Quality assurance · Production

Troubleshooting AI agents: data-driven techniques for improving AI agent performance

The problem

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.

First attempt

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.

Workflow diagram · grounded in source
1
User message received
trigger
“The agent receives the user's message, generates a database query (or multiple queries), and the data gets retrieved from the database and passed to the agent.”
2
Query generation
ai_action
“The agent may miss important information from the user's message, get fixated on a specific term, and generate a too-specific query or, on the other hand, generate a too-general query.”
3
Vector database retrieval
integration
“the data gets retrieved from the database and passed to the agent”
4
Document reranking
ai_action
“we need to rerank the documents to get the most relevant ones. For reranking, we use an LLM model”
5
Interaction loop control
ai_action
“The agent decides which tools to call and how many times. Depending on the framework or libraries you use, the agent may be allowed to call the tool multiple times. If we limit the number of calls to strictly one call every time, we no l…”
6
Answer synthesis
output
“At some point, the agent has to synthesize the final answer.”
Reported outcome

(not stated)

Reported stack
vector databaseLLM modelembedding model
Source
https://mlops.community/blog/troubleshooting-ai-agents-advanced-data-driven-techniques-of-improving-ai-agent-performance
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

(not stated)

What tools did this team use?

vector database, LLM model, embedding model.

What failed first in this deployment?

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 repe…

How is this quality assurance AI workflow structured?

User message received → Query generation → Vector database retrieval → Document reranking → Interaction loop control → Answer synthesis.

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