Description
We have a production AI Agent built with LangChain (Python) that is live and handling real user queries. Recently, we’ve encountered a set of non-trivial runtime issues that only appear under realistic load and multi-step reasoning scenarios. This is not a “build an agent from scratch” job. I’m looking for an engineer who has shipped LangChain-based agents to production and can quickly diagnose and fix subtle issues that don’t show up in simple scripts or playgrounds. The Scenario (Please Read Carefully) Our agent runs as an async FastAPI service and performs multi-step reasoning using AgentExecutor with a ConversationBufferMemory. The agent has access to several tools (APIs + vector search). Under normal conditions, it works well. However, under the following conditions, we see inconsistent behavior: When the agent executes more than 3 sequential tool calls in a single turn And the intermediate tool outputs contain large JSON payloads And the user message triggers both retrieval-augmented context + tool selection We observe: Memory truncation issues:The ConversationBufferMemory occasionally drops earlier messages or tool outputs, even though max_token_limit is set. The agent then “forgets” critical context mid-chain. Malformed LLM responses:The LLM sometimes emits tool call JSON that is valid JSON but semantically incorrect (e.g., hallucinated tool names or mismatched arguments). LangChain does not reliably catch these, leading to silent failures or misleading final