2h • General Discussion 💬
Autonomy is increasing. But is your control layer increasing with it?
Anthropic just released new data showing:
– Agents are running longer without intervention
– Experienced users auto-approve more
– Higher-risk domains are emerging (healthcare, finance, security)
– Oversight is shifting from step-by-step approval → real-time supervision
That’s not hype.
That’s a structural shift.
Now zoom out.
Microsoft is bundling agents.
Claude Code is going mainstream.
Copilots are being embedded into everything.
So yes — millions of agents will get deployed.
But here’s the question for operators scaling AI across clients:
Are you building supervision infrastructure?
Or are you just polishing the engine?
Most teams I see are focused on:
– Better prompts/skills
– Faster workflows
– Cleaner n8n stacks
– Smarter orchestration
Very few are asking: –
What happens after 20 deployments?
– What changed between v1 and v4 of this behavior?
– If something drifts quietly, how would we know?
– If a client asks “why did it do that?”, can we prove it?
At small scale, friction hides in the noise.
At scale, it becomes governance.
Not model governance.
Operational governance.
Curious how others are thinking about this:
If you’re deploying agents across multiple clients —
have you formalized behavioral version control yet?
Or are you still in build mode?
p.s The article from anthropic is a good read "Measuring AI agent autonomy in practice"
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Autonomy is increasing. But is your control layer increasing with it?
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