Prevent runaway test costs by scheduling tests during low-traffic periods and limiting concurrent agents. Automate regression triggers: Only run full test suites on critical deploys, with lightweight sanity checks on small samples regularly. Track cost vs. value: Add cost metrics alongside accuracy in your scorecard for weekly measurement ("What are we measuring this week?") to optimize ROI.

Putting It All Together: A Practical Test Case Strategy

Your AI evaluation harness should implement a layered approach combining:

Test Layer Number of Test Cases Focus Key Agent(s) Unit test per sub-task 10–20 Model output correctness, hallucination checks Specialist model, verifier Use case end-to-end ~50 Planner and router accuracy, task routing, cross-agent consistency Planner, router, specialist models Regression test per release Varies (aggregate of use cases) Detect model drift and regressions All agents + verification

Over time, build up domain-specific test cases reflecting real customer queries and edge cases https://seo.edu.rs/blog/how-do-i-classify-ai-requests-by-risk-and-complexity-11146 uncovered in production. Track performance and costs in an automated scorecard with clear pass/fail criteria.

Key Takeaways

Start with ~50 test cases per use case to balance statistical confidence, cost, and evaluation speed. Eval harnesses must simulate multi-agent workflows: planners, routers, specialist models, and verifiers working in concert. Hallucination reduction requires retrieval-augmentation and disagreement detection integrated into your testing. Routing to specialized models improves accuracy but needs dedicated tests to ensure the router agent's decisions are reliable. Control costs with budget caps and targeted test suites aligned with release cycles and risk levels.

Remember, the test cases you build aren’t just code — they’re your AI's safety net. The more thoughtfully you design your eval harness setup with planner and router agents, the fewer surprises you’ll face in production. And yes, always ask yourself: what are we measuring this week?