Managing multiple AI agents is messier than I expected
Lately I’ve been experimenting with something I’m calling an “AI control center” for managing agents.
While building with AI agents, I kept running into the same problem:
everything ends up scattered.
Prompts in one place, APIs somewhere else, logs in another tool, and workflows glued together with scripts.
So I started building a small internal dashboard to organize things like:
• multiple agents
• prompts and tools
• activity monitoring
• usage tracking
The interesting part wasn’t the building — it was watching how people actually interact with agents once everything is in one place.
A few things I’ve noticed so far:
- Most people underestimate how much monitoring and debugging agents matters.
- Users often interact with agents in ways you didn’t design for.
- Simple visibility (logs, actions, usage) changes how you design agents.
Curious how others here are handling this.
If you're building with AI agents:
How do you manage multiple agents and keep track of what they’re doing?
The visibility point is underrated.
Most of the issues aren’t in the agent itself, they’re in how multiple agents interact over time. Without proper logs you’re basically guessing where things went wrong.
It’s similar to any chained system. Everything works in isolation, then breaks in ways that are hard to trace once you connect it.
Thanks for sharing. We really need to think about such management frameworks as AI agents become bigger.
Managing multiple agents usually gets messy because of orchestration and state tracking. Are you coordinating them with a central workflow tool or just letting them run independently?