Agentic AI: The Future of Automation

I've spent most of my career automating things that humans didn't want to do anymore: provisioning infrastructure, reconciling data, chasing approvals, cleaning up after systems that behaved exactly as designed — just not as intended.

Lately, the conversation has shifted from automation to agents. Not chatbots. Not copilots. Actual software entities that can decide what to do next, execute actions, observe the result, and adjust.

In enterprise environments, agentic AI isn't about intelligence.
It's about control.

Why traditional automation is hitting a wall

I've built plenty of classic automation stacks: Azure Functions triggered by Event Grid, Logic Apps glued to Service Bus, Terraform pipelines enforcing guardrails via policy-as-code. This model works when the world is predictable.

The problem is that enterprise environments stopped being predictable a long time ago:

• Approval flows change mid-quarter
• Data contracts drift
• APIs behave differently across regions
• “Just this once” exceptions become permanent

Traditional automation assumes the happy path is stable. In my experience, it never is. Teams respond by layering more conditions, more branching, more YAML — until the automation is harder to reason about than the manual process it replaced.

That's the wall.

What makes an agent different in practice

An agent isn't just a script with better NLP. The useful distinction is that an agent owns a goal, not a flow.

One simple objective I've implemented: “ensure this Azure subscription remains compliant with internal cost and security policy.”
Not “run this check every night.” Not “execute steps A through F.” Just the outcome.

The best systems I've seen follow a simple loop:

• Observe: inspect state (e.g., Azure Resource Graph, policy compliance, Cost Management exports)
• Decide: choose the smallest safe action that moves toward the goal
• Act: create a PR, run an approved remediation, open a ticket
• Explain: produce decision logs humans can audit

That decision point is the key difference. You stop designing workflows and start designing constraints.

Where this actually works today

Let's be clear: I wouldn't deploy agentic AI to core transaction systems or anything that touches regulated data flows directly. Not yet.

Where I've seen it work is in what I call the “gray zones” of enterprise IT — areas that are operationally critical but already semi-manual.

Cloud cost optimization is a good example. In one setup, we used an agent to analyze daily Cost Management exports from a Storage Account, correlate them with tagging quality and usage patterns, and propose actions: rightsizing, schedule changes, SKU downgrades.

The agent didn't execute blindly. For resources under a certain spend threshold, it could act directly. Above that threshold, it produced a change plan with justification. Humans stayed in the loop — but the cognitive load dropped dramatically.

This approach beat static FinOps rules every time. Not because the agent was smarter, but because it adapted when reality didn't match assumptions. If you want to build this in a way your finance team can live with, start here: FinOps & cost governance.

Governance is the real design problem

Most agent demos ignore governance. In enterprise — and especially in EU contexts — that's a non-starter.

Every production-worthy agent I've worked with had:

• A constrained identity: Managed Identity with narrowly scoped roles
• No wildcard permissions: no owner, no “*”, no broad contributor rights
• Action logging: correlation IDs and audit trails an auditor can follow
• Clear boundaries: when to create a PR vs when to open a ticket

In one public-sector setup, data residency requirements ruled out hosted LLM endpoints. The agent ran against an Azure OpenAI deployment pinned to an EU region, with no data retention and explicit approval from legal. That constraint shaped the architecture more than the model did.

If you can't explain to an auditor why an agent took an action, you shouldn't deploy it. This is why agentic systems almost always need security & governance designed first — not bolted on later.

The uncomfortable truth about autonomy

Fully autonomous agents are usually a bad idea in large organizations. I've seen teams push autonomy too far, too fast. The result wasn't innovation — it was incident reviews, finger-pointing, and emergency kill switches.

Enterprises don't fail because systems can't act.
They fail because nobody knows who's accountable when something goes wrong.

The pattern that actually survives is graduated autonomy:

Cost and scale realities

Agentic systems aren't cheap — not in compute, and not in thinking time. Running agents that continuously reason over state (especially with GPT-4 class models) adds up fast. We had to cap reasoning cycles, batch evaluations, and fall back to deterministic logic when confidence thresholds weren't met.

In one case, we deliberately accepted lower-quality decisions during peak hours to stay within budget. That tradeoff was explicit, documented, and approved. That's how these systems get accepted.

What I'd do differently next time

If I were starting again, I'd invest less in agent frameworks and more in observability: clear state models, explicit decision logs, replayable actions.

Agents don't fail silently. They fail creatively.
When they do, you need forensic visibility — not just metrics.

The other lesson is cultural. Teams need to stop thinking in flows and start thinking in intent and boundaries. That shift is harder than adopting a new SDK.

The pattern that's emerging

Agentic AI isn't replacing automation. It's sitting above it — deciding when and how to apply the boring, reliable primitives: Terraform, Bicep, CLI scripts, and APIs.

What changes is who's driving.
I don't think the future is swarms of autonomous agents running wild. The future is fewer humans doing better work because the system understands the messiness we've been pretending doesn't exist.