AI Summary: This article shifts the focus of agentic AI development from model capabilities to data infrastructure and context management. It argues that autonomous agents cannot function reliably in enterprise environments without 'self-compound data objects'—data that carries its own metadata, access policies, and provenance.
In 2026, the success of an AI agent is no longer determined by the size of its underlying language model, but by the quality of its context. This post argues that most agent failures are actually context failures—when an AI lacks the definitions, provenance, and constraints needed to act decisively. The author proposes three milestones for 'good' agentic AI, focusing heavily on 'self-compound data objects' (Data Molecules) that package data with its required metadata and governance rules. Furthermore, the article explores how orchestration must evolve from simple agent workflows into complex, graph-based execution pipelines integrating both AI and deterministic code.
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