iPaaS migration has always been a time and cost problem. It just got 95% faster.

The case for staying on your current integration platform has always been practical. Your workflows are built. Your teams know the tool. Migrating means rebuilding logic that took months to write, retraining people who finally know how things work, and accepting the risk of moving live automation onto infrastructure that nobody in your organization has battle-tested.

That calculus made sense when the cost of migration was high and the gap between what you had and what you needed was manageable.

Both of those things have changed.

Some platforms are genuinely legacy. Assembled through acquisitions, running infrastructure that predates modern API design, carrying licensing models built for a world where integrations were IT projects rather than product capability. They power critical business processes — often reliably — but the overhead has been quietly growing. New connectors require custom development. Cloud-native data sources need adapters. The governance model was designed for on-premise deployments that look nothing like a modern multi-cloud environment. The gap between what these platforms were built to do and what the AI era requires is structural, not cosmetic.

Many enterprises aren’t running one integration platform — they’re running three or four. The iPaaS that came with the ERP rollout. The lightweight tool a team adopted to move fast. The data pipeline layer that predates the integration team. The workflow automation tool someone added during the pandemic. Each decision made sense at the time. Together, they mean fragmented visibility, duplicated tooling costs, and no single place to govern what’s connected to what.

A third category is platforms that are recent and well-regarded, but weren’t built for AI orchestration. n8n was designed for individual developers and small technical teams — fast to adopt, often too fast, creating shadow IT that enterprise IT teams then inherit. Critical security vulnerabilities in late 2025, including flaws rated CVSS 10.0 and 9.9, put it on CISA’s Known Exploited Vulnerabilities catalog. Workato is more polished and broadly adopted, but its recipe-based model constrains complex custom logic, and renewal pricing has become a consistent friction point at scale. Both are capable tools for the problems they were designed to solve. AI orchestration wasn’t one of them.

Across all three, the constraint is the same. AI agents need to read context from multiple systems, take actions, handle failures, and do all of it within a governance model that IT can audit. Retrofitting that capability onto platforms built around different jobs — legacy ETL pipelines, fragmented point tools, or developer-first workflow runners — produces something that works in a demo and struggles in production.

For most engineering leaders, the decision to stay wasn’t irrational. It was a rational response to a real cost.

Your existing workflows — in n8n, Workato, or wherever you’ve built — represent months or years of accumulated logic. Field mappings, error-handling branches, retry logic, conditional routing. All of it encoded in a proprietary format, documented (if you were lucky) across a mix of internal wikis and institutional memory.

Migrating that logic meant reading it manually, interpreting it, and rebuilding it by hand in a new environment. A realistic migration of several hundred workflows was a 6-to-12-month project — with risk on both sides of the cutover.

That’s why most organizations deferred. The bar was high enough that staying on a suboptimal platform was the rational choice.

The barrier to migration was always time and effort. Converting existing workflow logic from one format to another is not intellectually complex — it’s time-consuming and error-prone at scale because humans have been doing it manually.

The formats these platforms use to store workflow definitions — n8n’s JSON, Workato’s recipe schema — are structured and machine-readable. An AI coding assistant can parse them the same way a human engineer would, without the fatigue and error rate.

Tray Headless makes the full Tray platform accessible natively from inside AI coding assistants — Claude Code, Cursor, Codex, and others. This means an AI agent can read your existing workflow definitions directly and reconstruct the logic in Tray without a human doing that translation work. The conversion that used to take an engineer two days per workflow batch now happens in minutes.

What comes out is a working Tray workflow, with auto-generated documentation, that an ops team can inspect, modify, and maintain without writing code. Every converted workflow still gets reviewed before it goes live — the audit trail and documentation the AI generates is what makes that review fast, not what skips it.

One team migrating from n8n used Claude Code and Tray Headless to test the approach on a representative sample. Seven workflows — ones that would have taken around two days to rebuild manually — were completed in 30 minutes. The AI handled the conversion and generated a complete audit trail automatically. Stakeholders had working workflows and enough documentation to review and sign off in under an hour.

That’s a 95% reduction in migration time on a real sample.

A Tray partner working with a large enterprise on a Workato migration used Tray Headless as the core of their delivery approach, building the converted workflows without the customer’s team needing to learn a new visual builder or wait on specialists for each conversion.

Neither team is exceptional. They’re doing what any team can do once the approach is understood: using AI tooling to remove the labor from a problem that used to be almost entirely manual.

Not every workflow converts cleanly in one pass. Workflows with undocumented edge cases or heavily customized connector logic need closer review, and some will need to be rebuilt rather than converted. But the distribution matters: most of what’s in a legacy automation estate is structured, documented, and a good candidate for AI-assisted conversion. The complex tail is real — it’s also a much smaller percentage of the work than organizations assume when they’re sizing a migration project.

The destination matters. Moving off a platform that wasn’t built for the AI era only makes sense if what you’re moving to was.

Tray is built around a different premise: AI agents, governed MCP, integration, and automation — all on one platform.

Agent Builder lets technical and non-technical teams build, deploy, and manage agents that reason and take actions across your systems — connected through the same 700+ enterprise connectors that power your integrations. Agent Gateway gives IT a governed MCP layer: a single registry for every MCP server in your environment, with role-based access control, rate limiting, audit logs, and full observability across every agent-to-system connection. Integration and automation run on the same platform, the same connector library, under the same governance model.

No separate platform for agents. No separate vendor for MCP governance. The security and compliance posture — SOC 2, HIPAA, GDPR, 99.99%+ uptime — is part of the platform, not a tier you negotiate into.

The evidence from teams that have already moved is consistent. Yext migrated 100+ services in 3 months and cut integration costs by more than 60% compared to their previous platform. Crowdstrike built integrations 10× faster. HackerOne saw 4× developer velocity. The same holds for fragmented stacks: eHealth consolidated from three separate platforms — Tibco Scribe, Informatica, and MuleSoft — and cut customer onboarding from days to 3 hours.

The question isn’t whether migration is worth it. It’s whether the cost of waiting — the widening gap between what your integration infrastructure can do and what your AI agenda requires — is worth it.

For most organizations still on a platform that wasn’t built for this moment, it isn’t.

If your organization is running a meaningful automation estate on a platform that’s no longer the right fit, the migration barrier has changed materially. The first step is understanding your scope: what’s in your environment, how much of it is a migration candidate, and what a phased transition looks like.