QX Labs vs Make: Visual Workflow Canvas vs AI Agents

The short answer: Make is one of the best visual workflow builders there is, a drag-and-drop canvas where you wire up modules, routers, filters, and iterators to map complex, deterministic automations exactly the way you want them. QX Labs is an AI agent and automation platform you build by conversation. You describe the work in plain English, and QX combines agentic judgement with deterministic steps, runs the same work across thousands of records, and grounds everything in your own knowledge. If you want fine-grained visual control over a process you can fully specify in advance, Make is excellent. If the work needs reading, deciding, and researching, and you'd rather brief it than draw it, QX is the better fit.

This is a bottom-of-funnel comparison, so we'll be direct about where QX wins. But Make is a genuinely great product with a passionate following, and we'll be accurate about what it's good at and when it's the better choice.

Key takeaways

• Two different building models. Make is a visual canvas: you assemble a "scenario" from modules and connectors and control the branching by hand. QX is conversational: you describe an agent, grid, or flow in plain English and refine it.
• Make's strength is visual, deterministic control. A large app catalogue (3,000+ apps), granular branching with routers, filters, iterators, and aggregators, and flexible data manipulation make it superb for mapping intricate, predictable workflows step by step.
• QX's strength is judgement, scale, and grounding. Agents that reason and act, Grids that run work across thousands of rows in parallel, Knowledge Vaults that return grounded, cited answers, and Flows that blend agentic and deterministic steps in one pipeline.
• Make has added AI agents, but the foundation is the canvas. Make AI Agents work alongside its deterministic logic; QX is agent-first, with deterministic steps where you need reliability rather than as the base layer.
• Pick by how you want to build and what the work needs. Choose Make for visual, fully deterministic scenario design by a hands-on builder. Choose QX when you want AI judgement, scale, speed-to-build, and one connected workspace.

What is Make, really?

Make (formerly Integromat, now part of Celonis) is a visual automation platform. You build automations called scenarios on a canvas: each step is a module (connected to an app or performing a function), and you draw the connections between them. Where Make shines is granular control over the flow: routers split a scenario into multiple paths, filters gate which data passes through, iterators break arrays into individual items, and aggregators bundle them back together. You can see the whole process laid out spatially and watch data move through it.

That visual model has real advantages. For a builder who wants to map a complex, deterministic process exactly, with explicit branching, error handling, and data transformations, Make gives you precise, hands-on control that's satisfying and powerful. Its app catalogue is large (3,000+ apps), and its data-manipulation tooling is flexible. Many teams choose Make over alternatives because it's both more capable than simple trigger-action tools and more cost-efficient at volume.

Make has also moved into AI. Make AI Agents can reason, choose what to do next, and trigger workflows across its integrations, with a reasoning panel that shows each decision and the ability to add manual approvals. These are genuine, useful additions. But the heart of Make is still the visual canvas. AI agents sit alongside the deterministic scenario logic rather than replacing it. The platform's center of gravity is "draw the workflow precisely," and the learning curve reflects that: modules, operations, data structures, and mapping take time to master.

What is QX Labs?

QX Labs is an AI agent and automation platform. The premise: your next hire is an AI agent. Where a chatbot only answers and rigid automation only follows rules, QX understands context, uses your real tools, and completes the work, combining the judgement of an LLM with the reliability of automation.

The defining difference in how you build is that QX is conversational, not visual-first. You don't assemble modules on a canvas; you describe what you want and QX plans it, asks clarifying questions, and builds the first version, which you then refine. It's built from a few primitives that compose:

• Agents: autonomous AI co-workers you brief in plain English. They read context, decide which of your connected tools to use, take action, and report back. They run on the model you choose (OpenAI, Anthropic, or Google Gemini), and they build up institutional memory, getting more useful the more you use them.
• Grids: a spreadsheet-on-steroids where every column can run an agent, an integration, or logic across hundreds or thousands of rows in parallel. This is how one person does research, enrichment, or scoring at a scale that would otherwise need a team.
• Flows: multi-step workflows that mix deterministic nodes (read a file, write a record, send an email, call an API) with agentic nodes (summarise, classify, decide, draft), triggered by an event, a schedule, or on demand, with guardrails and human-in-the-loop approvals.
• Knowledge Vaults: your internal docs, indexed and continuously synced, so agents answer grounded in your real data, with citations.
• 1,000+ integrations: so agents act with the same reach your team has, including a deep long tail of research and enrichment APIs many no-code tools don't reach.

Crucially, QX still does deterministic steps. That's what Flows are for. The difference is you're not limited to them, and you build by describing the outcome rather than wiring the canvas.

QX Labs vs Make: feature comparison

Capability	Make	QX Labs
Build model	Visual canvas: drag-and-drop modules, routers, filters, iterators	Conversational: describe agents, grids, and flows in plain English
AI judgement	Make AI Agents added alongside deterministic scenario logic	Agent-first: agents reason, decide, and act end to end natively
Visual branching & data control	A core strength: granular routers, filters, aggregators, mapping	Conditional gates and branches in Flows; less manual wiring
Run at scale across many rows	Iterators within a scenario, counted in operations	Grids run work across thousands of rows in parallel
Grounding in your data	Relies on connected apps	Knowledge Vaults: indexed, synced, cited answers
App integrations	3,000+ apps (broad catalogue)	1,000+ apps, with a deep research/enrichment long tail
Where you operate it	Web app canvas	Slack, Teams, WhatsApp, email, web app, API
Learning curve	Steeper: modules, operations, data structures, mapping	Gentler: if you can brief a colleague, you can brief an agent
Pricing shape	Per-operation (operations counted per module run)	Usage-based credits, workspace-wide, no per-seat charge

Make figures as publicly listed at time of writing; check current details on make.com and our pricing page.

Where QX Labs wins

You build by talking, not drawing. Mapping a sophisticated scenario on Make's canvas is powerful, but it takes real effort, and edits mean re-wiring. With QX you describe the work ("research every company on this list, score them against our ICP, and draft an intro email for the top 20") and it builds it. Speed-to-first-working-automation is measured in minutes, and refinements are another sentence rather than another module.

Judgement-heavy work. When the task is "read this and decide," not "if field A then route to path B," an agent that reasons beats a hand-drawn tree of filters and routers. A QX agent can read an inbound email, work out what it's actually asking, check your CRM and Knowledge Vault, and draft the right reply, handling messy variation that would balloon into a sprawling canvas in a purely deterministic tool.

Work at scale. Grids are built for volume: score every lead against your ideal-customer rubric, research every company in a market in one pass, or extract fields from hundreds of PDFs. Thousands of rows run together, consistently, with the same logic applied to each. On a per-operation model, that same volume is something you have to architect carefully and watch closely; on a Grid, parallel scale is the default.

Grounded, cited answers. With Knowledge Vaults, agents answer from your documents and policies and show their citations. Not generic web text, and not whatever happens to be mapped through a module.

Operate it where you already work. Delegate to an agent from Slack, Microsoft Teams, WhatsApp, or email. @mention it like a colleague and it replies, then takes the follow-up action. You don't have to open the builder to get work done.

Where Make may fit better

Be honest about this: if you want fine-grained, fully deterministic control and you enjoy designing the process visually, Make is often the better pick. When a workflow is complex but fully specifiable in advance, with precise branching, exact data transformations, and careful error handling, the canvas lets you see and control every step in a way a conversational builder doesn't replicate. Builders who think spatially and want to hold the whole flow in view often prefer it.

Make's app catalogue is larger (3,000+ apps versus QX's 1,000+), so if your single deciding factor is coverage of a specific app, check whether QX supports it on the integrations page first. If it does, you get judgement on top. Make's per-operation pricing is also very cost-efficient for high-volume, simple, deterministic scenarios, which is a real reason teams choose it over pricier rule-based tools.

If your needs are "draw a precise, predictable pipeline and run it cheaply at volume," Make is a strong, mature choice and you may not need an agent platform at all.

Which should you pick?

Pick Make if you want visual, fully deterministic scenario design by a hands-on builder. You value mapping intricate branching on a canvas, you can specify the whole process up front, and you want granular control over routers, filters, iterators, and data transformations, plus the largest app catalogue and cost-efficient per-operation pricing.

Pick QX Labs if you want AI judgement, scale, speed-to-build, and one connected workspace: work that needs reading and deciding rather than just executing fixed steps, the same task run across thousands of records in a Grid, answers grounded in your own knowledge with citations, and the ability to build by describing the outcome and operate it from the tools you already use.

The two aren't mutually exclusive. Some teams keep Make running for precise, stable, deterministic scenarios and bring in QX for the judgement-heavy, research-heavy, or high-scale work. The honest test: if you'd want a smart colleague to read, judge, or research to do the task, you want an agent, not a hand-drawn module. And if you'd rather brief the work than draw it, that's QX too.

See it for yourself

If you're weighing QX against Make for real work, the fastest way to decide is to watch an agent do a task you'd otherwise build module by module. Book a demo and we'll run one of your workflows live, or start free (every feature is on the free plan) and try it on your own data.

Explore the pieces: Agents, Grids, Flows, Knowledge, and the 1,000+ integrations.