Context engineering is becoming an enterprise data discipline
Reliable agents need more than model access. They need trusted context, permissions, metadata and a clear way to know which information should shape an action.
Analytics, data platforms, artificial intelligence and responsible adoption. Practical guidance for evaluation, implementation, governance and day-to-day operation.
Reliable agents need more than model access. They need trusted context, permissions, metadata and a clear way to know which information should shape an action.
Read the lead insightReliable agents need more than model access. They need trusted context, permissions, metadata and a clear way to know which information should shape an action.
AI observability tools should help teams trace prompts, retrieval, tool calls, cost, quality and policy without creating a second opaque system.
Agents expose weaknesses in ownership, semantics and access faster than traditional analytics because they act on context rather than only display it.
The stack is moving toward multi-model flexibility, agent control planes, trusted context, evaluation and policy-aware action layers.
A practical checklist for evaluating retrieval quality, grounded answers, tool use, refusal, escalation and change over time.
A qualitative model for assessing strategy, context, evaluation, controls, operating ownership and measurable value.
Decisions about modern data platforms improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about modern data platforms improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Responsible AI does not begin with a thick policy. It begins with a few documents that help people make consistent decisions while the work is still moving.
Decisions about modern data platforms improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about privacy engineering improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about business intelligence improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about master data management improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about vector databases improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about vector databases improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about AI governance improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about AI governance improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about AI governance improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about AI governance improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about analytics engineering improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about analytics engineering improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about analytics engineering improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about analytics engineering improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
Decisions about analytics engineering improve when data & ai leaders define the work, ownership, evidence and exceptions before selecting or expanding technology.
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