Agentic validation tests whether AI agents — the LLMs behind tools like ChatGPT, Claude, and Perplexity — would include, rank, and recommend your product when given a real decision task. Instead of asking people what they think of your idea, AI agents are given tasks like "which of these products would you recommend for this situation?" and their reasoning, choices, and objections are observed and graded.
Why it matters now
More of your future customers will never see your website: they ask an AI assistant for a recommendation and act on the answer. If an AI agent skips your product today, that is a real, usable signal — the same way a human saying "I would not buy this" would be. Validating is the only tool that validates this as a first-class experiment.
How the two-pass engine works
The agentic engine runs two isolated passes so the machine’s judgment is never contaminated by your solution:
- Pass 1 — Discovery: the agent receives your problem and target only. The solution is hidden. Output: the agent’s decision model — criteria, weights, deal-breakers, and trust signals for this problem and segment.
- Pass 2 — Validation: the same agent now also sees your solution and a real competitor set. Output: inclusion decision, rank, recommendation, objection list, and a full reasoning trace.
What you get
- Agent-Readiness grade (A–F) summarising machine-observable metrics
- Decision-Model map: the criteria and deal-breakers agents apply
- Inclusion and ranking results vs real competitors
- Actionable recommendations to improve how agents see your product