quality_assurance · workflow
Troubleshooting AI agents: data-driven techniques for improving AI agent performance
AI agents built with a vector database and LLM can fail in multiple distinct ways: flawed query generation, data retrieval failures from vocabulary mismatch or poor chunking, LLM reranker errors, interaction-loop runaway, incorrect answer synthesis, and excessive cost or latency.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User message received
The agent receives the user's message to begin the workflow.
Tools used
vector databaseLLM modelembedding model
Outcome
(not stated)
What failed first
Common failure modes include vocabulary mismatch between queries and documents, chunking strategies that separate a problem from its solution, ignored metadata, LLM rerankers selecting wrong documents, and agents repeating the same tool call in a loop.
Grounding & classification
Source type: technical build writeup
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ai agentknowledge searchragknowledge basefailure mode describedtools describedworkflow describedtechnical build writeupquality assurancerag answering