Traditional AI evals are contaminated and disconnected from enterprise agentic scenarios, making them doubly useless and outright dangerous as tools to determine agentic fitness in enterprise deployments.
PICARD, a framework for agentic AI evaluation, offers memorization-proof AI evaluation through systematic multi-layered randomization and agentic scenario modeling. Measure genuine agentic capabilities, not training data memorization.
Popular AI benchmarks are compromised. As training datasets absorb the public internet, test questions inevitably appear in training corpora, leading to inflated performance that measures memorization rather than genuine problem-solving ability.
They are also completely disconnected from real-world agentic use cases in the real world. What does a score of 87% in your favorie LLM benchmark mean for your multi-agent enterprise automation, for example? (Nothing!)
PICARD generates infinite unique test instances through entity substitution and dynamic data generation - anything from text files to CSVs to databases with multiple related tables. Every test is different, making comprehensive memorization computationally impossible.
Multi-layered randomization creates more test combinations than atoms in the observable universe, making comprehensive memorization physically impossible.
Tests real tool use and multi-step workflows, not just single-shot Q&A. Evaluate file manipulation, database operations, and complex reasoning chains.
Runtime answer key generation provides exact expected values. No "LLM-as-judge" uncertainty or evaluation inconsistency.
Generate realistic CSV files, SQLite databases, and directory structures for each test. Create authentic business scenarios.