Where AI Systems Fail
As AI systems evolve from simple prompts to autonomous agents, the ways they fail evolve too. Understanding failure modes is the first step to evaluating them.
Quick Answer
Most AI failures come from the system, not just the model. This page maps the AI stack to failure modes so teams can evaluate the right layer and fix the real cause.
TL;DR
- Failures can originate in data, retrieval, prompting, or product logic.
- Map the system end-to-end before choosing metrics.
- Evaluate the layer that can actually be changed.
FAQ
Where do AI failures usually happen?
Most regressions come from upstream data changes, retrieval errors, or policy shifts, not from the base model alone.
How do I scope an eval?
Start with the system boundary, identify failure points, then choose metrics and data for the highest-risk nodes.
What should I measure first?
Measure retrieval quality and policy adherence first in RAG systems, then answer quality and user impact.
1. The Rapid Evolution of AI Systems
We are witnessing a shift from static models to dynamic, complex systems. As the complexity of the system increases, the difficulty of evaluation compounds.
RAG (Retrieval-Augmented Generation)
Systems that look up external private data before answering. The challenge is evaluating both the retrieval (did I find the right doc?) and generation (did I summarize it correctly?).
Learn about RAG (LlamaIndex) ↗Agentic AI
Systems that use tools (API calls, web search) to solve multi-step problems. Evaluation here is about reasoning paths and safety—did the agent call the delete API by mistake?
Learn about Agents (LangChain) ↗Multimodal
Systems that see, hear, and speak. Evals move beyond text matching to semantic understanding of images and audio alignment.
Learn about Multimodal (OpenAI) ↗2. The State of Evals: Why "Vibes" Fail
In traditional software, we have unit tests: assert 2 + 2 == 4. In probabilistic
AI, we have "Vibes-Based Evaluation"—looking at an output and saying,
"Yeah, looks good."
This fails in production because:
- Drift: A model update might fix one bug but break 10 others (Regressions).
- Scale: You can't manually review 10,000 logs a day.
- Subjectivity: "Good" means different things to Legal vs. Marketing.
The Industry Shift
Leading companies are moving to Custom Evaluation Frameworks. Instead of using generic benchmarks (like MMLU), they build "Golden Datasets" from their own production logs and evaluate against business-specific rules.
3. The Tooling Landscape
You don't need to build everything from scratch. The open-source ecosystem provides powerful primitives.
DeepEval
Open SourcePytest for LLMs. Best for developers who want to run evals as part of their CI/CD pipeline.
Ragas
Open SourceSpecialized metrics for RAG pipelines (Context Precision, Faithfulness).
Arize / Phoenix
Open Source CoreObservability first. Great for tracing complex agent workflows and visualizing traces.