Developer tutorial: adding a verifiable data tool to LangChain agents

On November 11, 2026, a developer posted on DEV Community's AI section a roughly 60-line Python recipe for wiring a data tool into a LangChain v1 Agent. The tool either returns an answer together with the underlying query and the data rows behind it, or explicitly returns DECLINED, and the system prompt spells out how to handle each of the two cases. The motivation the author lays out is a familiar failure mode: give an agent a database tool, ask it about last quarter's revenue, and most of the time the answer is fine. Sometimes the tool comes back empty, or comes back with something the agent cannot quite understand, and the agent does what language models do best, it writes a plausible sentence with a plausible number in it. Nothing raises an error, so nobody notices that the number is not real. The post's answer is to build a data tool that behaves differently instead, signalling a refusal rather than letting the model fill the gap with a fabricated figure. The data layer used in the example is FactQL, but the author stresses that the check-then-answer-or-decline pattern is not tied to it and can be applied to any self-built data tool. At this point the material is a single tutorial post: the timeline begins and ends there, with no subsequent implementation, adoption, or related developments documented, so the story currently rests on the approach and sample code as presented.