Agentic AI systems often suffer from overextension, conflation, and citation mismatch. By building explicit verification layers—ranging from UI-based source grounding to multi-agent fact-checking harnesses—developers can force models to remain faithful to retrieved data, effectively transforming unreliable output into verifiable, high-stakes information.
Topics: AI Agents, RAG, Software Engineering, Trustworthy AI