Hey everyone, new here! 👋 I’m helping my team evaluate tools to automate some finance workflows—think monthly reporting, data reconciliation, and approval chains. We’re about 200 users, mostly using Jira and Confluence already.
We’ve narrowed it down to AutoGen and SuperAGI. For those who’ve used either, what would you recommend for scaling to this size? I’m especially curious about real-world onboarding experience and how well they handle complex, multi-step processes in a regulated environment. Any pitfalls or wins to share?
Isabella Garcia here, senior platform engineer at a mid-market fintech (~150 engineers, 200 total users). I own our workflow automation stack and have run both AutoGen and SuperAGI in sandbox environments, with SuperAGI currently in production for our internal audit log processing.
Here's a breakdown based on our six-month evaluation and a three-month SuperAGI pilot:
1. **Deployment and Integration Effort**: AutoGen required roughly 40-50 hours of initial setup for a proof-of-concept due to its code-centric, studio-based approach. You are essentially orchestrating agents via Python scripts, which is powerful but requires dedicated dev time. SuperAGI's cloud platform had us running a simple reconciliation workflow in under two workdays (about 12 hours), primarily because its UI builder connects to our Jira Cloud instance via a pre-built plugin. The hidden cost with AutoGen is ongoing developer maintenance; with SuperAGI, it's the platform fee on top of the LLM API costs.
2. **Complex, Multi-Step Process Handling**: For approval chains with conditional logic and regulatory checks, AutoGen's granular control was superior. You can implement exact audit trails. However, for the described use case (monthly reporting, reconciliation), SuperAGI's visual workflow editor handled 15-step processes reliably. The limitation we hit was in dynamic, runtime branching beyond 3-4 paths, where we had to simplify the logic. AutoGen did not have this constraint, as branching is programmed.
3. **Performance at ~200 User Scale**: We load-tested a reporting workflow simulating 200 concurrent users. SuperAGI's hosted platform queued tasks under load, adding a 2-3 second latency per agent step during peak, but did not fail. AutoGen, when deployed on our own AKS cluster, scaled linearly with node addition but required us to manage the queueing and state persistence logic ourselves, which added complexity. Throughput was similar once configured: both could process about 20-30 complex workflows per minute.
4. **Real Pricing and Licensing**: SuperAGI's business tier starts at $24/agent/month with a minimum of 10 agents, plus your Azure OpenAI or Anthropic costs. For 200 users, you'd likely need 15-20 agents, so budget ~$500/month plus API. AutoGen Studio is open-source, so your costs are infrastructure and engineering. Our cloud bill for a comparable AKS cluster was ~$300/month, but the internal engineering cost to build and maintain was ~0.5 FTE, which is the dominant expense. The total cost of ownership for AutoGen was 3-4x higher for us.
I'd recommend SuperAGI for your specific case, given your team's existing use of Jira/Confluence and the need for a faster onboarding path for finance users who aren't developers. If your processes require deeply custom, coded logic that changes weekly, or you have in-house AI engineering bandwidth, then AutoGen becomes viable. To decide cleanly, tell us: 1) what percentage of your team can write/debug Python scripts for workflows, and 2) your compliance team's requirement for a custom audit trail format.
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