Obsidian Metadata
References - Building Reliable AI Agents How to Ensure Quality Responses Every Time
What Goes Wrong (and Why)
| Failure Mode | What It Looks Like | Root Cause |
|---|---|---|
| Hallucination | “Sure, your credit score is 980.” | Missing retrieval guardrails |
| Stale Knowledge | Cites 2022 tax rules in 2025 | Out-of-date embeddings or databases |
| Over-confidence | Gives wrong answer with a 0.99 score | Poor calibration |
| Latency Spikes | 12-sec response times at peak | Inefficient agent routing |
| Prompt Drift | Output tone slides from “formal” to “memelord” | Ad-hoc prompt edits |
The Five Pillars of Reliable AI Agents
3.1 High-Quality Prompts
Garbage prompt, garbage output. Test your prompts like you A/B test landing pages. Maxim’s prompt management guide walks through version control, tagging, and regression checks.
3.2 Robust Evaluation Metrics
Accuracy is table stakes. You also need factuality, coherence, fairness, and a healthy dose of user satisfaction. Get the full rundown in our blog on AI agent evaluation metrics.
3.3 Automated Workflows
Manual spot checks don’t scale. Use evaluation pipelines that trigger on every code push. See how in Evaluation Workflows for AI Agents.
3.4 Real-Time Observability
Production traffic is the ultimate test. Maxim’s LLM observability playbook shows how to trace every call, log, and edge case.
3.5 Continuous Improvement
Feedback loops turn failures into features. Track drift, retrain, redeploy, without downtime. Our take on AI reliability details the loop.