From MVP to Production: A Systems Approach to Detecting AI Hallucinations

As agentic systems mature, hallucinations stop being simple model-level bugs and start revealing themselves as system-level failures. When your architecture consists of a graph of interdependent agents and tools, a single incorrect output often points to a deeper issue: the system didn’t behave the way it was supposed to. In these cases, the question isn’t just “Was the answer correct?” It’s “Did the system follow the right path? Was the output grounded in actual data? Did retries or revisions meaningfully improve the result?”

To catch and correct these issues, teams need more than accuracy checks. They need system-level observability. This post introduces a practical, stage-based framework for detecting and preventing hallucinations—from the earliest MVP stage to full-scale production. Each phase helps teams build toward more robust, reliable AI by making system behavior visible, testable, and aligned with user expectations.

In the MVP stage, the goal is visibility with minimal engineering overhead. At this point, you’re not aiming for perfect performance—you’re trying to understand how your agents behave in the wild. One of the first steps is to log path fidelity: are agents actually following the expected execution routes through the graph, or are they branching off in unpredictable ways? Tracking retry rates is equally important. If agents are frequently re-running steps—especially after tool failures or ambiguous outputs—it’s a sign of silent failure and recovery attempts. You also want to introduce groundedness checks at key nodes in the graph. Are your most important outputs clearly tied to source data or tool responses, or are they being hallucinated? These kinds of lightweight checks reveal early-stage issues that might otherwise go unnoticed until they’re embedded in larger, more complex workflows.

As systems approach deployment, the focus shifts toward connecting backend observability with frontend experience. This is the Beta stage, where internal signals should start shaping UX and trust. One powerful move is to surface groundedness scores directly in the UI. This not only helps users gauge reliability but gives your team a feedback loop on when and where agents might be overconfident or fabricating. It’s also a good time to A/B test reflection or retry logic. Does prompting the agent to reconsider its response actually lead to better outcomes? Are revisions improving quality, or just introducing variance? These experiments help refine behavior before it reaches users at scale and strengthen the link between internal metrics and external value.

Once the system hits production, the emphasis is on long-term reliability. At this point, you’re not just catching bugs—you’re working to prevent them systematically. Start by logging execution path drift at scale. Are agents consistently following expected routes, or are new conditions pushing them into unintended flows? Deviations from the baseline graph can indicate instability that’s otherwise invisible. In parallel, run periodic entailment audits offline. These involve checking whether agent conclusions are logically supported by their context and inputs. It’s a low-cost, high-impact way to ensure your agents aren’t just fluent—they’re actually reasoning within bounds.

Throughout all three stages, the theme is consistent: hallucinations are not just output problems; they’re observability problems. And solving them requires treating AI reliability as a systems discipline, not just a modeling one. By instrumenting agent behavior early, validating assumptions through real-world tests, and operationalizing checks at scale, teams can move from prototypes to production-grade AI with confidence. This is how you build trust—not just in your model, but in your system as a whole.

Key Takeaways

1. Hallucinations are system-level failures
   They're often caused by breakdowns in agent behavior, not just model prediction errors.

2. System observability is essential
   You need visibility into how agents move through the graph, when they retry, and whether their outputs are grounded in real data.

3. Track behavior from MVP to Production
   Use a phased approach:
   - MVP: Log execution paths, retries, and add simple groundedness checks.
   - Beta: Surface internal signals in the UI and test retry/reflection logic.
   - Production: Monitor drift at scale and run offline entailment audits.

4. Connect backend signals to user trust
   Showing groundedness or confidence scores in the UI helps build user intuition and improves reliability.

5. Prevention beats detection
   By operationalizing these checks, you catch issues early and avoid regressions later.

6. AI reliability is a systems discipline
   It’s not enough to fix model outputs—you need to understand and shape the entire system’s behavior.