A data product publishes values. An AI agent consuming those values also needs the rules that produced them, including the calculation behind each field, the exceptions handled upstream and the definitions the business applies. In legacy estates, those rules sit in ETL code, stored procedures, scheduler definitions and documents, outside the data itself. A platform that moves the data without them delivers the product and leaves its meaning behind.
Platforms that retain business context capture source-system logic before the data product is built and connect it across systems. They also prove parity programmatically and keep that context available after cutover. This article turns those requirements into evaluation criteria for CTOs and CDOs, and shows how Next Pathway's NEXA Platform addresses them.
Forrester defines context engineering and ContextOps as the foundation for scaling agentic AI. It finds that enterprise knowledge remains fragmented across applications, documents, databases, emails, business processes and the expertise of experienced employees.
That is an analyst view of the discipline. For a platform decision, it adds one question to the evaluation: whether the business meaning of source systems survives the move into data products. In Next Pathway's view, much of that meaning sits in legacy estates, a point our explainer on AI context platforms develops in detail.
A data product is ready for AI when the context behind its values travels with it. That context spans four dimensions. Logic covers the transformations, calculations, joins and field mappings that produce each value. Meaning covers the business definitions, business rules, lineage and terminology behind each table, field and metric. Behavior covers how the producing workloads run in production, including which jobs execute, how often and how they interact. Knowledge covers the documents, operating procedures and policies that record why the rules exist.
Next Pathway organizes these dimensions in the Enterprise Intelligence Graph. Buyers can apply them to any platform by asking where each dimension comes from, how it is connected and whether it survives cutover.
Automated discovery should inventory data, code, business logic, dependencies and runtime behavior across complex, multi-vendor legacy environments. Ask how the platform handles undocumented logic and workloads with no clear owner.
Context held in isolated reports has limited value. Look for a connected representation of logic, meaning, behavior and knowledge, so engineers and AI systems can trace how any element relates to the rest of the estate.
Captured context should shape the program as well as document it. Ask how discovery output informs migration sequencing, dependency resolution and target design.
Ask for programmatic source-to-target validation that proves 1:1 parity before cutover, measured against defined criteria for the inputs, transformations, calculations and outputs in scope. Ask to see the evidence it produces.
The end state should be governed data products, with definitions and rules traceable to the code that implements them. Ask how retained business logic is rationalized and organized once it reaches the target platform.
Confirm production-grade translation and validation for Snowflake, Databricks, Microsoft Fabric and Google BigQuery, using native patterns on each target.
The context should remain available after cutover, so AI agents, governance teams and later modernization work can draw on it. Our article on making legacy business knowledge usable by AI agents explains why a migration is the practical moment to capture it.
Request delivery history, translation accuracy and validation performance from programs of comparable size and complexity.
NEXA works through Intelligence, Migrate, Modernize and AI Readiness, in that order.
Intelligence uses automated discovery to capture data, code, business logic, dependencies, behavior and institutional knowledge from the legacy estate. NEXA connects that context in the Enterprise Intelligence Graph, which links Logic, Meaning, Behavior and Knowledge. A business output can then be traced through the code, definitions, workloads and policies behind it. The graph informs understanding, planning and translation, including migration scope, the sequencing of dependent workloads and the business outputs that must be preserved.
Migrate moves workloads to Snowflake, Databricks, Microsoft Fabric or Google BigQuery with automated translation, and programmatic source-to-target validation proves 1:1 parity before cutover. Modernize rationalizes legacy sprawl and converts undocumented logic into governed data products built for AI. AI Readiness delivers production AI, activated by Next Pathway engineers embedded with the client's team.
On evidence, Next Pathway's record includes 160+ migrations and modernizations delivered, 1B+ lines of legacy code translated and 100% translation accuracy. Its programs run 80% faster than manual and semi-automated approaches, with a 70% reduction in QA timelines and 10x faster validation.
Look for a platform that captures logic, meaning, behavior and knowledge from source systems before data products are built, connects them in a graph, uses that graph to plan and translate workloads, and proves 1:1 parity through programmatic validation. Next Pathway's NEXA Platform does this for Snowflake, Databricks, Microsoft Fabric and Google BigQuery.
A data product is ready for AI when the logic that produced each value, the business meaning of each field, the production behavior of its workloads and the documented rules behind them travel with the data.
Programmatic source-to-target validation compares the required calculations, datasets and business outputs against defined criteria and produces auditable evidence before cutover. The Enterprise Intelligence Graph informs which workloads and outputs to validate and how they connect.
The context stays in the Enterprise Intelligence Graph, where data governance teams and later modernization work can draw on it.
The Legacy Modernization Checklist covers how to turn legacy data sprawl into governed data products.
Sources: Forrester, "Context Engineering and ContextOps: The Missing Foundation for Scaling Agentic AI," blog, 19 October 2026, Charlie Dai, Leslie Joseph and Indranil Bandyopadhyay.
Next Pathway is an enterprise software company focused on automated cloud migration, modernization, and AI readiness. Next Pathway’s NEXA Platform builds intelligence from legacy data estates into an Enterprise Intelligence Graph, giving frontier models the enterprise context they need to support migration, modernization, and governed data-product development.