We started thinking about autonomous AI agents because some of our workflows were getting ridiculously complex. Multiple decision points, different AI models needed for different tasks, a lot of context shuffling between steps. It felt like the kind of thing that multiple AI agents working in parallel might solve.
So I built a proof of concept. The scenario was actually pretty reasonable: take customer support tickets, have one AI agent categorize them, another agent draft responses, a third agent flag anything that needed escalation. In theory, having these run somewhat independently would be faster and more maintainable than one monolithic workflow.
Here’s what I didn’t expect: the complexity moved, not disappeared.
When you’re running a single workflow with sequential steps, the data flow is straightforward. One step outputs, the next step inputs, done. When you have multiple agents, you suddenly need:
- Coordination logic—how do the agents signal to each other?
- Context management—what information does each agent get, and how do you prevent duplicating processing?
- State tracking—if one agent fails or returns something unexpected, what happens to the others?
- Error handling that’s actually thoughtful—cascading failures get weird fast with parallel agents
I thought this would be cheaper computationally because agents could work in parallel. It wasn’t cheaper—some of the complexity overhead actually made it more expensive than sequential execution for smaller workflows.
But where it did win was in situations where agents could genuinely work independently on different aspects of the same data. We set up a workflow where one agent was analyzing patterns, another was generating suggestions, and a third was pulling in external data. Those actually could run in parallel and that saved time.
The pattern I’m seeing is this: agent orchestration makes sense when you have genuinely independent tasks that can run in parallel. If the tasks are sequential or heavily dependent on each other’s output, a well-designed single workflow is actually simpler and cheaper.
Has anyone else built multi-agent workflows? I’m trying to figure out if I’m just not architecting them correctly, or if the complexity spike is real and just requires a different design approach.