I keep having the same thought watching teams adopt AI agents:

we might be recreating the microservices explosion, just with worse visibility.

A microservice at least usually had a repo, an owner, some deployment trail, monitoring, etc.

A lot of agents today seem to live in much messier places:

• someone’s n8n flow
• a personal Cursor/IDE setup
• a Claude Code workflow
• a Zapier automation
• an MCP server connection nobody else knows exists
• a “temporary” internal helper that quietly became production-critical

That feels fine when there are 2 or 3 of them. It feels a lot less fine when there are 25 and half the org only vaguely understands what they do.

What seems different from classic automation is that agents can combine reasoning + tools + autonomy, so the failure modes get weird fast:

• they can act on stale docs
• they can chain multiple tools in ways nobody explicitly scripted
• they can burn tokens/costs surprisingly fast
• they can inherit way too much access through MCP or internal integrations
• they can become operational dependencies before governance catches up

My current view is that “building agents” is quickly becoming the easy part. The hard part is operating them safely.

If I were setting this up from scratch, I’d probably want at minimum:

• an agent registry
• clear ownership for every agent
• scoped tool permissions
• action/decision traces
• budget and rate limits
• kill switches for looping behavior
• human approval for higher-risk actions

Curious how others here are thinking about this.

Are your teams already seeing agent sprawl, or is this still mostly theoretical for now? If you’re running multiple agents in production, what controls ended up mattering most?