In July 2026, Gartner® published a research note titled Capitalize on Agentic SaaS Disintermediation Aspirations, With Service Providers to Execute. We believe it describes a shift that has been building across enterprise technology: organizations are beginning to replace rented application logic with AI agents running on data infrastructure they own. Onix was among the companies named in the research.
For most of the last two decades, enterprises rented their software logic through per-seat subscriptions because building it in-house was slow and expensive. Agentic AI is starting to change that math. Agents can now learn the business logic sitting inside existing systems and run it directly on data infrastructure the enterprise owns, which turns a large, recurring cost into something the company controls. It is early, but the direction is clear, and it has real implications for how enterprises buy, build, and run software.
What follows is Onix’s own point of view on the shift the note examines, and where our work fits within it.
How agentic AI is reshaping enterprise software
Moving business logic out of rented applications and onto infrastructure the enterprise owns is not as simple as swapping one system for another. It raises hard technical questions, and the first one is always reliability.
Gartner® writes: “The immediate objection to this notion is reliability. Enterprise systems are deterministic, while most agentic AI systems are largely probabilistic, and for mission-critical processes, that variability is unacceptable. However, providers are increasingly demonstrating that balancing two architectures renders the reliability problem as solvable ” The answer taking shape across the industry is to split the two, let agents reason at design time, and keep execution governed, auditable, and repeatable at runtime.
In our experience, moving in that direction means solving three hard problems:
- Semantic extraction. Teaching agents to read and reproduce the business logic buried inside an incumbent system. Validation rules, workflows, approval chains, much of it undocumented. This is closer to reverse engineering than to migration.
- Reliable execution. Enterprise systems are deterministic. Agents are probabilistic. The pattern emerging in the field is to let agents reason at design time and keep runtime governed and auditable.
- An independent system of record. A data layer the enterprise owns, on modern cloud platforms like BigQuery, Snowflake, or Databricks, rather than one locked inside a vendor’s application.
On economics, Gartner® states: “Advanced enterprises are replacing SaaS with new, agentic AI solutions engineered for reliable and durable execution architectural patterns, achieving more than 60% gains while maintaining strict governance.” The pace is not hypothetical either. Gartner® predicted that 40% of enterprise applications will feature task-specific AI agents by the end of 2026, up from less than 5% in 2025, and that over 40% of agentic AI projects will be canceled by the end of 2027.
The appetite is real, and so is the failure rate.
How Onix closes the context gap with Wingspan
This is the problem Wingspan was built for. Wingspan is an agentic AI platform that gives enterprise agents the business context they need to act reliably, then puts them to work on migration, modernization, and daily operations.
At its center is the Semantic Twin, a governed model of the enterprise that captures data, relationships, KPIs, processes, and the rules that connect them. Because agents work from that shared context rather than from disconnected tables, their output can be traced back to its source rather than taken on faith.
What Wingspan does:
- Builds and maintains a governed Semantic Twin, with no manual ontology work to slow the start.
- Migrates and modernizes data and code onto modern cloud platforms.
- Grounds agents in real business context, so answers are accurate and explainable.
- Wraps every action in governance, with lineage, policy, and human approval built in.
What enterprises can work toward with it:
- A Semantic Twin stood up in 4 to 6 weeks, proven in one domain before scaling.
- Up to 95 percent accuracy on governed KPIs and metrics with semantic grounding.
- 20 to 35 percent lower operational cost, with ROI in 3 to 6 months.
- Up to 45 percent lower maintenance cost on legacy systems.
Migration is the entry point. The Semantic Twin is what makes it permanent, because it deepens with every system it maps, so the next initiative starts with more context than the last.
The models were never the bottleneck
Our view is that context and reliability, not model quality, decide whether this works. The evidence supports it. MIT’s NANDA study found that 95 percent of enterprise generative AI pilots delivered no measurable return, and traced the cause to a learning and context gap rather than weak models. At the same time, Gartner® expects 40 percent of enterprise applications to embed task-specific AI agents by the end of 2026. And it predicts more than 40 percent of agentic AI projects to be canceled by the end of 2027 on cost and unclear value.
All of it points the same way. The technology works, and the teams that succeed are the ones that give agents a trustworthy model of the business first. That is the work the Semantic Twin is built for, and the reason customers stay with it long after the first migration is done. If your organization is weighing a major migration or modernization, that is the right moment to build the context layer in from the start. We are happy to talk through what that looks like.
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