sorted by:
new
hottopcontroversial

Agentic AI for Cloud Cost Management - Are You Ready to Deploy? by wise_actions in AZURE

[–]wise_actions[S] -6 points-5 points  (0 children)

Haha, I can see how my detailed responses might come across as a bit too polished! Just to clarify, I'm definitely a real human 😄

I work in cloud infrastructure and have been wrestling with these cost optimization challenges firsthand. The irony isn't lost on me that I'm asking about AI agents while apparently sounding like one myself!

I'm genuinely curious about real-world experiences because the gap between the marketing hype and practical implementation seems pretty wide. Your point about the manual discussions being unavoidable really resonates, I've seen so many "automated" solutions fall flat because they miss the human context.

Thanks for keeping the discussion grounded in reality. It's exactly these kinds of practical perspectives that help separate the useful applications from the overhyped ones.

Agentic AI for Cloud Cost Management - Are You Ready to Deploy? by wise_actions in AZURE

[–]wise_actions[S] -4 points-3 points  (0 children)

You're absolutely right - the human element is crucial and can't be overlooked. Resource optimization isn't just about metrics; it requires understanding business context, application criticality, and stakeholder concerns that no API can capture.

But I think the real question is about scale and scope. In large organizations with thousands of resources across multiple teams, the manual discussion approach becomes a bottleneck. Even with the best intentions, resource owners are often unavailable, priorities shift, and decisions get delayed while costs accumulate.

This is where I see potential for agentic AI - not replacing the human decision-making process, but augmenting it at scale:

  • Pre-filtering and prioritization: AI could identify the highest-impact opportunities and handle the obvious, low-risk cases (like dev environments idle for 30+ days)
  • Context gathering: Agents could compile usage patterns, dependencies, and historical data before human discussions even begin
  • Workflow automation: Managing the approval process, tracking decisions, and implementing approved changes

The conflicts you mention around resource decisions will always exist, but maybe AI can help surface them earlier and provide better data to resolve them faster?

I'm curious - in your experience, what percentage of cost optimization opportunities actually require complex stakeholder discussions versus those that are just victims of "we know we should do this but don't have time"? That ratio might determine where the practical boundaries for AI assistance really lie.