Recently got involved in a project where we’re using autonomous AI teams to handle an end-to-end process. The concept is solid—you’ve got an agent that analyzes incoming requests, another that routes them, another that drafts responses. On paper it looks like it should parallelize work and cut throughput time significantly.
In practice, coordinating them revealed some unseen complexity. One agent finishes its work but the output format doesn’t quite match what the next agent expects. Or timing issues where one agent is waiting for another. Or worse, cases where two agents disagree on the interpretation of an input and you need a tiebreaker.
When you’re building ROI models on top of this, you’ve got to account for the orchestration overhead—it’s not just the sum of what individual agents do. And testing scenarios to validate throughput assumptions is harder because the interaction patterns aren’t straightforward.
I suspect the ROI value is real, but the build and troubleshooting complexity is higher than single-agent workflows. Has anyone else mapped out where the actual complexity spike happens when you’re coordinating multiple agents, and how that affects your timeline and cost estimates?