I’m building an application that uses a LangChain AI agent for analyzing pandas DataFrames. The agent gets comprehensive instructions through system prompts, along with conversation history and table metadata. My main issue is that the agent sometimes misinterprets user queries when converting them to pandas operations.
The system works well most of the time - it can answer data questions and generate charts with plotly based on the detailed prompt instructions I provide.
But there are frustrating inconsistencies. When users ask about countries, results vary unpredictably. Puerto Rico should match code “PRI” but sometimes the agent looks for “PR” instead and finds nothing. Mexico works fine with “Mexico” or “MEX” since both appear in different DataFrame columns. But if someone types “MX” for Mexico, the agent might search for that exact string and return empty results with no charts or answers.
Case sensitivity also causes problems even though I added instructions to handle it in the system prompt.
I’ve included detailed metadata for all columns across my 3 datasets in the prompt. The agent has conversation memory enabled too. I don’t want to hardcode every possible country variation or edge case. The model temperature is set to 0.
How can I make the agent more reliable at interpreting user queries?
I ran into the same DataFrame headaches with LangChain agents. Here’s what fixed it for me: I split the process into two phases. First, the agent figures out what the user actually wants and pulls out the key info. Then it writes the pandas code. Don’t just rely on the system prompt - add a validation step that uses fuzzy string matching to check for similar values in the DataFrame before running the final query. For country codes, I have the agent scan the unique values in relevant columns first. This way it sees what formats actually exist in the data and adjusts its search instead of guessing. The trick is making the agent look at the real data structure when it’s processing the query, not just relying on static metadata you fed it earlier.
I solved this by adding a search strategy chain to my agent workflow. Instead of jumping straight to pandas operations, I made it explore the data first to see what values actually exist.
Here’s what I do: when the agent spots a search term like “MX” or “PRI”, it runs a quick scan across relevant columns using .str.contains() with case=False. This shows all potential matches before running the final query.
I also built in simple scoring - the agent ranks matches by how close they are to the search term. Someone searches “MX”, it finds “Mexico” and “MEX” in the data, then picks the best match based on string similarity.
The key insight? Make it a two-step process: discovery first, then execution. The agent uses a few extra tokens exploring the data but gets way more reliable results.
For your Puerto Rico example, this would catch that “PRI” exists in the data even when the user types “PR”. The agent learns from the actual DataFrame instead of guessing based on general knowledge.
your agent’s fuzzy matching logic needs work. try adding a preprocessing step to normalize country names before processing. I ran into the same thing and used a country code mapping dict as backup when exact matches failed. really helps with abbreviations and variations.
Honestly, just add a similarity check before the pandas query runs. Use difflib or fuzzywuzzy to compare what the user typed against your actual column values. If the match confidence is low, make the agent ask for clarification instead of returning nothing. Saves tons of debugging time.
Temperature 0 might be working against you. I’ve found that bumping it up slightly (0.1-0.2) helps with query interpretation without hurting accuracy. The agent gets too rigid at zero and can’t handle variations it hasn’t seen. Also, add a validation step where the agent checks if its pandas query returned empty results, then automatically tries alternatives like partial string matching or case-insensitive searches. You get reliability without hardcoding every edge case. You could also add a fallback that prompts the user to clarify when there are multiple ways to interpret something.