Busting myths about agentic AI in the enterprise

I'm sure you've seen it by now—hot takes from just about everyone in tech about how AI agents are “revolutionizing everything.” They’re not wrong, but they’re not exactly right either. This isn’t magic. This is the result of serious advancements in generative AI, careful orchestration, and years of hard-won lessons in enterprise data architecture. We’re not talking about science fiction here—we’re talking about practical, powerful systems that can finally scale to enterprise needs.

Let’s unpack what’s really happening, what’s just hype, and why Cohesity is uniquely positioned to bring this paradigm safely, securely, and responsibly into the enterprise.

What agentic AI isn’t (Let’s bust the myth)

Let’s be clear upfront: agentic systems aren’t self-aware overlords. They’re not digital employees who just show up, grab a coffee, and start making business decisions. They’re software. Sophisticated? Absolutely. Autonomous? To a point. But they still operate within the guardrails set by human developers.

Take that famous 2023 demo where AI “agents populated a virtual town.” Under the hood? It was ChatGPT running character scripts. That’s clever orchestration, not autonomy. Today, real-world agentic AI is more like a junior analyst with a toolkit—it knows how to plan, fetch data, make API calls, and iterate, but it’s not steering the ship.

Think: Thought → Action → Feedback loop.

Powerful? Yes. Magical? No.

What agentic AI is (The real value)

Agentic AI is about goal-driven autonomy. You tell the system what you want—“Help me prep the quarterly business review”—and it figures out how to get there: pulling data, running forecasts, summarizing documents, even calling APIs or scripts. It’s like giving a smart intern access to your tools and watching them get to work. Only this intern doesn’t sleep, scales across departments, and always cites its sources.

That’s where it gets interesting. Because when paired with high-quality data and governed access, agents don’t just do tasks—they accelerate workflows. How? They cut through tribal knowledge silos. They onboard new team members faster. And they help your teams make smarter, faster decisions. But they only work if you’ve done the hard work first: consolidating your data and putting real governance in place.

What the hyperscalers are doing

Let’s talk about the big three—Microsoft, AWS, and Google—and how they’re shaping this landscape:

• Microsoft Azure: Their Semantic Kernel SDK and Azure AI Agent Service make it easier to build and orchestrate agents that interact with Microsoft 365 data. It’s tightly integrated and ready to scale.
• AWS: With the Strands Agent SDK, AWS makes agent deployment a push-button experience. It supports multi-LLM pipelines and integrates deeply into Bedrock and its Knowledge Base. It’s enterprise-grade from day one.
• Google Cloud: With the Vertex AI Agent Engine and ADK toolkit, Google is all-in on scalable, interoperable agents. Their Gemini models are optimized for these use cases, and they’re pushing open standards like the A2A protocol.

What else powers this ecosystem

Beyond the hyperscalers, you’ve got LangChain, LlamaIndex, and NVIDIA laying down the blueprints for what agentic AI can really do in the enterprise:

• LangChain: The go-to framework for orchestrating agents, now with LangGraph for multi-agent flows and LangSmith for observability.
• LlamaIndex: A critical layer for connecting agents to your data via RAG—the retrieval part of RAG is everything when you care about grounding answers in facts.
• NVIDIA: Their NIM microservices and NeMo Retriever stack are all about high-performance, GPU-optimized agents. Launchables make it easy to deploy, experiment, and scale.

Advice for enterprises getting started

Let’s cut through the noise. If you want to actually get value from agentic AI, here’s where to focus:

1. Get your data house in order. Garbage in, garbage out. High-quality, centralized, accessible data is non-negotiable.
2. Enforce governance from day one. Role-based access control, logging, and audit trails aren’t optional. They’re the foundation for responsible AI.
3. Tear down the silos. Unified data means agents don’t get stuck. It means faster insights, easier compliance, and better outcomes.

Where Cohesity fits in

This is where the story comes full circle. If agentic AI is the next-gen digital workforce, then Cohesity is the secure knowledge base it needs to operate. Here’s why:

• Unified data access: Our platform, Cohesity Data Cloud, brings together backups, cloud data, and application data into a single, indexed source of truth. That means agents can access everything they need—without jumping through hoops.
• Built-in governance: We enforce security and immutability by default. Fine-grained role-based controls, auditability, data masking—all built in. No shortcuts.
• Cohesity Gaia—The Agent Accelerator: With Cohesity Gaia, you can spin up RAG pipelines with push-button simplicity. Create datasets, enforce RBAC, and generate grounded responses with citation and context—all using the data you already manage.
• Platform. Insights. Ecosystem. This isn’t just a feature. Cohesity’s vision of Cohesity AI spans our platform intelligence, insights layer (hello Gaia), and our partner ecosystem—including integrations with NVIDIA, AWS, and beyond.

The bottom line

Agentic AI is here, and the enterprises who are ready will pull ahead. But success isn’t just about the agent—it’s about the data foundation underneath it. That’s why Cohesity matters.

So, when your board asks how you’re using Gen AI to drive value, you can say: “We’ve already got the foundation. Now we’re bringing the AI to the data—safely, securely, and responsibly.”

Let’s build wisely.