We’re planning our migration from Camunda and someone on the team mentioned using autonomous AI agents to coordinate the workflow. The idea is having different AI agents represent different teams—finance, operations, engineering—and letting them work through the migration plan together. It sounds theoretically interesting but also kind of chaotic.
My main concern is that a migration has dependencies and constraints that need actual human judgment. Finance cares about cost, operations cares about downtime, engineering cares about technical debt. Those interests don’t always align, and I’m not sure an AI agent can mediate that in a way that doesn’t just optimize for whatever we told it to optimize for.
I get the appeal of using AI to handle coordination overhead—honestly, a lot of our migration delays come from scheduling calls and waiting for feedback from different teams. But I’m skeptical that we can replace that with agents without losing critical context.
For people who’ve tried this kind of thing: what actually happens when you set multiple AI agents loose on a cross-functional workflow? Do they surface real conflicts that need human decision making, or do they just make decisions that look good on paper but create problems later?
I’m going to be honest—I tried this approach last year and it went sideways. We set up agents to represent different departments in a workflow redesign project. The agents could propose changes and evaluate them against their own constraints.
What happened was they optimized locally. The finance agent would propose solutions that looked good for cost but created operational complexity. The ops agent would suggest changes that made sense for uptime but were expensive. They’d get stuck in loops arguing about tradeoffs without moving forward.
The real issue is that migrating from Camunda isn’t a pure optimization problem. It requires value judgments about what matters more—speed, cost, risk mitigation, technical maintainability. Those are human calls.
What actually worked better was using AI agents to handle the analysis and research part. They’d pull together data about current workflows, cost implications, technical complexity estimates. That reduced a lot of research grunt work. Then we used that analysis in actual human decision meetings.
The coordination part is still human. You need someone making the calls about which tradeoffs matter. The AI agents are research assistants, not replacements for a project manager.
The thing that surprised me is how much coordination is actually about communication and alignment rather than computation. Autonomous agents are great at computation but they miss the nuance in communication. When the finance person says “we can’t afford downtime,” they might mean we literally cannot shut things down for five seconds, or they might mean we need to avoid scheduled downtime during peak business hours. An AI agent might take it too literally.
I’ve seen better results using AI agents for specific narrow tasks within the migration rather than the whole coordination. Like an agent that analyzes which workflows are highest complexity, or one that maps dependencies between systems. Those are tasks where the AI can deliver clear value. The decision about what to migrate when? Still human.
Migrations are fundamentally about managing risk and making tradeoffs. Autonomous agents are good at finding optimal solutions within constraints, but they’re not good at deciding what constraints matter. You need human judgment on that.
I’ve seen teams use autonomous agents for specific workflow tasks during migration—testing compatibility between old and new systems, validating data transformations, running scenario analysis. Those work well. Full autonomous coordination of the migration? I haven’t seen that land well in practice.
I understand the skepticism. I was skeptical too until I actually worked with Autonomous AI Teams in Latenode.
The thing is though, you’re right that pure autonomy doesn’t work for strategic decisions. But that’s not really what Autonomous AI Teams are for. What I’m doing is setting up agents that simulate different scenarios and surface the tradeoffs clearly, then I make the actual decisions.
So instead of agents autonomously coordinating, I have agents that run different migration scenarios in parallel. One agent evaluates phased migration, another evaluates big bang cutover, another explores parallel running costs. They generate analysis I can compare. That actually saves enormous amounts of manual scenario modeling.
The coordination is still mine. But the AI is doing the heavy lifting on “what if” analysis that normally requires engineers to manually build and test scenarios. That changes the speed of decision making without removing human judgment from the decision.
For a BPM migration specifically, being able to rapidly test different migration strategies and see financial and operational implications… that’s genuinely valuable. The agents aren’t replacing the decision makers, they’re making the decision makers faster.