Tray.ai was named a Visionary in the 2026 Gartner Magic Quadrant for iPaaS.
We’re proud of that.
But the more interesting story isn’t where vendors sit on the quadrant. It’s what the quadrant reveals about the state of the market.
In many ways, this may be the last Magic Quadrant of the integration era.
Not the last report Gartner will publish. But the last one built around the assumptions that defined the integration category for the past twenty years.
Because those assumptions are breaking.
AI orchestration is now core enterprise infrastructure. And iPaaS is either evolving to power it… or becoming irrelevant.
The integration model that powered SaaS
For two decades, integration platforms did one thing extremely well: move data between systems. Structured in, structured out. Predictable APIs and deterministic workflows. That model powered the SaaS revolution.
It does not power AI-first enterprises.
Today, teams want AI embedded everywhere. If 2025 was the year of AI pilots and experimentation, 2026 is the year of AI operationalization. That means:
- Working faster by infusing AI into workflows
- Eliminating busy work with agents that can do things like resolve tickets, score calls, and draft briefings
- Orchestrating models, tools, and systems through MCP so those isolated agents become coordinated systems
- Re-architecting data pipelines to support both structured systems and AI-native workloads
These are no longer theoretical ideas. The companies that plan to compete over the next decade are already building them. And they keep running into the same constraint: the foundation of their stack wasn’t designed for this world.
Building the future on yesterday’s architecture
IT teams are expected to drive AI transformation across the business. Many are trying to do it with platforms selected before AI reshaped the operating model.
Those platforms were designed to connect apps through deterministic workflows. They were not built for autonomous agents, MCP governance, or unstructured AI workloads at scale.
What once created efficiency now creates handoffs between agent development, integration, governance, and data preparation. Every handoff slows delivery. Every boundary introduces risk. AI is demanding velocity but fragmented systems are throttling it.
Orchestrating AI across the business using SaaS-era integration platforms is like building a race car from boat parts. The architecture will fight you.
And most integration platforms are still built on that foundation.
Moving fast means rethinking the foundation
Most legacy iPaaS platforms were architected as workflow engines with connectors attached. They assume predictable APIs, structured payloads, and deterministic execution.
AI orchestration requires the opposite: a system where agents, data, governance, and interoperability protocols like MCP are native citizens, not add-ons.
In the SaaS era, change cycles were measured in months. In the AI era, agents evolve weekly, models evolve monthly, and architectures evolve constantly.
Enterprises cannot afford platforms that require major effort just to adapt to change. Heavy systems slow experimentation, and slow experimentation kills iteration. AI initiatives stall before they reach production.
This is why enterprises are consolidating legacy tooling as they move toward AI-first operating models. The integration layer must now move at the speed of AI.
From integration to orchestration
Organizations cannot afford a dozen disconnected agents running across separate platforms with no centralized control. They cannot afford MCP services deployed without visibility. They cannot afford ad hoc data pipelines feeding critical decisions.
They need a unified architecture.
One layer that brings together application integration, data integration, automation, MCP governance, agent development, identity, observability, and lifecycle management.
One system. That is where the market is heading.
AI orchestration cannot run on patched platforms
The pace of change around agents, MCP, interoperability, and AI data pipelines is accelerating. Enterprises do not have the luxury of waiting for legacy platforms to catch up through incremental updates.
The market is moving toward platforms where agents, MCP services, integration, and governance operate together by design, not as stitched extensions. That’s what Tray is.
We did not bolt AI onto an aging integration engine. We built an AI orchestration platform where agents, MCP services, integration, and governance operate together by design.
Agent Gateway centralizes MCP control. Agent Hub accelerates composable agent development. Merlin Agent Builder closes the gap between prototype and production. Data integration supports both structured systems and unstructured AI workloads in the same foundation.
Teams can innovate without fragmentation.
The proof is in production
At Apollo, Tray underpins AI-driven IT support that reduced case resolution time from fifteen minutes to one, delivering 24/7 assistance while freeing IT to focus on higher-value work.
At Zuora, Tray orchestrates AI-infused customer briefing processes end to end, replacing manual coordination with a fully automated, always-on system that connects knowledge with action.
At Life360, Tray supports multi-agent orchestration across the business, powering dozens of AI-infused processes in under a year and establishing a single orchestration layer rather than isolated pilots.
This is what orchestration at scale looks like.
The foundation has changed
The difference between legacy integration tools and platforms built for the age of AI is not a feature checklist. It is architectural cohesion.
Can your platform unify agents, data pipelines, MCP governance, identity, observability, and operational control in one system?
Or does it stitch them together across tools that were never designed to operate as one?
In the integration era, stitching was enough. In the AI era, cohesion is infrastructure.
This is the shift the market is now confronting. The Magic Quadrant may still be labeled iPaaS. But the category it describes is evolving into something else entirely.
AI orchestration is the next operating model of the enterprise. And once you see it running on unified architecture, the old model doesn’t feel incomplete.
It feels obsolete.
