DEV TOOLS Signal 457
Data architectures built for human analysts must add foundation, context, and access layers to support autonomous agents
↗
Illustration only Photo by Konstantin Evdokimov on Unsplash
Pramod Sadalage and Prem Chandrasekaren outline why data systems built for human analysts fail autonomous agents and propose a layered architecture to make data trusted, contextualized, and actionable for machines.
Autonomous agents act confidently on flawed data because they lack the human instincts to question suspicious values or apply tribal knowledge. Without explicit data contracts, context layers, and auditable access controls, deploying agentic AI will propagate errors at machine speed rather than streamline processes.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
Unlike human analysts who apply skepticism and tribal knowledge to incomplete data, autonomous agents treat every data value as truth and act on it confidently.
Making data AI-ready requires building a data foundation with contracts and quarantine patterns, a context layer with metrics as code and knowledge graphs, and an action-ready access layer.
Organizations must implement continuous observability, agentic lineage, and staged autonomy to audit how agents use data in decision-making.
THE CLUSTER