Welcome to Intelligent Automation

The iPaaS sector is a mature, fast-growing category in enterprise software, roughly at $12 billion today and growing 17% annually. And yet, despite 20 years of growth, the category has never fully delivered on its original promise.

The first generation of platforms solved the problems of their era. But despite the low-code premise, they were built for a deeply technical audience—requiring specialized skills to configure, manage, and troubleshoot.

The second generation, including Celigo, came to the fore roughly ten years ago with a fundamentally different philosophy: lower the bar. An intuitive user interface made it possible for both highly technical users and, for the right use cases, business technologists to build workflows. Celigo went further—introducing AI-based error management that made it easier for anyone to not just build workflows, but keep them running. This was a meaningful step forward.

Broader Reach. Same Intelligence.

To its credit, the category didn’t stand still. What started as simple app-to-app integration gradually expanded: data integration to feed warehouses and lakes, B2B integration to connect supply chains, basic human-in-the-loop capabilities for approvals and exceptions, and API management to design and govern your own endpoints. The category slowly shifted from “iPaaS” to “automation platform”—and for good reason. The scope of what these platforms could do genuinely grew.

Despite all this progress—more integration patterns, new automation categories, broader reach—these platforms struggle to tell you why something broke, what would happen if you changed a connection, or how your integrations actually work as a system. The platforms got wider. They didn’t get smarter. And the gap between what they can do and what they can understand has only widened.

The Spectrum: From Predictable to Autonomous

Getting smarter starts with what the platform can actually do. And every automation platform now needs to be agentic.

The ability to build AI-infused workflows and fully autonomous agents isn’t a future roadmap item—it’s a present-day requirement. Imagine forecasting stockout risk and automatically triggering replenishment POs before you run out. Intelligently onboarding new employees across HR, IT, and payroll systems without someone manually shepherding every step. Deflecting support tickets by understanding intent, pulling context from multiple systems, and resolving issues without human intervention. These aren’t science fiction. They’re the workflows enterprises need to build right now.

But here’s the inconvenient truth most platforms won’t say out loud: the future isn’t all-AI, all-the-time.

Moving sales orders from your webstore to order management? That demands precise, deterministic rules—the same logic, every time, guaranteed. Payroll processing? You don’t want an AI “deciding” how to handle your tax calculations. Predictable workflows aren’t legacy. They’re essential. And they’re not going anywhere.

The right answer isn’t picking a side. It’s the full spectrum—fully deterministic for processes that demand precision, fully autonomous for workflows that demand adaptability, and everything in between. The right tool for the right job. And a platform that lets you choose without forcing you onto a different product for each.

This is what we’ve built at Celigo. With Agent Builder, you can create autonomous agents in clicks, not code. Every workflow and connection can be exposed as an enteprise MCP server, making the platform a tool that other AI products can use natively. Predictable where you need predictable. Autonomous where you need autonomous. One platform, one modern interface. That’s one aspect of intelligent automation.

Describe It. Done. Meet Ora.

But how intelligent does a platform actually need to be today? What’s the bar?

Most of what the platform industry calls “AI” today is still underwhelming: chatbots or assistants bolted onto aging architectures. Many lack a coherent strategy for what intelligence even means in automation; others have the vision but are trapped by platforms that can’t support it. The result is the same—surface-level AI on top of yesterday’s platform. But the industry is starting to wake up to the need for operationalizing AI. In just the last few weeks, we’ve seen platforms rush to add natural language interfaces and adopt open protocols like MCP so that external LLMs—Claude, GPT, and others—can interact with their APIs. These are early steps. But there’s a fundamental difference between an LLM that calls your platform’s APIs from the outside, reconstructing context with every request, and an AI that lives inside the platform, sees what you see, and works where you work.

At Celigo, we’ve always been obsessive about usability and lowering the bar for who can build and manage automations. Now, we’re taking intelligence somewhere no automation platform has gone. Meet Ora—a copilot that doesn’t just assist you. It replaces the UI entirely.

It is a multi-agent system—a team of specialized AI agents embedded directly in the platform that collectively understand every corner of your account. You talk to it in plain English. It builds, configures, troubleshoots, and manages your integrations.

Tell it what you want. It figures out the how.

Say: “Sync new wholesale orders to Microsoft Dynamics F&O as sales orders, map customer email to the entity, line items to order lines.” Ora analyzes your request, produces a structured design, and—when you say “build it”—scaffolds the entire flow. Exports, imports, field mappings, hooks, filters. The specialist agents handle their domains: the SQL agent writes queries, the JavaScript agent writes transformation logic, the mapping agent auto-maps between schemas.

Need changes? “Add a filter for orders over $100.” It modifies the existing flow in place. No starting from scratch.

Not sure yet? Use the “walk me through it” mode. Ora explains its plan without touching a thing. Every change is staged as a preview—visible in the same interface where you’d normally manage your integrations. You review each modification in context, accept or reject it, and nothing goes live until you approve. This isn’t an AI returning an API response and asking you to trust that it worked. This is an AI showing you exactly what it’s about to do, in the place where you’d verify it yourself.

It doesn’t just build. It understands.

This is the gap between a chatbot and real intelligence. Most platforms now have some kind of AI assistant—but they operate on the surface, responding to what you ask without understanding what you have. Ora is different. It has access to a knowledge graph that maps every integration, every flow, every connection, every script, every user, and every relationship between them. It doesn’t just know what you’re asking—it knows why it matters, what it might affect, and what’s worked before.

When Ora connects to your account, it overlays recent usage data on that graph, so it knows which workflows are mission-critical and which are barely running. Within minutes, every agent in the system understands your entire operation before you ask a single question.

Ask “What would happen if I deleted the Workday Prod connection?” and Ora traverses the dependency graph, finds every resource that depends on it, annotates each with traffic volume, and gives you a risk assessment.

Ask “Something broke yesterday—what changed?” and Ora cross-references the audit log with the dependency graph to show you that a specific connection edit at 3 PM caused a specific flow to start failing at 3:15 PM.

Ask “What went wrong?” on any failed step, and specialized error analyzers route based on the error source—application failures, connection timeouts, script exceptions, mapping issues—each fetching the exact context needed for a root-cause analysis with actionable fixes.

Then take action at scale, conversationally: “Retry all Shopify order sync errors from the last 24 hours with a 429 status code.” Done. “Assign all Salesforce connection errors to Sarah.” Ora walks the integration hierarchy, finds every affected step, and assigns them. Multiple agent handoffs, zero manual navigation—in a single conversation turn.

That’s not a chatbot. That’s operational intelligence.

And it meets users where they are. The vast majority of people managing integrations today aren’t working in developer tools or AI-native IDEs. They’re in the platform. They need to see the change, understand it, and approve it—in the environment where they’d catch a problem on their own. For users who want to bring their own AI and work through APIs and open protocols, we support that too. But for the broadest audience, the copilot needs to be in the room, not calling in from outside.

The Standard Is Set

The old model—complex UX, workflows built manually, errors you diagnose by reading logs, inability to easily rectify them—is the past. Not because we say so, but because the technology now exists to replace it.

And there’s no reason to cobble together multiple tools and platforms when a single universal platform can handle the full spectrum—from deterministic workflows to autonomous agents, across every integration pattern—natively.

An intelligent automation platform isn’t one that sprinkles AI on top. It’s one where a single platform handles everything from predictable rules-based workflows to fully autonomous agents. Where an embedded copilot doesn’t just assist but actually understands your entire environment. And where anyone—IT specialist, business technologist, or someone who’s never touched an API—can build, manage, and troubleshoot automations by having a conversation.

That’s what we’ve built. And we believe it should be the baseline for the entire category.

Whether you choose Celigo or not: demand this. If your platform can’t do roughly what we’ve described here, you’re paying for yesterday’s technology.

Choose wisely.