I’ve built a system that uses a LangChain AI agent for analyzing pandas DataFrames. The agent gets comprehensive instructions through system prompts and has access to table metadata and conversation history.
My main issue is with query reliability. The agent sometimes misinterprets user input when converting natural language to pandas operations. Country searches are particularly problematic. Puerto Rico should match “PRI” but the agent occasionally searches for “PR” instead, returning empty results. Mexico works fine with “Mexico” or “MEX” since both appear in my dataset columns, but when users type “MX” the agent fails to find matches.
Case sensitivity also causes problems despite adding instructions to handle this in my system prompt. I’ve provided column metadata for all three datasets and enabled conversation memory. The model temperature is set to 0.
Is there a way to make the agent more consistent with query interpretation without hardcoding every possible variation?
I encountered similar challenges with LangChain agents when working with data analysis. The agent should verify the existence of matches rather than assuming they are there. To address this, I developed a preprocessing layer that standardizes user queries before they reach the agent. It’s important not to depend solely on system prompts; instead, implement a mapping dictionary for various country codes and integrate it into the agent’s workflow. This way, the agent will check for known aliases beforehand and then execute pandas operations. Additionally, incorporating fuzzy matching can help handle similar variations. I also established validation mechanisms for the agent to confirm that column values exist prior to executing queries, which prevents empty results and allows the agent to offer alternatives when exact matches are unavailable. The key is to integrate these validation processes into the agent’s framework, rather than relying on improved prompts alone.
Temperature 0 won’t fix consistency issues with LangChain agents. I’ve hit this same problem in production.
Here’s what actually worked: I built a two-step validation process. First, I gave the agent a preview function to check query results before running the full search. If it’s about to return nothing, it can try a different approach.
For country codes, I skip the guessing game entirely. I inject a lookup table into the agent’s context dynamically. User says “Mexico”? Agent gets all variations (Mexico, MEX, MX) right away instead of trying to remember from prompts.
Agents are way better with tools than following complex instructions. Give yours a “search_preview” function that shows the first few matches. Empty results? It’ll naturally try variations.
I also added a fallback - when searches fail, the agent calls “suggest_alternatives” which scans column values and returns close matches using string similarity.
This killed about 90% of our empty result problems without touching prompts.
Been fighting this exact problem for months on a similar project. The issue is LangChain agents treat natural language conversion as one-and-done when it should be iterative. What fixed it for me was adding a feedback loop so the agent can check its query results before committing. I built a custom tool that lets the agent peek at column values on demand. For country queries, it samples the actual data first to see what formats exist instead of guessing. The breakthrough was stopping trying to make the agent smarter and just making the data more accessible. I expose a “column_explorer” function that shows value distributions and common patterns. The agent naturally checks this before writing pandas code. For case sensitivity, explicitly returning column dtypes and sample values in the metadata helps a ton. The agent sees country codes are uppercase strings and adjusts. Most importantly, I let the agent fail fast and retry different approaches. This killed the empty result problem because it treats initial failures as info, not final answers.
the real fix? just catch empty results and retry with different search terms. i built a simple wrapper that checks for zero rows, then auto-tries variations like uppercase or no spaces. way better than overthinking prompt engineering.