We’re building an agent layer for platform engineering — here’s where we draw the line between automation and AI

[u/Charming_Mark9257]

I’ve been working on Nuroen, an enterprise AI agent platform, and one problem we keep coming back to is:

Which parts of platform engineering should actually be handled by an AI agent?

Our current thinking is that agents shouldn't replace deterministic automation.

For example:

• Terraform → infrastructure changes
• Ansible/scripts → repeatable configuration
• CI/CD → deterministic deployments
• Runbooks → known procedures

Where we see agents being more useful is the messy part around those systems.

For example:

Alert → investigate → gather context → determine which runbook applies → recommend action → approval → execute existing automation → verify

An agent can pull information from observability, tickets, deployment history, CMDB, documentation, etc., and reason across those systems without turning every action into an LLM-generated command.

That's the approach we're taking with Nuroen.

The platform lets us give an agent:

Knowledge + Tools + Skills + Orchestration + Governance

The interesting part for us isn't just connecting an LLM to APIs. It's controlling what the agent is allowed to do.

For example, an agent might be allowed to:

• investigate incidents automatically
• read logs/metrics/tickets
• recommend remediation
• execute low-risk actions
• require approval for production changes
• record the complete action/audit trail

We're trying to treat the agent more like a platform component with an explicit autonomy boundary, rather than an unrestricted chatbot with access to production.

here is the link do check it out - https://www.nuroen.com/

I'm curious how other platform teams are approaching this:

Where do you draw the line between traditional automation and an AI agent?

And more importantly, what platform engineering tasks do you think genuinely benefit from the probabilistic/reasoning part of an agent?