The Hidden Risks of Agentic AI Systems — And Why Verification Matters
Agentic AI systems that let LLMs fetch and analyze data look powerful on the surface. But a growing body of research reveals dangerous failure modes — from silent hallucinations to context window degradation — that make verification essential.
A recent deep-dive by Eric Glover at Applied Ingenuity explored what happens when you build an agentic system that lets an LLM fetch and analyze financial data. The findings are a cautionary tale for anyone deploying AI agents in production.
The Problem With "LLM as Analyst"
Agentic AI architectures — where an LLM decides what data to fetch, processes it, and synthesizes results — are becoming increasingly common. The pattern is seductive: give the model tools, let it reason, and watch it work. But Glover's instrumented prototype revealed five categories of business risk that are easy to miss in demos and hard to catch in production.
Silent Failures Are the Worst Failures
The most dangerous finding is what Glover calls the "helpfulness paradox." When an LLM encounters a database error or missing data, it doesn't fail gracefully — it improvises. The model produces plausible-sounding analysis that looks authoritative but is fabricated. There's no error message, no warning. Just confident, wrong answers. This problem gets worse as context windows fill up. Research cited in the analysis shows accuracy can drop significantly once context utilization passes 40-50% — precisely the point where complex multi-step analyses tend to land.
Why This Matters for Software Teams
These same failure modes apply to AI-generated code. When an LLM writes or modifies code, it can introduce subtle behavioral changes that look correct in isolation but break real-world API contracts. The code compiles, the obvious tests pass, but the actual behavior has shifted. This is exactly the gap that verification tools need to fill. You can't rely on the AI to tell you when it got something wrong — by definition, it doesn't know.
Verification Closes the Loop
The solution isn't to stop using AI agents. It's to verify their output against ground truth. In data analysis, that means deterministic computation pipelines. In software development, that means replaying real traffic against new code and comparing the results at the packet level. When you can show an AI agent exactly where its output diverges from expected behavior — and feed that information back into the loop — you get the productivity benefits of agentic AI without the silent regression risk.
The era of "trust but verify" for AI-generated work isn't coming. It's here.