How agentic AI prevents hallucinations without over-engineering validation
Agentic AI validates itself in real time, creating audit trails that help product teams build both reliable systems and the transparency needed for legal compliance when AI decisions need explanation.
I've been working with product teams on reducing AI hallucinations, and this New Stack article hits on something practical: how to build validation without creating Byzantine approval systems. The manufacturer example shows the core problem—AI generating device troubleshooting steps that don't exist in their knowledge base.
Their approach centers on agentic AI that validates itself in real time. The AI evaluates its own responses against known facts before finalizing answers, then cross-references multiple sources for consistency. Instead of expanding training datasets or adding more human reviewers, you're building self-correction into the process itself.
For legal teams, the Chain-of-Thought prompting technique creates something useful: an audit trail showing how your system reached specific conclusions. That transparency matters when you need to explain AI decisions to regulators or in disputes.
The data quality discussion shifts focus from volume to verification and source control. Human-generated data and domain diversity matter more than accumulating training examples. For product teams, this means your content strategy and data curation practices directly affect AI reliability—making these decisions part of risk management, not merely technical choices.
Niranjan Krishnan
