Intelligence is hardly a limiting factor these days. Most production agents fail to give the right response because of evidence failures. Because the agent's picture of the world is stale, conflicting, unattributed, and no amount of prompt engineering or model upgrades can fix that as retrieval can find the text, but not the truth. Worse, when the system recommends action and if evals do not find the problem with the action, the feedback might never come or comes via a customer-facing issue. The solution pattern we use: an ontology that defines what entities and claims mean, a graph that holds the evidence with its source, timestamp, and lineage, a vector index for finding candidate evidence, and an orchestrating agent on top that resolves conflicts, cites what it used, and refuses when the evidence isn't there.
Avneesh Sharma is a software engineer focused on production AI/ML systems, specializing in LLMOps, evals, RAG, and scalable architecture. He brings a practical, software-first mindset to building reliable AI beyond the prototype stage. He writes about AI infrastructure and engineering judgment at Build Tech Career.
