Sykik vs. Make.com: Visual Scenarios or Proactive Agents?

Two tools. Two philosophies. Make.com hands you a visual canvas and says "draw your automation." Sykik hands your team agents that live inside Gmail, Docs, and Chat and says "let them figure out the work." This comparison is about that difference, because it decides who gets value out of an automation project — and how fast. On one side you get a superb canvas for the flows an engineer designs. On the other, you get agents that pick the work themselves, for the people who actually do it.

We'll be fair to our competitor first. It deserves credit, and a comparison that skips that part is marketing, not analysis. Then we'll show you where the model breaks, what that costs in practice, and what we built instead.

Full disclosure before we start: we're Sykik, and we built a different kind of tool. We also work with real companies every day, so our opinion comes from what we actually see — not from a slide deck. Keep that in mind as you read.

#Make.com's visual scenario builder is genuinely good

Let's start with what the platform does well, because it does a lot. Make.com is the no-code visual automation platform formerly known as Integromat, acquired by Celonis in October 2020 and rebranded Make (their own retrospective says so). Today its official positioning line reads: "Build and manage AI agents and automations in one visual landscape" (official site).

That landscape is the product's real strength. The scenario builder gives you genuine logic control — routers, error handlers, loops, filters — so an engineer can carve out branching workflows with real precision. You can see the whole flow on one screen: what triggers it, what branches, what happens when something fails. For people who think in diagrams, that's a comfortable, powerful place to work.

There's more. A large template library gets you started in minutes. Enterprise connectors for SAP, Workday, ServiceNow, and Coupa open serious systems at the Enterprise tier. And annual pricing comes with a transparent discount, which is rarer in this market than it should be.

If your team already knows how to design flows, if the process is stable, and if someone is paid to maintain the diagram — Make is a legitimate, mature tool. We're not going to pretend otherwise.

#The credit model punishes complex automations

Now the catch. Make prices itself per module operation. Every step in every scenario consumes a credit, and credits are the currency of the whole platform. The official list: Core at $16/month, Pro at $28/month, Teams at $51/month (about $38/month billed annually), and Enterprise at custom pricing.

Those prices look friendly. The picture changes when you build something real. Say your scenario has eight modules — that's eight credits on every single run, before any error retry or branch. Multiply that by an hourly schedule, and a "cheap" workflow quietly becomes a monthly subscription on top of a monthly subscription. Lots of teams tell us their real scenarios sit at twenty modules or more — which means every run costs twenty credits before a single branch fires.

Complex multi-step scenarios consume credits quickly. That's not an implementation bug; it's the business model. The more value your automation produces, the more it costs per run. And the free plan allows only two active scenarios, so you can't even prototype broadly before paying up.

Governance sits behind the same wall. Audit logs and similar controls live at the custom-priced Enterprise tier, which puts basic oversight out of reach for mid-sized teams that need it.

We see the pattern with customers who came from flow-builder tools: the diagram is beautiful, the invoice is not.

#Make's AI agents are new — and they don't live where you work

The bigger issue is what "AI agent" means here. Agent capability on Make is comparatively new and not native to the platform. There's no proactive AI that lives in your email or chat, and there's no native Gmail or Google Workspace agent that watches your inbox and acts without being prompted.

That's not a niche complaint. The market has moved: Gartner predicts 40% of enterprise applications will integrate task-specific AI agents by the end of 2026, up from less than 5% in 2025 (Gartner press release, 26 August 2025). And McKinsey estimates generative AI can automate work activities that absorb 60–70% of employees' time (McKinsey, "The economic potential of generative AI", June 2023).

Here's the thing about that 60–70%: it doesn't happen inside a scenario builder. It happens in the tools where work already lives — the inbox, the chat, the document. A flow you drew can't notice an urgent email at 7 p.m. and prepare the reply. It can't read this quarter's contract, spot a changed clause, and draft the redline. It only runs when someone designed it to run, in exactly the way they predicted. A workflow answers the question you wrote down; an agent answers the question that actually showed up.

Flows also rot. Business changes, fields change, APIs change, and somebody has to redraw the branches. The diagram that took a week to build takes a week a year later to repair. That maintenance cost never shows up on the price list.

#Sykik flips the model: agents that decide the work themselves

This is where we went a different way. Sykik is a Hybrid AI architecture that turns Google Workspace or Microsoft 365 into a conversational, agentic operating system. Our agents live inside the tools your team already uses: Gmail, Google Chat, Docs, Sheets, Drive — and the Microsoft equivalents. Not behind a diagram, not in a separate canvas.

And they're proactive, not just reactive. Agents are scheduled, event-triggered, and rule-based. They run scans, prepare drafts, and post briefings without being prompted. Nobody draws a flow for "the weekly supplier digest." The agent knows it's Friday, checks the inbox, and drafts the summary before anyone asks.

Reactive is easy to build; proactive is where the leverage is. If an agent only answers when summoned, it saves a few minutes per request. If it anticipates, it removes whole categories of work. That's the difference between a tool you open and a colleague you trust.

If you want the architecture in detail, here's how Sykik turns Google Workspace into an agentic operating system, and what Hybrid AI architecture actually means and why it matters.

The people who benefit aren't the person who drew the diagram. They're the people doing the work — the ones who never open the automation tool at all.

#Model freedom and EU data sovereignty, baked in

Your automation tool shouldn't decide which AI you're allowed to use. On Make, models are a feature bolted onto a workflow engine. For us, model freedom is the foundation.

Sykik is model-agnostic by design — we literally call it freedom of choice. Any provider works: OpenAI, Google, Anthropic, Meta, Mistral, your own fine-tuned models, or open-weights models via OpenRouter. BYO API keys are supported, and every task can be routed to the best or cheapest model for that specific job.

That routing is where the cost control lives, and it's a deliberate contrast to credits-per-step pricing. Instead of paying per module operation, you pay per model call — and you choose which model earns that call. Here's how cost optimization through intelligent model routing works, and why we think vendor lock-in damages your AI strategy.

EU data sovereignty follows the same logic. Sensitive workloads can be routed to EU-hosted models, and your data stays yours. For European enterprises, that's often the difference between "AI we can run" and "AI legal will approve." We wrote more about why data sovereignty in the EU matters for enterprise AI.

#From diagram to done: what we see with customers

Here is what we see with customers who switch. They don't come to us because they love diagrams. They come because the diagram was drawn, approved, and then nobody maintained it — or because the work they wanted automated was never a predictable flow in the first place.

Our founders, Hermann Wagner and Niklas Retzl, built Sykik on exactly that observation. Real workflows are messy, judgment-heavy, and full of exceptions. A router with twelve branches stops being maintainable the moment the business changes. An agent with instructions and context adapts instead.

Sykik isn't a concept. It's a live MVP running with real design partners — Croowy GmbH and Moregola Fine Jewelry are using it today, not staring at a roadmap. And it's production-ready in about two weeks, including workflows, integrations, and model configuration. That timeline matters, because the value of automation doesn't come from the tool; it comes from the first week of real use. Most switchovers we've watched look less like a migration and more like a sigh of relief: the digest appears, the draft is already there, and nobody had to redraw anything.

So which tool is for you? If you have a stable, high-volume process and an engineer whose job is maintaining it, Make's canvas is a fine choice. If you want AI that lives where your team works, decides the work itself, and scales without a diagram — that's the thing we built, and we'd love to show you.

#FAQ

Is Make the same as Integromat? Yes. Make is the no-code visual automation platform formerly known as Integromat, acquired by Celonis in October 2020 and rebranded to Make.

Does Make have AI agents? It has added AI agent features, but they are comparatively new and not native to the platform — there's no proactive AI that lives in your email or chat, and no native Gmail or Workspace agent.

How does Make's pricing work? It's credit-based: every module operation costs a credit, so complex multi-step scenarios consume credits quickly. Core is $16/month, Pro $28/month, Teams $51/month (about $38/month billed annually), Enterprise custom.

What makes Sykik different from Make? Sykik is a Hybrid AI architecture with proactive agents that live inside Google Workspace or Microsoft 365. Agents are scheduled and event-triggered — they run scans, prepare drafts, and post briefings without being prompted.

Can Sykik use models from different providers? Yes. Sykik is model-agnostic: OpenAI, Google, Anthropic, Meta, Mistral, your own fine-tuned models, or open-weights models via OpenRouter, with BYO API keys and routing to the best or cheapest model per task.