How much of an AI agent’s execution quality is actually a data problem?

I’ve been thinking about why some agents look impressive in demos but become unstable in real workflows. A lot of discussion around agents focuses on planning, tool use, memory, orchestration, multi-agent collaboration, or better harnesses. These are obviously important. But I’m starting to wonder whether many execution problems are also deeply tied to data. For example: * The agent’s behavior depends on the quality of task examples it has seen. * Tool use depends on whether there are enough clean execution traces. * Evaluation depends on whether test cases reflect real user workflows. * Memory and retrieval depend on whether domain data is structured and reliable. * Failure recovery depends on whether past failures are captured and reused. So when an agent fails, maybe it’s not always just a reasoning issue or a prompt issue. It may be that the surrounding data loop is weak: poor task data, weak feedback data, noisy tool traces, missing domain context, or evaluation sets that don’t match production use. Curious how others think about this: Do you see agent execution quality as mostly a model/planning/harness problem, or as something tightly coupled with the data pipeline behind it? This is also one of the problems I’m trying to explore while working on OpenDCAI/DataFlow, though I’m still not sure how well this approach will work in real agent workflows.