Hey everyone, I'm new here and hoping to get some advice. I recently convinced my team to switch our automation workflows from Make (formerly Integromat) over to AgentGPT, mainly because the whole autonomous agent concept seemed really powerful for our project management and support ticket routing.
But now I'm hitting a major roadblock I didn't anticipate: the lack of native app support. With Make, we had dedicated apps for things like our cloud storage, our internal chat tool, and our helpdesk software. Setting up a connection was usually just "Sign in and pick your account." With AgentGPT, I'm finding myself needing to use the HTTP/Send Request modules for *everything*, which means I have to manually handle API keys, figure out the right JSON structure, and it's just... a lot 😅.
For example, just to create a simple task in our project management tool when a specific type of support ticket comes in, I'm now writing out raw API calls. In Make, that was a couple of clicks. I feel like I'm spending more time wrestling with API documentation than building useful automations.
Is this just the way it is with AgentGPT? Am I missing something obvious? I love the agentic logic, but the integration part feels like a huge step backwards in terms of user-friendliness. Does anyone have a good workflow for managing these custom connections, or do you just get used to it? Maybe there's a best practice I haven't found yet.
I'm a data engineering lead at a 100-person fintech, we run our ML pipelines and backend automations on a mix of n8n, Prefect, and some custom agents.
* **Target Audience:** Make is built for SMBs and ops teams. AgentGPT is for devs comfortable building their own integrations. If your team isn't writing API calls weekly, it's the wrong tool.
* **Real Cost:** Make's visible cost is $10-20/user/month. AgentGPT's cost is developer hours. I spent 8 hours last quarter just maintaining auth for our internal tools because their API changed. That's a real cost.
* **Integration Effort:** Make's "native" app is a pre-built connector with managed OAuth. AgentGPT's "native" support is the HTTP module. You are the app builder, full stop.
* **Where It Breaks:** AgentGPT breaks on API versioning and schema changes. If your project management tool updates its API, your flows fail until you manually rewrite the JSON. Make's connectors usually handle that.
My pick is n8n for your case. It keeps the low-code UI and has hundreds of native apps like Make, but you can drop into code for agentic logic when you need it. If you're fully committed to agentic workflows and have a developer to own it, stick with AgentGPT and build a shared connector library. Tell us your team's dev-to-non-dev ratio and if you have a dedicated automation owner.
Prove it with a benchmark.
You've put your finger on the hidden cost perfectly. That "developer hours" line is so real, and it's often the deciding factor that gets missed in the feature comparison. Teams see "agentic" and think "more powerful," but they don't always budget for the ongoing maintenance overhead.
Your n8n suggestion is solid for bridging that gap. I'd just add that for teams who've already jumped to AgentGPT, it might be worth looking at whether they can use it for the truly unique agent logic while still routing simpler, standardized tasks back to a tool like Make or n8n. Trying to force every single workflow into one pure agent platform can be a recipe for burnout.
Keep it real, keep it kind.
That's a really good point about using each tool for what it's best at. I'm trying to learn more about API connections now, and it's definitely slow going.
For someone new like me, how do you even start figuring out which workflows are "truly unique agent logic" and which should just go back to a simpler tool? Is there a rule of thumb?