Agentic AI Is Already Here—Just Not How You Think

Artificial Intelligence has made impressive strides over the past decade; AI-generated music, AI-driven customer support, and even automated content creation are all signs of its growing presence in daily life. But when it comes to "agentic AI," we're just beginning to scratch the surface.

What is Agentic AI?

To understand where we are, we need to define what we mean by "agentic AI." At its core, agentic AI refers to systems that exhibit:

• Agency - The ability to make decisions and take action independently, without constant human supervision or intervention.
• Memory - The capability to retain experiences, learn from them, and adapt behavior over time.
• Autonomy - Enough independence to execute complex, long-term tasks without human prompts for every step.
• Feedback/Improvement Cycles - The ability to self-correct and improve based on outcomes.

The Illusion of Agency

Many current systems labeled as "AI agents" are better described as intelligent automation. They follow a predefined logic, execute workflows, and create outputs based on scripted sequences. This is akin to setting up a chain of dominoes. While the effect may look sophisticated, the dominoes are still falling according to pre-planned arrangements. No true "thinking" or autonomous decision-making is happening. But let’s not dismiss them—they’re incredibly useful. Think of them as programmable co-workers with expanding skill sets.

What’s changing now is that these agents are gaining the ability to interact, adapt, and collaborate within ecosystems. They can execute tasks like booking travel, writing reports, or even helping with compliance audits—tasks that once required several layers of manual coordination.

Why The Wait Is Worthwhile

What businesses actually want is agentic AI, where the agents themselves are more deterministic than probabilistic. With Agentic AI, a robust agentic orchestration (app) layer owns the UI and integrates memory, cross-agent interactions, and feedback-driven learning. The orchestration layer is vested with the "agency" to make decisions based on the feedback from the agents. Importantly, these systems must also ensure reliability, compliance, repeatability, transparency, and security during tasks. A robust orchestration layer will:

• Own the UI and user interactions
• Manage memory and feedback loops
• Integrate specialized agents for specific tasks
• Allow dynamic handoffs and autonomous decision-making
• Enable seamless cross-platform communication

As new challenges and technology arise, new specialized agents can be added. This modular approach allows agentic AI to reach its full potential.

A Note of Caution

Rushing toward agentic AI carries risks. The history of technological development is riddled with cautionary tales of what happens when systems with significant power are released without adequate testing.

Consider the infamous example of Knight Capital Group. A simple, unchecked automated trading algorithm led to financial catastrophe, wiping out nearly $400 million in 45 minutes. This cautionary tale highlights the risks of implementing autonomous systems in high-stakes environments where a single failure could lead to significant problems. The same principle applies to AI agents.

Today, enterprises should rightly be cautious. We do not yet fully understand the boundaries, constraints, and failure modes of AI agents. For critical tasks where accuracy and safety are paramount, businesses need time to test agents in controlled environments before deploying them more widely. But there are still many use cases where AI Agents can be deployed safely today.

Current Use Cases for AI Agents

While we may not yet have full-fledged agentic AI, agent technology is already useful in several areas, particularly in proofs of concept and low-risk tools:

• Customer Support: Tools like ChatGPT and dynamic knowledge bases can enhance self-service experiences without endangering business-critical systems.
• Routine Analytical Tasks: AI agents streamline data analysis, offering descriptive insights while remaining within clearly defined instructions.
• Non-Critical Automation: AI and agents are thriving in areas where being "wrong" is not fatal, such as recommendation engines or email drafting.

These proof-of-concept agents provide a testing ground for what may eventually evolve into true agency.

The Path Forward for Agentic AI

The future of agentic AI isn't about a single, all-powerful model. It's about building modular agent ecosystems where specialized agents communicate through emerging standards like MCP (Model Context Protocol) and A2A (Agent-to-Agent) interoperability.

MCP establishes industry-standard norms for how agents communicate with models and external systems. A2A focuses on the groundwork for open, secure, and structured agent to agent communication across platforms. With these protocols, agents can interoperate with one another regardless of their underlying architecture or ecosystem — a foundational shift that will accelerate enterprise-grade agency.

With this architecture, organizations can deploy app store-like libraries of task-specific agents that operate seamlessly together. This isn't sci-fi; it's happening now in early-stage platforms like agent.ai and initiatives built on A2A and MCP

Best Practices for Enterprises Today:

1. Start Small - Begin with pilot projects that use agents in low-risk, non-critical areas.
2. Prioritize Security - Address vulnerabilities aggressively, as tools like “operator” and "Computer use" currently present significant security holes.
3. Understand LLM Challenges - Get comfortable with the quirks of large language models (LLMs), like sampling variability,
4. Start Specialized LLM development – Create LLMs with business specific data to use in expert agents
5. Invest in Interoperability - Agents need to talk to each other. Adopt tools and standards like A2A and MCP to ensure cross-agent and cross-platform functionality.

By mastering these foundational practices, enterprises can position themselves to adopt truly agentic AI when the time is right.

Looking Ahead: Agency as a Platform

The next chapter of AI isn’t just smarter tools—it’s orchestrated agency. Think of a future where your enterprise has a fleet of agents working collaboratively: an HR agent for onboarding, a finance agent for forecasting, a legal agent for contract reviews. They operate with autonomy, communicate via shared protocols, and evolve through continuous feedback.

Are we all the way there yet? Not quite. But the pieces are in place—and agency is on the horizon.

We’re not just imagining the future. We’re building it

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