How to Test an AI Agent Without Writing a Single Test
Summary
Current agent evaluation methods, such as hand-written test sets and LLM-as-judge pipelines, suffer from coverage failures and shared blind spots, making them unreliable for critical applications like compliance. A new approach proposes using the source document itself as both the test set and the oracle. This involves transforming the document into a "DocumentGraph"—a knowledge graph of (head, relation, tail) triples, along with an Exact Numerical Memory (ENM) for precise values. This structured representation enables deterministic generation of diverse question categories (e.g., plausibility, multi-hop reasoning, adversarial framing) and automatic, verifiable grading without human intervention or reliance on another LLM. The process includes parsing, storing, and validating the graph, followed by a four-stage pipeline for test set generation and a Design of Experiments (DoE) approach to systematically vary question presentation factors, providing actionable diagnostics on agent performance.
Key takeaway
For AI Engineers building document-grounded agents, relying on hand-written prompts or LLM-as-judge systems introduces critical coverage gaps and blind spots. You should adopt a DocumentGraph-based evaluation pipeline to automatically generate and grade test questions directly from your source corpus. This approach provides audit-grade traceability and continuous regression testing, ensuring your agent's reliability and defensibility in production by shifting from subjective prompt writing to objective, structure-derived verification.
Key insights
Leverage document structure to automatically generate and grade agent evaluation questions, ensuring comprehensive and auditable testing.
Principles
- The source document is the ultimate ground truth.
- Deterministic generation and grading eliminate human bias.
- Structured data enables precise, auditable verification.
Method
Parse documents into a DocumentGraph and Exact Numerical Memory. Generate diverse questions and grade answers deterministically against this graph. Use Design of Experiments for systematic presentation factor testing.
In practice
- Implement a DocumentGraph for compliance agents.
- Automate test generation from structured documents.
- Use DoE to diagnose agent performance factors.
Topics
- AI Agent Evaluation
- DocumentGraph
- Knowledge Graph Construction
- Automated Testing
- Design of Experiments
Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Agus’s Substack.