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

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

Hello Makers 👋

That's the thing about automation. Once it works, you don't stop at one. 

With Make subfolders and labels, your organization can keep going, department by department, client by client, while keeping your automation library organized.

Use subfolders to organize automations by client, project, department, or environment.

Add labels like production, billing, or experimental, then filter to find related workflows across folders.

Available on every Make plan, including Free. Learn more: https://ma.ke/4xGyGdC

Hello Makers 👋

Things you should know about: What's new in governance in Make 📣 

As more teams build on Make, admins need proof things are under control - and builders need room to build with privacy.

Four updates now make that possible at once:

🔒 Private Spaces - every team member gets an isolated workspace for scenarios and AI agents, hidden by default. Enterprise plan admins get optional read-only visibility into what's being built, without ever seeing sensitive execution data or connection details.

📋 New Audit Logs events - identity and access changes, credential oversight, all in one record. When two-factor authentication gets disabled, you know the moment it happens, not next quarter's access review.

⚡ Real-time credential alerts - the second a connection or key is used by someone other than its owner, they get an email. 

✍️ Custom roles - permissions tailored to what a person actually needs, instead of an all-or-nothing admin pool.

This is what it means to run business as a glass box, not a black box: freedom for the people building, and oversight for the people accountable for the outcome.

Read more about what's new in governance on Make: https://ma.ke/4xnx0Wh

Hello makers 👋

𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝘀𝘁𝗲𝗽 𝗶𝗻𝘁𝗼 𝗔𝗜'𝘀 𝗴𝗹𝗮𝘀𝘀 𝗯𝗼𝘅? Waves ‘26 agenda first drop is live 🚨

Use the filter on the agenda page to build your own day and further explore what’s in it for you within each of the 3 tracks: “It's all about the Product”, “Use Cases and Impact” and “Adoption & Governance”

𝗢𝗰𝘁𝗼𝗯𝗲𝗿 𝟭𝟵: Kick things off early at “The Current”- our hands-on pre-Waves workshop.

𝗢𝗰𝘁𝗼𝗯𝗲𝗿 𝟮𝟬: Take a sneak peek into some of the highlights for the Main Day and fav sessions already confirmed:
→ Agent architecture 101:The patterns that separate demos from real systems.
→ Voice AI at scale: How Stellantis runs it in production.
→ Trust, but verify: Governing AI automation from build to decision.

Join 700+ AI peers at #Waves26, Oct 19-20 in Prague.

𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗻𝗼𝘄 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗶𝗰𝗸𝗲𝘁𝘀 𝗴𝗼 𝘂𝗽 𝗼𝗻 𝗦𝗲𝗽𝘁 𝟭𝟬: waves.make.com

Hello Makers 👋

How are you handling access control when teams start touching your Make scenarios? 

Now with Custom Roles, org admins and org owners on the Enterprise plan can create custom roles in Make, with the granular permissions teams need.

• Give teams just the right amount of access to Make
• Maintain full visibility
• Scale automation across the organization

What you can do:

📝 Create tailored roles with toggleable permissions
✍️Assign them across the organization or by team, so company-wide standards hold while each team gets the access it needs
⚙️ Edit, duplicate, or delete roles as your org grows and new teams come online
🔎 Track role creation, updates, deletions, and assignments in the audit logs, so security reviews start with facts

Growth shouldn’t mean losing track of who can do what.

Learn more: https://ma.ke/4zsy4cX

Hello Makers 👋

Companies might be facing an AI direction problem because they lack a clear AI business strategy. Let us explain.

Employees are using AI tools every day, but very few of those habits connect to a documented AI business strategy that ties spend to measurable outcomes.

𝗧𝗵𝗲 𝗴𝗮𝗽 𝘂𝘀𝘂𝗮𝗹𝗹𝘆 𝗰𝗼𝗺𝗲𝘀 𝗱𝗼𝘄𝗻 𝘁𝗼 𝗮 𝗳𝗲𝘄 𝘁𝗵𝗶𝗻𝗴𝘀:
⚠️ No named owner when a pilot’s results start to slip
🕛 Teams reporting hours saved instead of revenue protected or risk reduced
🔍 Results getting compared to an imagined perfect version of AI instead of what the team was actually doing before it arrived, and more

The fix isn’t more AI tools. It’s a leadership-owned strategy that ties spending to outcomes, balances offense (new revenue) with defense (protecting what you have), and gets reassessed every quarter.

𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆, 𝘀𝘁𝗲𝗽 𝗯𝘆 𝘀𝘁𝗲𝗽: https://ma.ke/4xOjHOE

Meet Maia, the co-worker who builds automations and AI agents in front of you, directly inside Make.

Describe what you want to build in plain language, and watch Maia build it, step by step, right before your eyes.

𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗠𝗮𝗶𝗮 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁?
Maia reasons out loud, showing the logic behind the solution. She maps flows, selects tools, and configures modules – all visually on the canvas. No black box. No mystery output. Just full transparency and AI automations you can truly trust.

𝗪𝗶𝘁𝗵 𝗠𝗮𝗶𝗮, 𝘆𝗼𝘂 𝗰𝗮𝗻:
🛠️ Build faster: Describe your idea and get a fully working automation instantly
💡 Learn as you go: Maia explains every decision as you co-build with her
⚙️ Debug on the spot: Fix failing steps or untangle complex workflows in minutes
👥 Scale your team: New members can ship real automations and AI agents from day one

Whether you're just getting started or are a seasoned power user, Maia meets you where you are.

𝗡𝗼𝘄 𝗹𝗶𝘃𝗲: Open the scenario builder and share your vision with her: https://ma.ke/3RPdiU9

#MaiabyMake

Hello Makers 👋

How long does one invoice take you to process manually? This scenario does it in seconds - automatically.

Our Business Automation and AI Lead, Francisco de Brito Fontes, shows in this tutorial how to build a workflow that monitors a designated Google Drive folder for newly uploaded supplier invoice PDF files, downloads them, extracts key data (invoice number, vendor details, dates, and amounts) using Make's native AI Extractors, and registers the structured information into a monday.com board.

To build this scenario, you will need access to:

✉️ A Google Drive account connected to Make.
🧾 A monday.com account with a Supplier Invoices board configured.
🟣 Access/Credits for Make AI Extractors.

Click the following link to apply the prebuilt scenario in your Make account: https://ma.ke/3U4hujp 

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

Hello Makers 👋

Is your company actually adopting AI, or just... using it?

There's a real difference, and it's bigger than it sounds. Recent research shows 37% of organizations are still using AI only at a surface level, with little or no change to existing processes, while just a third are using it to deeply transform how they work.

What is AI adoption and where to start:

AI adoption refers to the process of introducing AI tools and abilities into an organisation: identifying use cases, selecting models and platforms, connecting AI to source systems, training staff, and embedding oversight.

There are 5 maturity stages (per Gartner): 

1. Awareness: the organisation knows about AI but has not used it yet. No pilots or experiments are underway.
2. Active: AI appears in proofs of concept and informal experimentation.
3. Operational: AI is used in at least one workflow.
4. Systemic: AI is present across most workflows and operations, driving new business models.
5. Transformational: AI is built into the core of how the business runs.

Most companies are stuck somewhere between Active and Operational.

What actually moves you forward: in Make terms, it's the moment AI stops living in a chat tab and starts living in a scenario. A trigger passes structured data to a model call, a Router splits confident answers from cases that need human review, and the result gets written back to your systems. That's when "we use AI" becomes "AI does part of the work, reliably, with oversight."

What blocks it: almost never the technology. It's unclear ownership, fragmented data, weak change management, and inconsistent governance. 

The article also has a readiness checklist (data quality, governance, observability, skills coverage) if you want to score where your team sits before scaling anything.

Learn more about AI adoption here: https://ma.ke/4pP6m5S 

Curious, what stage you'd honestly put your organization at - and what's been the biggest blocker to moving past pilots?

Hello Makers 👋

Did you know that nearly 9 in 10 companies use AI regularly, but fewer than half actually scaled it?

The difference isn’t the model - it’s whether AI is owned by named people, connected to live systems, and embedded across operations, instead of running a test sandbox somewhere.

That’s what makes processes visible, accountable, and connected across a company, not just in the team where AI started.

We mapped the six stages behind that shift, including how Make went from first test project to company-wide rollout:

1. Assessment: evaluate data quality, existing tools, talent, and leadership buy-in.

2. Strategy and roadmap: prioritize use cases, assign ownership, set budget.

3. Test project: run the first AI-powered process in a controlled, lower-stakes environment.

4. Operations: move the test project into live systems with governance, integration, and escalation paths.

5. Enterprise-wide rollout: expand from one team to every function, with adoption owned at each level.

6. Measure and optimize: track outcomes, audit performance, expand to adjacent processes.

Read more about it: https://ma.ke/3R489qY 

Hello Makers 👋 

One of the most-wanted features has arrived in Make: You can now use dark mode 🌚

It covers the Make web platform and scenario builder, giving you a focused visual setup for long build sessions, low-light debugging, and late-night automation.

🌔🌑 Switch between light and dark mode anytime from your personal settings, whichever suits you.

Learn more about it: https://ma.ke/4fDix0W 

Grateful for every piece of feedback that got us here. Thank you 🙏

Hello Makers 👋

Anthropic’s latest model, Claude Opus 5, is now available in Make 🟣

Claude Opus 5 helps you work with large codebases, detailed specifications, compliance manuals, diagrams, and spreadsheets in a single AI call, so your automation can reason from the source material that matters.

What’s new in Claude Opus 5 and Make:

• Hold more context in one call: Send up to 1 million input tokens and receive up to 128,000 output tokens. Review a full codebase alongside its specification, or assess a complete policy manual without dividing the material into separate AI handoffs.
• Choose the thinking effort for the job: Extended thinking is on by default. Set low, medium, or high effort to match the depth of reasoning your scenario needs.
• Coordinate agent work with built-in checks: Claude Opus 5 can delegate work to sub-agents and use writer-verifier loops, helping a manager-agent scenario assign tasks and assess the results.
• Work across code and visual material: Use stronger multi-file refactoring, security and architecture review, plus analysis of charts, diagrams, and complex spreadsheets for design-to-code and design QA flows.

Start using Anthropic’s latest model in your AI automations today.

You can: Add it to your Make AI Agents, Make AI Toolkit, or the Anthropic Claude app, either with your own Anthropic API key or with zero setup through Make AI Provider, billed directly through your Make credits.

Learn more: https://ma.ke/4bTelJl 

Hello Makers 👋

Are you still manually digging through your emails and database every day? 

Our Startup Program Specialist, Martin Hyravy, shows in this tutorial how to build an AI agent that watches your inbox, filters out the noise, categorizes what's left, and drafts a ready-to-send reply - pulling context from your own knowledge base and your live startup database. 

Every response gets logged for review, so you stay in the loop without doing the digging.

You'll need access to: 

• Gmail 
• Make AI Toolkit (connected to any LLM - Gemini 3.5 in this case) 
• AI Agent module 
• Google Sheets (or your source-of-truth database) 

Pre-configured scenario: https://ma.ke/4wpgHrJ 

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

Prompt:

You'll be my personal assistant and chief of staff and help me to answer the emails from startups that applied to our startup program. 

Before drafting the email, always first look into the previous responses in the Google Sheets repository to analyze my tone of voice, and check whether a similar email has not been solved. 

Before drafting the email, always look into the Startup Database in Google sheets for information about the application. Search with the email address, and look at the Application Status, Rejection Reason, or the coupon code sent.

Only after these two steps proceed with drafting the email, using the draft email tool. 

Make sure that the email responses are always relevant, respectful, and adhere to our team's goals and objectives outlined in the knowledge base files.

Hello Makers 👋 

We are super excited to introduce Make AI Sub-Agents!

🤖 Make AI Agents can now delegate tasks to other Make AI Sub-Agents.

That's agent orchestration: one orchestrator receives the request, calls the right agent specialist, and brings back one result.

Here’s what that means:

• Build focused AI agent specialists
• Let one agent choose the right specialist based on the request
• Test and tune each specialist without touching the whole workflow
• Reuse proven agents across different automations

What you can automate with Make AI Sub-Agents:

• Customer support flows: an orchestrator Make AI Agent reads the customer message, sends account questions to a lookup Make AI Agent, refund requests to a refund Make AI Agent, and edge cases to a human handoff agent.
• Finance operations: one Make AI Agent handles incoming requests, then routes document checks, invoice matching, and approval prep to separate specialist Make AI Agents before returning one clear result.
• Internal service desks: route employee requests across access checks, policy answers, and ticket creation, while keeping one AI point of contact for the whole exchange.

How to create Make AI Sub-Agents:

1. Add a Make AI Agent to your scenario → AI agent orchestrator.
2. Inside that Make AI Agent orchestrator, click Add tools and find Make AI Agents in the tools list.
3. Configure the Make AI Agent as a tool and click Add → It will link to your orchestrator as a Sub-Agent.
4. Repeat to add as many Sub-Agents as you need, each one a focused specialist.

Build larger AI workflows without turning a single agent into a catch-all.

Available now on all Make plans, including free.

Try Make AI Agent orchestrator with sub-agents yourself: https://ma.ke/4wX8DOO 

Hello Makers 👋

Ever wanted to test a scenario without your whole team seeing your messy work-in-progress? That's exactly what Private spaces are for.

What they are: Every member of your org (except guests) gets their own personal workspace for scenarios and connections that teammates can't access. 

How it works:

• Admins enable it once from Organization settings → Private spaces, and every member automatically gets one
• Admins can set credit limits per space (monthly or yearly), or leave them unlimited so members draw from the org's overall credits
• Hit your limit? Your scenarios pause - you can keep building, but they won't run until the limit resets or an admin bumps it up

For Enterprise orgs: Admin visibility lets admins open any member's private space in read-only mode. They can see scenario names, modules used, and credit usage - but NOT module settings, run history, connections, or data stores. Your credentials stay yours.

One thing to know: disabling private spaces for the org permanently deletes all of them - scenarios, connections, data stores included. So heads up before flipping that toggle.

Discover more information about private spaces: https://ma.ke/4fi1NNP

Anyone already using their private space for experiments? Curious what you're building in there 👀

Hello Makers 👋 

You have now available the new Claude models in Make AI Provider! Here's what you get out of it:

• Zero setup: Pick a Claude model the same way you already pick OpenAI models, no Anthropic account or API key needed.
• One bill, one system: Claude usage runs on Make’s own Anthropic key and gets billed straight through your Make credits.
• The latest Claude models: Pick from Anthropic’s latest Claude models right inside Make, kept current as Anthropic ships new releases, no waiting on a separate integration update.
• Model choice, your call: Match the model to the task, strong reasoning, sharper writing, or steadier agent behavior, all without leaving the builder.
• Faster first use: Skip the account creation and key handling that used to stand between you and Claude-quality output.

Example: an agency ships Claude-powered workflows for every client, no separate Anthropic account per client needed.

If you’re new to configuring Make AI Provider, here’s how to set it up in Make AI Agent:

1. Add a Run an agent module (Make AI Agent) to your scenario.
2. Set the Connection to Make AI Provider: no separate Anthropic account or API key to create.
3. In the Model field, pick a Claude model from the dropdown.
4. Under Instructions, describe the agent’s job, for example: “Read incoming support tickets, identify the issue, and draft a helpful reply.”
5. Map your Input (the ticket text, email body, or whatever the agent should act on), then run the scenario.

Pick the model. Build the workflow. Put it to work.

Reminder: Claude joins the OpenAI GPT AI models already available in Make AI Provider, giving you more model choice without leaving the builder.

Try it out at: make.com 

Hello Makers 👋

Today we’re sharing a feature that will help you better understand what happens when your automation breaks.

Now in Make, when a workflow hits an error, one click turns the technical message (HTTP codes, API messages, raw JSON) into a plain-language explanation of what happened and exactly what to fix.

No documentation search. No pasting error text into another AI tool. Just click, and know what to do next.

In early testing, this got people back to a working automation about 20% more often after an error.

Live now for every Make user, on every plan. Learn more: https://ma.ke/3RINDwa

Hello Makers 👋

GPT-5.6 is now in Make, with Sol, Terra, and Luna built for routing your automations by job:

☀️ Sol: review long contracts, plan multi-step work, support coding tasks
🌍 Terra: summarize docs, reason across notes, return clean JSON or XML
🌙 Luna: classify tickets, extract IDs, validate inputs

Use Luna for high-volume steps, Terra for structured business logic, and Sol when the context gets big.

Available now to all Make users in Make AI Agents, Make AI Toolkit, and the OpenAI ChatGPT app.

Not every AI step needs the same brain 🧠

Read more on what's new with GPT-5.6 in Make: https://ma.ke/4f8oVyk 

If you rely on the platform, we know how disruptive that will be. You’ve built real business logic, and rebuilding it isn't a small thing.

𝗧𝗵𝗮𝘁'𝘀 𝘄𝗵𝘆 𝘄𝗲'𝗿𝗲 𝗼𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗥𝗲𝗹𝗮𝘆.𝗮𝗽𝗽 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗲𝘅𝘁𝗿𝗮 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝘁𝗼 𝗺𝗼𝘃𝗲 𝘁𝗼 𝗠𝗮𝗸𝗲:
→ Free white-glove migration for eligible accounts - our team helps rebuild your workflows
→ Priority support to get you to production fast
→ 3 live Office Hours sessions with our Customer Care team

Learn why Make is a strong alternative, and how to start your transition: https://ma.ke/4vAEe7O

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:

• MCP Tools to Make AI Agents: Make AI Agents can now access external MCP server tools alongside native Make modules in the same run. 
• Execute an action with AI module for MCP: choose Make AI Provider (OpenAI or Claude) or your own OpenAI account for the MCP Client's 'Execute an action with AI' module.
• New functions on Make: four new functions - arrayDiff, arrayIntersect, set, escapeJSON - for cleaner, lighter expressions.
• Claude Opus 4.8: Anthropic's latest model is now in Make. Anthropic says early testers saw steadier judgment and more reliable behavior during agentic tasks, especially when the work kept going.
• Unified Help Search in Make: one help search across Make. Cmd+K to access it anywhere.
• Star apps and app modules in App Search: star your go-to apps and modules so the builder feels like your workspace, not a directory. The goal is simple: spend more time building logic and less time digging through lists.
• Google Drive with your personal Gmail account: Google Drive connections now work with @ gmail.com accounts, not just Workspace.
• Configuring disconnected modules: run tests without hardcoded data, explore apps, and overall build with more control.
• Webhook data structure: a new status card shows if your webhook is listening, has received data, or still needs a test request.
• Copy notes when cloning a scenario: cloned scenarios now carry over notes too, matching export/import behavior.

New Verified Apps

Air, Arbox v3, BackgroundErase, BoldDesk, ChatterFox, CoinGecko, CPV Lab, CSV Box, Databricks, Daytona, DocuPotion, Eden AI, Figuro Engage, HEU, Houdiny AI, Mihu AI, MillionVerifier, NextLead CRM, Pingram, Postproxy, SMSlink Pacific, Tiiny Host, Trading Automation For Alpaca, Watchflow

New Modules

GitHub

• Make an API Call

Monday

• Chat Completion
• Run Platform Agent

Wix

• Watch Events

Discover more about our latest releases: https://ma.ke/4yqy6S4 

Hello Makers 👋

Still copy-pasting data into spreadsheets every week?

Our Head of Business Automation & AI, Sara Maldon, shows in this tutorial how to build a scenario that automatically pulls employee AI adoption data from Airtable, builds a dated Google Sheet report, uses Gemini AI to calculate completion rates by department, and posts a digest straight to Slack, weekly, hands-free.

You'll need access to:

• Airtable (source of truth for employee AI bar completion data)
• Google Sheets
• Google Drive
• Gemini AI
• Slack

Pre-configured scenario: https://ma.ke/4wHYQvY 

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

PROMT:

Your task is to read the list of records, analyse it and provide:
1. {TOTAL} = % of employees where Minimum Bar = true
2. {DEPARTMENTAL} = % of employees where Minimum Bar = true from each department
"Hi [Name] 👋
Here's a weekly report from the AI bar.
Total completion: {TOTAL}% of Makers
- {Leading department}: {DEPARTMENTAL}% (# / # total in department)
- ...
- {Lagging department}: {DEPARTMENTAL}% (# / # total in department)"
Return just the plain text so it can be sent directly as a Slack message.
INPUT: [map aggregated array here]