Make AI Agents：自动化场景中的 Agent

Make is a versatile visual automation platform that allows users to connect various services like WordPress, webhooks, HTTP requests, and OpenAI into complex workflows. It offers powerful automation capabilities with features like filters, retries, error handling, and integrations, making it suitable for both simple and advanced business processes. The platform is designed to be user-friendly, enabling users to visually design, test, and adjust workflows without needing extensive technical knowledge. This flexibility makes it an excellent choice for businesses looking to streamline operations and reduce development and maintenance efforts. Make's value proposition lies in its ability to handle both straightforward automations and intricate logic within the same platform, providing a cost-effective solution for complex workflow needs. What I like best about Make is the flexibility of its visual automation platform. I use it to connect WordPress, webhooks, HTTP requests and OpenAI in fairly complex workflows without having to build everything from scratch. Once the scenario is correctly configured, the automation is very powerful: filters, retries, error handling and integrations make it possible to create robust business processes while keeping the workflow understandable visually. I also appreciate that Make can handle both simple automations and much more advanced logic within the same platform. Make offers very good value for the price, especially for complex workflows that would otherwise require custom development. For my use case, being able to connect WordPress, OpenAI, webhooks and other services in one visual platform saves both development time and ongoing maintenance effort. The value is strong, particularly as the automation becomes more complex. The main downside is that some technical issues are difficult to diagnose from the visual interface alone. I encountered a case where modules looked correctly connected on the canvas, but the actual scenario structure still treated them as orphaned modules, which caused unexpected webhook behavior. Error handling can also be a little unforgiving: a temporary HTTP/network issue can deactivate an instant scenario if no retry handler has been configured. It would be helpful if Make made these risks and the recommended retry/incomplete-execution settings more visible by default. Overall, the platform is powerful, but troubleshooting advanced scenarios sometimes requires digging deeper than the UI suggests. To enhance the user experience, it would be beneficial if Make provided more detailed diagnostic tools within the visual interface. This could include a module dependency map or a more intuitive error reporting system. Additionally, offering a guided setup for retry and error handling configurations could help users avoid common pitfalls and ensure smoother automation processes. Finally, integrating a community-driven knowledge base or forum directly into the platform could provide users with quick access to solutions and best practices shared by other users. Make helps me automate workflows between WordPress, webhooks, OpenAI and other services without having to build a custom backend for every integration. For my internship platform, I use it to receive new data, validate it, route it through an AI review process, handle retries and send the result back to WordPress automatically. This saves a significant amount of manual work and makes the process much more scalable and reliable. The biggest benefit is that I can design and adjust complex business workflows visually, test them step by step, and keep improving the automation without having to redevelop the whole system each time.

We use Make for orchestration that needs to remain visible to both development and operations. A typical scenario might start with a Custom Webhook, pass through Routers and Filters, and then call external services through HTTP modules. This setup makes it easy to adjust a branch when a business rule changes, without having to ship another version of our API. Error Handlers are where the platform became much more reliable for us. We use Retry, Resume, Skip, Commit, and Rollback depending on the type of failure, and Incomplete Executions give us a way to recover after fixing the underlying issue instead of treating every run as disposable. We really noticed the difference during a synchronization when a provider started returning intermittent rate limits. Our earlier scenarios would simply stop and leave us guessing which records had actually made it through, but adding a retry path made the flow far more predictable. Data Stores have also been useful for keeping small pieces of state, supporting idempotency, and maintaining references between executions. What I still miss are stronger diff and review tools between scenario versions. As scenarios grow, it becomes harder to track what changed and why. To keep things manageable, we try to keep flows small and move more complex logic into .NET when automation shifts from orchestration toward core application code. Make is great for connecting systems, but business rules shouldn’t quietly evolve inside visual workflows that aren’t being properly reviewed. Our B2B onboarding used to start only after Sales closed an opportunity. From there, someone had to manually create the customer across three different systems, send the relevant details to Finance, and open several internal tasks. We replaced that entire handoff-heavy process with a Make Scenario triggered by a Webhook. The Scenario calls our ASP.NET Core API to validate the CustomerId, then uses a Router to split the flow by product and region before creating the required records in the external tools. To prevent duplicates, we used a Data Store to persist the event ID so repeated webhooks wouldn’t create the same customer twice. We also added Error Handlers to retry temporary failures, without replaying steps that had already completed successfully. The first version surfaced a design mistake on our side: one branch continued even when the API returned a pending state. Instead of piling on another layer of conditions inside Make, we moved that rule back into the backend and allowed the Scenario to proceed only after receiving a valid decision. That kept the automation valuable without turning it into yet another place where domain logic lived. Overall, we ended up with fewer manual handoffs, fewer copy/paste errors, much clearer visibility into which system was blocking a run, and a workflow we could automate confidently without pulling critical rules out of our .NET architecture.

I love the ability to create automations with almost any platform and how Make is constantly evolving to make automations easier and better. I love that I am able to use AI to help build scenarios but also include AI performance in my scenarios. Once a scenario is set up correctly, it runs perfectly and consistently. The price for Make is very reasonable considering everything they offer. Support has been responsive and helpful. If you make an infinity loop accidentally, you can zap all your credits and even go into a deficit. I did that and when I reluctantly purchased more credits to help get me to end of the month when my supply would renew, my extra credits went towards the deficit. That was a learning experience. I'm a small business owner with multiple income streams. Automating my business processes helps me perform at a high level without the benefit of staff. This means I can spend my time earning and less time administrating.

Das Make-Team fügt sehr regelmäßig neue Tools/Schnittstellen hinzu, sodass es inzwischen wirklich für sehr viele Tools Möglichkeiten zur Verbindung gibt. Größtenteils funktioniert das auch sehr gut und über sehr weite Strecken kommt man wirklich mit "No-Code" zurecht. Durch die EU-Herkunft, war die Diskussion mit unserem Datenschutzbeauftragten auch etwas entspannter. Aktuell erscheint mir das Pricing zusätzlich auch noch etwas attraktiver, als bei Zapier. Besonders dann, wenn es etwas komplexer wird, hätte ich mir schon ab und zu eine ausführlichere Dokumentation gewünscht. Im direkten Vergleich mit Zapier habe ich ganz generell das Gefühl, dass es an der einen oder anderen Stelle etwas weniger intuitiv ist. Zumindest bei relativ einfachen Tasks, sollte das aber niemandem Probleme bereiten. Ehrlicherweise habe ich schlicht nach einer europäischen Alternative für Zapier gesucht und bin bei Make fündig geworden. Funktional konnte ich bisher wenige Unterschiede feststellen. So lassen sich einige Prozesse automatisieren und ohne großen Entwicklungsaufwand auch Tools "mit einander sprechen", die das von Haus aus nicht (so einfach) könnten.

Die Usability ist deutlich besser als bei vielen anderen Middlewarelösungen. Man kommt wirklich sehr schnell in das Produkt rein und kann in kurzer Zeit sehr effektive Automationen bauen, die verschiedenste Systeme miteinander kombiniert. Das Produkt hat bereits viele Anbindungen, obwohl es weniger bekannt ist, als viele Wettbewerber. Speziell das Pricing gefällt mir im Vergleich zum Wettbewerb auch extrem gut und deutlich fairer. Der Support ist super schnell und sehr gut. Leider kam es in der Vergangenheit immer mal wieder zu offline Zeiten, wo die Software nicht erreichbar war. Ab und zu haben einzelne Integration mal ihre Bugs, die aber eigentlich immer schnell behoben werden. Durch Make mussten wir für verschiedenste Systeme keine Integrationen mehr bauen, sondern konnten diese über Make verbinden. Das spart Zeit und besonders Geld. Durch den Editor, in dem man die Automationen bauen kann, spart man ebenfalls viel Zeit, da dieser deutlich intuitiver und übersichtlicher gebaut ist, als bei vielen Wettbewerbern. Besonders Integrationen zu Google Produkten und Slack sind sehr hilfreich für Updates jeglicher Art.

Make bietet zahlreiche Integrationen zwischen diversen Softwares. Wir benutzen es als Zapier-Alternative und konnten größtenteils unsere Automatisierungen 1:1 übernehmen. Dabei ermöglicht Make sehr komplexe Automatisierungen (sog. Scenarios), die grafisch gut greifbar aufbereitet werden. Es gibt eine Template-Bibliothek, um schnell einfache Automatisierungen zu erstellen, aber man kann diese dann noch sehr weit ausbauen. Preislich bewegt sich Make momentan in einem günstigeren Segment als der größte Konkurrent Zapier. Make ist relativ unintuitiv und kann für Nutzer*innen mit wenig Erfahrung schnell unübersichtlich werden. Das ist allerdings auch der hohen Featurefülle geschuldet und ich wüsste auch nicht, wie man es besser machen sollte. Manche Integrationen sind leider noch nicht verfügbar, das lässt sich aber oft über Webhooks lösen. Wir sind von Zapier zu Make gewechselt, hauptsächlich aus finanziellen Gründen. Es erledigt die gleichen Aufgaben: Automatisierung von Tasks und Integration von verschiedenen Anwendungen untereinander. Mit Make lassen sich sehr komplexe Prozesse abbilden und automatisieren.

What is our primary use case? I'm working on multiple Make, n8n, and a lot of tools. For sending some proposals and login systems, I use a tool called Bubble. Some of the workflows that are complex on Bubble, we used to do with Make. For Google Docs, AI automations, and content creation, I have a couple of things, and email commenting and replies for that are some examples. When I'm trying to build some workflows, there's a chatbot component. When I ask for something like this, it helps me in that. Or if I get a bug that I'm finding difficult to debug or understand the use case or the log, it clearly explains it to me and sometimes it advises me to do this or that, so that it's easy for me to fix it. Real-time functionality is really needed in most cases. For example, in AI automations, when a customer signs up into my portal and I try to send them a reply, the data would synchronize so then I can send them accurate data. There are a couple of use cases which are complex that I cannot explain on the call. Most of what I learned about Make is mainly from the templates only. Anything that I tried to do, I try to see who has done it already and try to understand and rebuild it. What is most valuable? The new AI feature that Make has launched is really amazing. The UI is pretty clear for me compared to Zapier. In terms of features, the flexibility of adding the code and doing all of that is the best thing that I appreciate about Make. It's complicated to give good feedback, but it's helping me in saving a lot of time in terms of manual input. There's a huge cost cutting in my application when I'm using Make. What needs improvement? Most of what I learned about Make is mainly from the templates only. Anything that I tried to do, I try to see who has done it already and try to understand and rebuild it. When I'm trying to build some workflows, there's a chatbot component. When I ask for something, it helps me in that. Or if I get a bug that I'm finding difficult to debug or understand the use case or the log, it clearly explains it to me and sometimes it advises me to do certain things, so that it's easy for me to fix it. One area for improvement is an auto-building feature. Another is how n8n has a chatbot completion where you can bring some LLMs into the workflow and integrating Ollama and all of that is something I felt is really needed for Make also. For how long have I used the solution? I have been using Make for three years. What do I think about the stability of the solution? I don't have any issues; it's as smooth as the other platforms, not very complex or hard to understand, but it's fine for me. It's working fine, and I don't have any major concerns about it, but my purpose and my use case is getting done. So I don't have many additional points there. What do I think about the scalability of the solution? It's scalable. I don't feel that for my requirement, I'm getting the best out of it. How are customer service and support? I haven't taken very much advantage of the services, but what I had earlier, they were doing the best job for my use cases and my problems. So they helped me. However, I'm not very extensively reliant on the customer support. I used to talk to the developers or the forum that Make was having. I used to go there and figure it out by myself. So very rarely I used to get in touch with the support team. How would you rate customer service and support? Neutral Which solution did I use previously and why did I switch? Something like n8n is an alternative. Recently I started learning to use n8n a lot, which has AI agent features, and it is open source. Right now, n8n is an open source platform. So I would be more interested in learning more and exploring more if Make is also an open source option. Because a lot of experiments can be done if it's an open source. How was the initial setup? It's straightforward and easy for me because I come from a tech background. It's quite easy for me. I don't know if a person from a non-tech background would find it a bit difficult, but for me it's very comfortable. What about the implementation team? I also referred to a couple of my internal team members and some of my clients to use Make for their business processes as well. What was our ROI? I've achieved a lot. The return is approximately 300%, and that you can think about, with an approximate range of around 500 to 600%. What's my experience with pricing, setup cost, and licensing? It's perfect. It's cost-effective and it's pocket-friendly. I don't have many issues with the pricing part. Pricing is quite comfortable for me. Which other solutions did I evaluate? Something like n8n is an alternative. Recently I started learning to use n8n a lot, which has AI agent features, and it is open source. Right now, n8n is an open source platform. So I would be more interested in learning more and exploring more if Make is also an open source option. Because a lot of experiments can be done if it's an open source. What other advice do I have? Each platform has its own keen usage and keen requirement. I feel that Make is doing the best of its use case. Each platform has its own specialty in terms of UI, workflow, or customer support in terms of building the tool more reliable to customers and accessible to people when issues come. For now I feel that it's doing good. I would rate this review a 9 out of 10. Which deployment model are you using for this solution? Public Cloud If public cloud, private cloud, or hybrid cloud, which cloud provider do you use? Other

What is our primary use case? My primary reason for using Make is workflow automation. When it comes to scraping data from portals like LinkedIn or any other website and then storing it on a Google Sheet, that is the most time-consuming task. I was able to create a workflow and automate it through agentic AI, which was a significant achievement for me. How has it helped my organization? Make could possibly provide scenarios on their homepage. The moment I log in to my account, Make could ask me what I really want to do in a visual way and not in text form. When I see visuals indicating that I'm going to scrape data, I would click on a thumbnail, or if I'm going to create a workflow, I might look for something like an always-on agentic AI. These can be put onto a thumbnail which can further make my work faster on make.com. There is still a lot of difference when it comes to how the credits come into use and how to monetize what is being offered by Make. For me to be able to monetize what Make offers is equally important as using it. If my clients are going to pay me in a span of 60 to 90 days for a service that is consumed, I need to understand that within that 90-day timeframe, whether using free credits or otherwise, I have to justify the cost. Can I give it more work than the ones I'm currently doing? The ultimate factor is that there are many tasks that an executive does in a recruitment consultancy. Within a recruitment consultancy, being on the agency side of the business where I do business development, prospecting, and lead generation, I document information for each candidate, and then create a business model canvas for each lead I prospect. When presented with 10 different requirements from a Fortune 500 client, I need to quickly close those. Can those free credits be really worthy at that point in time? My commitment needs to be based on the inventory I have. Without really looking at many things, I feel that Make is a space where I can automate a lot of my work, and that says a lot. What is most valuable? When I think about automating my work with Make, I would say that the credits given on a monthly basis are useful for me to use them. The integration of webhooks and understanding how which particular tool can be integrated and how it can be played further is a significant achievement. Make is a space which can be explored further. If I have to scrape data and if that consumes five to six hours, the fatigue that comes after doing repetitive tasks does not allow me to do any other work for another three or four hours. This means the task that I would complete in a span of one day is completed in a matter of minutes by using Make. What needs improvement? Make is the reason why I feel confident about taking up newer and newer assignments. Make could possibly provide scenarios on their homepage. The moment I log in to my account, Make could ask me what I really want to do in a visual way and not in text form. When I see visuals indicating that I'm going to scrape data, I would click on a thumbnail, or if I'm going to create a workflow, I might look for something like an always-on agentic AI. These can be put onto a thumbnail which can further make my work faster on make.com. There is still a lot of difference when it comes to how the credits come into use and how to monetize what is being offered by Make. For me to be able to monetize what Make offers is equally important as using it. If my clients are going to pay me in a span of 60 to 90 days for a service that is consumed, I need to understand that within that 90-day timeframe, whether using free credits or otherwise, I have to justify the cost. Can I give it more work than the ones I'm currently doing? The ultimate factor is that there are many tasks that an executive does in a recruitment consultancy. Within a recruitment consultancy, being on the agency side of the business where I do business development, prospecting, and lead generation, I document information for each candidate, and then create a business model canvas for each lead I prospect. When presented with 10 different requirements from a Fortune 500 client, I need to quickly close those. Can those free credits be really worthy at that point in time? My commitment needs to be based on the inventory I have. For how long have I used the solution? I have been using Make for close to about three to six months. What other advice do I have? My rating for Make is 90 out of 100. I give it 90 because there is always scope for improvement, but I feel that this product is exceptional. I have not seen anything like Make so far. A perfect 10 would mean there is nothing more to improve. A nine probably means there is scope for improvement, changes to be made, and relevance to be established. Even with the world's best service, I question whether the world really recognizes it or if it can be utilized economically. Giving me a thousand credits per month can be consumed quickly, but none of those things accumulates, nor is there an unlimited version that can wait until I become profitable before offering me the first paid plan. I give commitments based on the number of credits being offered. Make is deployed in my organization on a public cloud. I use the make.com website itself. I signed up directly through the Make website. I would say to make your first use case and understand what the product is. Each entrepreneur or executive must understand what inventory they add by taking a subscription to Make. When you realize you have a machine that can save you time, it still needs to be understood that it can save you time based on the credits. If the credits get exhausted quickly, then you cannot save any further time. I am neither a partner nor a reseller. I am just a customer. I think it is a wonderful product, and if the aspect of giving time-based or quarterly credits instead of monthly credits comes into play, it could allow an entrepreneur or executive at any designation in an organization to make one or two full cycles over a quarter, which could be a significant achievement. I rate Make at 90 out of 100. Which deployment model are you using for this solution? Public Cloud If public cloud, private cloud, or hybrid cloud, which cloud provider do you use? Other

What is our primary use case? In automating any kind of user workflow or business process workflow, everything leads back to how data is being transferred, modified, or saved across systems while using Make automations. For example, if we have our CRM systems on monday.com and we are using a Google Form to collect responses for a particular item, we use Make to understand the responses and pass it properly into the monday CRM and send relevant communications using email or text. In our current organization, we have automated well over 600 processes including all the major workflows of student onboarding, student discontinuation, team member handling, tickets, and CRM manipulation. We have across 2,000 to 3,000 scenarios in the system, with over 500 to 600 workflows automated. What is most valuable? In terms of communicating between systems and between platforms, Make would be the primary tool of choice because instead of going about creating or writing API codes for multiple platforms, Make creates a seamless connection where modules come in and take care of most of the heavy lifting of code. The visual workflow builder is something that stands out prominently, providing a no-code platform which connects across multiple different integrations and platforms. This means that even making changes to a particular platform can trigger a different functionality on a separate platform, so integrations definitely come into the picture. Additionally, the ability to create webhooks on the go is another particular feature that stands out. Having an in-built or in-house data store to manipulate data and store it across multiple different scenarios is one more feature which particularly makes it stand out. The new work grid feature makes it visually very appealing to see how the automation workflow works. Make has positively impacted my organization by reducing the number of hours that go into validating and verifying the flow of a particular repetitive process, which would otherwise take multiple hours, to just a few minutes to review for more than 1,000 or 2,000 case scenarios. Any fallback cases also go seamlessly into the incomplete executions DLQ for me to analyze and resolve. What needs improvement? The ability to have further customizations to certain functions and modules that are pre-existing may be something that could be improved. Regular updates to cover up any bugs or issues or proper repeated community asks is also essential. Pricing is definitely something that is on a higher tier where we are consuming around 1.5 to 2 million operations per month, which makes the bill quite substantial. Keeping that in mind, there can be instances where the scenarios can be optimized properly to reduce operation count. One other thing that probably needs improvement is proper documentation of internal automations, which could be a tool that queries all the current scenarios and acts as a chatbot or helper to find certain values or parameters used across scenarios. For how long have I used the solution? I have been using Make for about three to four years. What other advice do I have? My advice to others looking into using Make is to go through the documentation first and go through the training and certification courses. Those are pretty detailed and almost cover all aspects that you would need to implement in an automation scenario. I would rate this product an 8 out of 10. Which deployment model are you using for this solution? Private Cloud If public cloud, private cloud, or hybrid cloud, which cloud provider do you use? Other