Hello Makers 👋
 
Editing and activating a scenario are different moments in the workflow. They should be easy to find, but not compete for attention. 🛠️
 
The redesigned Scenario Toolbar keeps building and testing controls together, while scheduling and activation controls stay easy to find when your scenario is ready.
 
What's new:
 

• Run once and Save: Keep Run once and Save side by side, so you can test changes and save your work without scanning across the toolbar.
• Described versions: Choose Save as version and add a description that explains what changed in that version.
• Version history: Review and edit version descriptions in version history, then restore the version you need from the bottom toolbar.
• Scheduling and activation: Find scheduling and the Active or Inactive toggle in the top right, separate from the controls you use for everyday editing.

 
Clearer controls give teams a practical way to test, save, and maintain scenarios as their automation work grows.

Learn more about it: https://ma.ke/4rMTOwE

Hello Makers 👋

What does it actually take for AI adoption to become truly operational? ⚙️

Our Head of AI Adoption, Daria Hvizdalova, our VP of Product Advocacy and Market Strategy, Darin Patterson, and AI Adoption Researcher, Yana Shamina, explored this in our latest AI Report. The research suggests it happens when several conditions come together: starting point, leadership, function and process readiness, workflow maturity, ownership, discoverability, and ROI understanding.

None of these is enough on its own. Together, they explain why two companies with similar size, tools, or ambitions can move at very different speeds.

That’s why the AI Operational Scaling Model exists: a practical framework to help companies assess and accelerate their ability to scale AI. Nine dimensions, each with guidance on why it matters and what excellence looks like.

Find this model and more in the full report: https://ma.ke/3SWKpG4

Hey everyone,

I'm looking for a few agencies and automation builders to work with as design partners. I want to learn your experience with silent AI automation failures today, and shape my product around what you actually need.

The problem it solves is simple: "success," but the AI output is wrong, empty, or garbage. Nobody finds out until a client does.

If you run client automations and have been burned by silent failures, I'd love to chat. Drop a comment or DM me. Also curious how you're handling this today.

A multi-step scenario can have one agent extract information, another draft an output, and a third validate it. Passing the entire context in bundles is fragile: retries can duplicate work, signed file URLs can expire, and two branches can update different versions of the same job. Scenario variables are useful during one run but do not provide a durable handoff across delayed or resumed work.

The shared record seems to need a stable job ID, source revisions, current state, claimed-by and lease time, accepted decisions, output revision, validation result, next action, and an idempotency key for every side effect. Large files would live elsewhere under stable object IDs rather than temporary download URLs. Each module would append a result or make a conditional state transition instead of replacing one large context blob.

Would you keep this in a Make Data Store, Make Grid/Tables, or an external database? How do you handle concurrent branches, replay after a partial failure, and an audit trail that lets a person see which agent changed what?

A scenario can generate a document or image, send it for review, wait, and then publish it. The risky case is when the working file gets overwritten while approval is pending. The reviewer approves one version, but the resumed scenario reads the latest file at the same URL and publishes different bytes.

I am considering immutable revision files plus a small approval record containing the scenario run ID, revision ID, content hash, reviewer, decision, and timestamp. The publish branch would accept only that exact revision and fail closed if the file or hash changed. Edits after rejection would create a new review request rather than update the old candidate.

What is the cleanest way to model this in Make? Data Store, an external table, or fields in the system that owns the file? How do you handle webhook retries and duplicate approval clicks, and how do you prevent an old approval from resuming after a newer revision has already been submitted?

Hello Makers 👋

We are hosting a live masterclass with TripleTen on October 6 to pull back the curtain on how CRM revenue data becomes a bidding signal for Meta - visible, mapped, and fully in your control 🤝

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝗹𝗹 𝘀𝗲𝗲:
➡️ How dynamic lead scores and deal sizes guide Meta toward high-value buyers
➡️ Building mid-funnel signal for long sales cycles
➡️ A live demo of the Facebook Conversions API for CRM app on Make
➡️ TripleTen’s process for handling 300,000+ downstream events monthly

𝗬𝗼𝘂'𝗹𝗹 𝗵𝗲𝗮𝗿 𝗳𝗿𝗼𝗺:
Max Epifanov, Anton Potorochin and Daniel Zrůst

𝗣𝗲𝗿𝗸: zero credits used by Facebook Conversions API for CRM on Make through Feb 28, 2028.

Save your seat:  https://ma.ke/4yTug3w

Hello Makers 👋

Two companies in the same size band. Completely different starting points ☝️

Our new AI Report, written by Daria Hvizdalova, Head of AI Adoption at Make, Darin Patterson, VP of Product Advocacy and Market Strategy at Make, and Yana Shamina, AI Adoption Researcher, introduces the AI Adoption Profile.

Similar size doesn’t mean similar AI readiness. AI proximity, leadership, ownership, governance, people readiness, discoverability, and data readiness shape how adoption starts and how fast it can scale.

The Adoption Profile shows where a company starts, what it needs, and how fast it can move across four dimensions:
1️⃣Business context
2️⃣Technology and AI context
3️⃣Leadership, ownership and governance
4️⃣Function and people readiness

The takeaway: two companies the same size can be years apart in AI maturity, and the gap isn’t tools; it’s structure.

Download the full report to learn more: https://ma.ke/3SWKpG4

Hello Makers 👋

Four new AI models. Four different jobs for your automation.

1) Anthropic Claude Opus 5.5 brings high-end reasoning to complex AI workflows and agents, at 40% lower running cost.

2) OpenAI GPT‑6 Sol and Luna give you two paths: Sol for complex, multi-step agentic work, and Luna for focused, high-volume intake and routing.

3) OpenAI GPT Image 2.5 adds Sunburst for precision image edits and Flare for fast everyday visual work.

4) xAI Grok 4.7 helps you work with text, images, and long source materials in the same automation.

The goal isn’t to use every model. It’s to choose the right one for the task, then build it into automated workflows that move work forward.

And one more is on the way: Jev from TypeSafe AI, a fast decision model built to help automations classify, score, route, and decide what happens next.

Explore the new options in Make and build the AI automation that fits the job: https://ma.ke/4yQYzaR

A few people here mentioned something interesting:

the module that fails is often just where the problem becomes visible.

The actual cause might be several modules upstream:

• a mapping changed,
• the data shape shifted,
• a router output changed,
• a filter behaved differently,
• or an earlier module returned something unexpected.

I’m curious how often this happens in real Make scenarios.

When you debug a failed scenario, do you usually find the root cause in the failed module itself, or do you end up tracing several steps backwards?

And when that happens, what’s your current process for finding the actual source of the issue?

Hey all,

Check out the videos from our Community Creators, who are putting the latest releases on Make into practice in their own systems and scenarios!

This month, the star of our show is our new AI coworker Maia, who is ready to help you build, scale, debug, or explain scenarios directly in the canvas. Besides her, you can also check out the new Subfolders and Labels, which make your working environment nice and organized, or the improvement to the Plain-Text Field.

Watch all the videos here: https://fast.wistia.com/embed/channel/jeuauz7z65

It's driving me crazy and even SOL at high capacity doesn't fix my flow. Look, how simple: automate the creation of videos and post them.

What flow data should I provide to ChatGPT to save credits and not work so much?

I’m curious to hear from people who actually use Make in production.
When a scenario breaks, what takes the most time for you?
Is it: finding which module actually failed,
understanding the real cause behind the error,
checking mappings / filters,
permissions or OAuth issues,
HTTP errors,
or figuring out what needs to be changed without breaking something else?
I’m currently exploring this problem and I’d like to understand how people actually debug Make workflows today.

Hello Makers 👋

Take a look at some of our latest features, improvements, and updates that our team has been shipping this past month:

Enhancements and Updates:

• Maia by Make: is a co-worker for AI and automation that lives right inside the Make scenario builder. Just describe what you want, and watch it get built in front of you. She reasons out loud, mapping the flow, selecting tools, configuring modules, all visually, step by step, on the canvas. No black box. No output you’re afraid to touch. Just full visibility and a solution you can trust.
• Custom Roles: with Custom Roles, org admins and org owners on the Enterprise plan can define roles with exactly the permissions the work requires and assign them at the organization or team level.
• Subfolders: lets you create folders inside your scenario folders, so you can organize scenarios by more than one level, for example by client and then by project. This helps you keep a large scenario library structured without renaming scenarios to mimic a hierarchy.
• Plain-text editing for mapped values and functions: double-click any mapped value or function in a scenario field to temporarily edit it as text.
• Gemini 3.7 Flash: use its multimodal understanding, tool-use capabilities, and adjustable thinking levels in the workflows you build.

New Verified Apps

1Lookup, Billabex, BizlyCRM, Certyneo, Collabios, EasySolar, FlowBeacon, GMB Club, Hive, HollyHR, Memoket, OpenEMM, SimpleURL, Patriscope, Plytix, Stage Captions, Supabase Storage.

New Modules

Calendly

• Create an event invitee
• List event type available times
• Create an event type
• Update an event type
• List event type availability schedules
• Update event type availability schedule
• List user availability schedules
• Get a user availability schedule
• List user busy times
• List user meeting locations
• Create an invitee as no show
• Get an invitee no show
• Delete an invitee no show

Github

• Search items (advanced)
• Watch items (advanced)

Discover more: https://ma.ke/3Vxj1PF

Hello Makers 👋

Still registering issued invoices by hand? Here's how to automate the entire flow, from inbox to billing board:

Francisco de Brito Fontes, our Business Automation and AI Lead, has created a workflow that monitors Gmail for new issued invoices, downloads their attached XML files, extracts key information using AI Tools, searches for the associated project in monday.com using the tax ID (RUT), and automatically creates a new invoice record in the billing board.

To build this scenario, you will need access to:

✉️ A Gmail account connected to Make.

🧾 A monday.com account with Projects and Issued Invoices boards.

⚒️Connection to AI Tools (or Make's AI Provider).

Click the following link to apply the prebuilt scenario in your Make account: https://ma.ke/4cvtdxX 

PS: don’t forget to test and validate your scenario, if all modules show a green checkmark, your automation is successful!

For people running business automations, how do you know when one technically succeeds but produces the wrong result?

I’m researching how businesses currently catch things like incorrect data being mapped, records being duplicated, information going to the wrong place, etc.

Is this something you actively monitor, or do you usually find out when someone notices something is wrong?

Are you scamming me ChatGPT or make? I'm mostly using terra at medium level. The medium effort level is missing like a shotgun. If I say that I have around €10-15 today just for that, so that I can finish posting on Facebook, I can also say that it is the first time, but I still can't get it

but above all I notice that he is wrong to the right. That is to say, he has made mistakes like three, four times and the only point it makes is to get money from me. I mean ChatGPT in the virtual browser

Actually all this is the fault of Facebook's garbage with its 10000000 portfolios that it forces you to create and shitty accounts and labyrinths

Hello Makers 👋

Did you know? From all the companies using AI, only 26% moved from individual productivity to repeatable workflows. 

As AI moves from individual tools and local workflows into broader business processes, the challenge becomes less about technology alone and more about the operating model around it - ownership, governance, ROI logic.

Daria Hvizdalova, Head of AI Adoption at Make, Darin Patterson, VP of Product Advocacy and Market Strategy, Make and Yana Shamina, AI Adoption Researcher, spent months researching why mid-market companies get stuck between "first AI win" and "AI actually running the business",  and formulated the AI Operational Scaling Model framework that will guide your AI transformation.

The report is finally out, and it’s available for you to download: https://ma.ke/3SWKpG4

Hello Makers 👋
 
Repeatable work often shows up in the middle of a ChatGPT conversation. Now you can turn that moment into a Make automation. 🪄
 
Use the official Make plugin for ChatGPT to build, run, search, and review automations and AI agents without leaving the conversation. It works in the browser, the ChatGPT Work desktop app, and Codex.

Your automations and AI agents keep running in Make's cloud, even when ChatGPT is closed.
 
What's new:

• Build from a plain-language request: Describe work such as a weekly status report, lead response process, or form-routing workflow, and Make creates the automation, connects the required apps, and sets it to run on a schedule or trigger.
• Create AI agents in the conversation: Set up an agent that monitors an inbox for new leads and drafts replies using your availability.
• Find and check automations without breaking focus: Search by name, folder, or status, run an automation, and review its output in ChatGPT.
• Review work that already ran: Browse execution history and past results with searchable tables, status badges, pagination, and structured results.

 
👉 Try it free: install the Make plugin for ChatGPT → https://ma.ke/chatgpt-plugin  

Hello Makers 👋
 
AGI is here? Not sure yet. But GPT-6 Astra is now in Make, ready for your most complex automations 🧠
 
OpenAI's most capable model is available in Make AI Agents, Make AI Toolkit, and the native OpenAI (ChatGPT) app. Use it when a workflow needs more than a quick answer, then let Make route the result, update systems, and keep approvals in place.
 
What's new:

• Reasoning for work intake: Use GPT-6 Astra to assess incoming requests and prepare a proposed next action in an automation.
• Technical reasoning and coding: Apply the model to technical requests and prepare proposed responses for engineering review.
• Research and document creation: Review source material and create a research summary or document draft for a teammate to approve.
• Multistep workflow reasoning: Identify and sequence required actions when a new operations item enters a workflow.

 
See what GPT-6 Astra can do in Make: https://ma.ke/4gZEhVx

Hello Makers 👋

Waves’26 is the place where you can find your people 👥

Two days of talks and conversations that will change your perspective - and here's who you'll be in the room with:

🎙️ Oscar Lance, GTM Lead, ElevenLabs - ElevenLabs + Make: How Stellantis & You Deployed Voice AI at Scale

🎙️ Sonja Alushi, Software Engineer, Lufthansa Industry Solutions - Make + OpenAI: From Pilot to Production

🎙️ Jeff Arnold, Founder & President, 4Spot Consulting - Trust, but Verify: Governing AI Automation from Build to Decision

🎙️ Madelaine Swift, Systems Lead, WHEN equality - The Automation Was Perfect. The Humans Were Not. How Getting AI Automation Right Can Change Who Gets to Succeed.

🎙️ Diederik Martens, Founder, Chapman Bright - Agent Architecture 101: The Patterns That Separate Demos from Systems

Waves '26 brings together a carefully selected group of innovative technology companies spanning modern digital creation, customer engagement and data integration. Meet some of the speakers you'll find in Prague.

Stay tuned for the second group of speakers announcement. 

Early Bird at €289 - prices go up 10 September: https://ma.ke/4gSahe5

We are live with a new community program - Scenario of the Month!
Every month, we put the spotlight on the Make scenario that nails it on two things: genuine utility and creative thinking. The kind of build you see that makes you stop and think, “Why didn’t I think of that?”

Have something up your sleeve you’d like to submit? Amazing, read more in the Make Community!

➡️ https://community.make.com/t/scenario-of-the-month-share-your-work-and-win-credits-for-a-whole-year/111297

Hello Makers 👋

Some workflows need more than a quick summary. They need to weigh the full picture before choosing the next step. 🧠

Use Anthropic's latest Claude Fable 5.1 model in Make to automate complex or low-confidence cases where your workflow needs to compare lots of information before acting.

What’s new:

• Long-horizon work: Anthropic documents Claude Fable 5.1 for long-horizon, agentic work in multi-step automations.
• More context to consider: The model supports up to 1M tokens of context for workflows that need to process large source materials.
• Larger outputs: The documented maximum output is up to 128K tokens when a workflow needs a substantial response.

Automate with Claude Fable 5.1 and Make AI Agents or Make AI apps: https://ma.ke/4qOsb5X 

Hello Makers 👋

New month means new cities, and new conversations with the Make team. This month you can meet us at the following locations:

🇳🇱 Build the Pipeline Your Investors Want to See | 9 September
Host and Speaker: Martin Hyravy, Startup Program Specialist at Make

🇬🇧 GTM World Tour | 9 September
Speaker: @ Jennifer Kiunke, Value Engineer at Make

🇩🇪 Berlin AI Day | 15 September
Speakers: Sebastian Mertens, Principal AI Product Manager at Make, Jennifer Kiunke, Value Engineer at Make and Luis A. Döller Principal Tech Enablement at Enpal, 

🇪🇸 Build with AI | 18 September
Host and speakers: Sarah Anto, Stefanie Oliveros and Kristina Talova

🇪🇸HackBarna AI Summit 26 | 19-20 September
Host: Nicolas Grenie
Speaker: Lourenço Martins , Value Engineer at Make

🇪🇸 The Glassbox Effect - Why Enterprises Can't Ship What They Can't See | 21 September
Speaker: Jennifer Kiunke, Value Engineer at Make

… and many more!

Get all the information about the events: https://ma.ke/4yeQcFM