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The Infrastructure Race Behind Agentic AI
• 4 min read
Much of the conversation around agentic AI focuses on models: reasoning improvements, autonomy levels, and what the next generation of systems will be capable of. But as adoption accelerates, the real competition is shifting away from models and toward something less visible, and far more decisive: infrastructure.
In the 2026 State of Agentic AI report, we surveyed more than 1,000 developers and product leaders building or influencing agentic systems. The data is clear. 67% are already building or shipping agentic workflows. 85% believe agentic AI will become table stakes within three years. And 94% say they would consider switching vendors for stronger, more scalable, and more compliant agentic AI capabilities.
That is not a signal about model preference. It’s a signal about infrastructure readiness.
Agentic AI refers to systems that can plan and execute multi-step tasks with a degree of autonomy. These systems typically:
Unlike simple prompt-response AI, agentic systems interact directly with the software stack. They access email, calendars, CRM systems, internal tools, and external APIs. As a result, their reliability depends not only on model intelligence, but on the quality and structure of the infrastructure beneath them.
The shift from experimentation to production is already underway. With 67% of teams building or shipping agentic workflows, organizations are no longer debating whether agentic AI matters. They are asking whether their infrastructure can support it.
As agentic systems move into real-world environments, buying criteria are evolving. Reliability, price, and feature depth still matter. But they are no longer enough. Teams need assurance that the platforms they depend on can support autonomous workflows at scale, with governance, observability, and clean underlying data.
This shift is practical. Developers and product leaders are focused on reducing operational friction, accelerating execution, and ensuring AI-driven workflows perform consistently under real-world conditions. When the foundation is unclear, confidence erodes. Our data shows buyers are prepared to reassess their stack if vendors can’t meet these expectations.
Agentic AI depends on planning, reasoning, memory, and tool use. Yet those capabilities are only as strong as the systems beneath them. Autonomous workflows require structured access to operational data, especially in systems where work actually happens, such as email and calendar environments.
If that data is fragmented or inconsistently normalized, agents lose context. If APIs are brittle or unpredictably rate-limited, workflows stall. If compliance and governance controls are layered on after deployment instead of built into the architecture, risk compounds over time.
Consider this practical example: An AI agent responsible for scheduling customer meetings across time zones must access real-time calendar availability, respect permissions, interpret email threads accurately, and update systems without introducing errors. Even a highly capable model can’t compensate for inconsistent data structures or unreliable APIs. Without a strong infrastructure layer, autonomy remains constrained.
This is why the next phase of agentic AI competition will not be decided at the model layer alone. It will be determined by which vendors can provide scalable, compliant, production-ready infrastructure.
The finding that 94% of developers would consider switching vendors for stronger agentic AI capabilities reflects a broader market shift. As agentic AI becomes table stakes, infrastructure gaps become strategic liabilities.
Organizations are evaluating whether their vendors can:
Vendors that can’t meet these requirements risk losing long-standing customer relationships. In a market where agentic AI adoption is accelerating, loyalty increasingly depends on architectural readiness rather than brand familiarity.
At Nylas, we have long believed that communications data represents one of the most complex infrastructure layers in modern software. Email and calendar systems are deeply embedded in operational workflows, yet they are often fragmented across providers and formats.
Clean, normalized communications data, reliable APIs, and compliance built into the foundation are not headline features. They are foundational requirements for enabling trustworthy automation at scale. Agentic AI systems that depend on email and calendar access can’t function effectively without a resilient communications data layer.
As agentic AI reshapes how software is built and deployed, the competitive landscape is shifting toward vendors that can support autonomy responsibly and reliably over time. The infrastructure layer is becoming the deciding factor.
To understand what teams are actually building and shipping today, read “The State of Agentic AI in 2026: What Teams Are Actually Shipping.”
For the complete dataset, including vendor-switching insights and broader adoption trends, explore the full 2026 State of Agentic AI report.
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