An AI agent working on enterprise data needs to know what each field means, which transformation produced it, which jobs consume it and which policy governs it. The data carries none of that. In large enterprises, much of that context was never written as documentation. It was written as code, in stored procedures, ETL mappings, SAS macros and scheduler definitions, and it has run in production for decades.
Gartner has identified AI context platforms as a strategic technology trend. This article explains the emerging category and makes a separate engineering argument: for large enterprises, a substantial part of the context AI needs resides in the legacy estate. Next Pathway's NEXA Platform is built on that argument. It combines enterprise intelligence, automated legacy data modernization and governed data products to prepare enterprises for production AI.
Gartner named AI context platforms one of its top strategic technology trends for 2027. Gartner describes them as platforms that provide AI agents with the contextual knowledge they need to interpret inputs, make decisions and act in line with business objectives.
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. It also concludes that the winners in the agentic AI era will build better context platforms.
For technology leaders, the practical point is clear. Frontier models bring general reasoning. Knowledge of how a specific enterprise operates has to be supplied from its systems and institutional records.
Neither firm singles out legacy systems. Next Pathway's view is that the business knowledge buried in legacy estates is the context most enterprises are missing.
The reason is architectural. Production code is an important record of the business logic an enterprise executes. Documentation and design notes may diverge from production as systems change. Stored procedures, ETL mappings and scheduler definitions record the rules the business runs on today. They capture how a value is calculated, which exceptions are handled, which job runs before another and which downstream reports rely on the output. Many of the engineers who wrote that logic have since left their roles. That makes code, lineage and runtime history essential sources for reconstructing how the business operates.
A table gives an AI agent columns and rows. The transformation logic, dependencies and production behavior around that table tell the agent what each value means and how the business uses it. When an enterprise moves its data to a modern platform without carrying forward the logic and dependencies, its AI systems may receive values without the rules that produced them. Capturing those rules is how a data estate becomes enterprise context.
NEXA captures enterprise context from the legacy estate in an Enterprise Intelligence Graph. The graph connects four dimensions of the estate. Logic traces column-level transformations, calculations, joins and code dependencies, so a business output can be followed back through the code that produces it. Meaning holds the semantic model, lineage and business definitions that describe what the data represents. Behavior records how the estate runs in production, including which workloads execute, how often and how they interact. Knowledge brings in the documents, operating procedures, architecture notes and policies that encode institutional rules.
The value sits in the connections. A calculation in one ETL job may feed a report owned by finance and support a regulatory filing described in a policy document. The graph links those pieces, so engineers can trace how a business result is produced.
NEXA works through Intelligence, Migrate, Modernize and AI Readiness. Intelligence builds the Enterprise Intelligence Graph before a single line of code is translated. Migrate moves the estate to Snowflake, Databricks, Microsoft Fabric or Google BigQuery with automated translation and 1:1 parity proven before cutover. Modernize rationalizes legacy sprawl into governed data products built for AI. AI Readiness delivers production AI, activated by Next Pathway engineers embedded with the client's team.
A translated workload provides a direct test of whether required business logic was carried forward. The target workload must produce results that meet defined parity criteria against the legacy source. Programmatic source-to-target validation checks those results and proves 1:1 parity, so the translated logic is tested against source outputs before production cutover.
Next Pathway's record includes 160+ migrations and modernizations delivered, 1B+ lines of legacy code translated and 100% translation accuracy.
For the CTO, the Enterprise Intelligence Graph provides an architectural map of how systems connect and informs modernization decisions.
For the CDO, definitions and rules stay traceable to the code that implements them, giving data and AI teams a governed foundation for data products and agent workflows.
For the CFO, modernization can produce two lasting assets: a modern data platform and the enterprise context that production AI relies on. Automation also shortens delivery timelines, with programs running 80% faster than manual and semi-automated approaches.
Next Pathway's NEXA Platform brings the three together. It builds an Enterprise Intelligence Graph of the legacy estate, uses it to automate migration and modernization, and turns retained business logic into governed data products for production AI.
Gartner describes AI context platforms as platforms that provide AI agents with the contextual knowledge they need to interpret inputs, make decisions and act in line with business objectives. NEXA Platform addresses a specific part of that challenge: capturing the enterprise context embedded in legacy estates.
Forrester defines context engineering and ContextOps as the foundation for scaling agentic AI. In Next Pathway's view, an AI context platform is the infrastructure that captures and connects that context across an enterprise estate. NEXA captures the logic, meaning, behavior and knowledge held in legacy systems and connects them in its Enterprise Intelligence Graph.
NEXA works across complex, multi-vendor legacy environments, including Teradata, Netezza, Oracle, Hadoop, SAS, Informatica and IBM DataStage. Target platforms include Snowflake, Databricks, Microsoft Fabric and Google BigQuery.
The Enterprise Intelligence Graph informs which calculations, datasets and business outputs must match. Programmatic source-to-target validation compares the required calculations, datasets and business outputs to establish 1:1 parity before cutover.
The Enterprise Intelligence Checklist explains what an Enterprise Intelligence Graph contains and how to build one across a legacy estate, from discovery through the Logic, Meaning, Behavior and Knowledge dimensions.
Sources: Gartner, "Gartner Identifies the Top Strategic Technology Trends for 2027," press release, 20 October 2026. 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.