What Are AI Agents? The Autonomous Systems Reshaping Enterprise Computing

Beyond Chatbots: Understanding AI Agents

AI agents are autonomous software systems that can perceive their environment, make decisions, and take actions to achieve specific goals, all without constant human supervision. Unlike traditional software that follows predetermined paths, or even generative AI that responds to prompts, agents operate with genuine autonomy.

The agent then determines what needs to be done, breaks down the problem, accesses necessary tools and data, and delivers the complete solution.

The Four Pillars of Agent Computing

What makes AI agents fundamentally different from traditional applications? Four key characteristics:

4. Network Centric Architecture

Agents are inherently collaborative. They communicate with:

• Large Language Models (LLMs) for reasoning and natural language processing
• Other agents for task delegation and specialized expertise
• Databases and knowledge bases for information retrieval
• External APIs and services for extended capabilities

How Agents Actually Work

Let us trace through a real scenario to understand agent operations.

Each step involves autonomous decision making. The agent determines which databases to query, what information is relevant, which specialized agents to involve, and how to present the final response, all without explicit programming for this specific scenario.

The Protocol Revolution: MCP, A2A, and ACP

The Enterprise Impact

For businesses, agents represent both an opportunity and a challenge.

Used correctly, agents can compress multi step human workflows into a single request and response. They become always on operators sitting across support, finance, engineering, and operations.

Traditional monitoring and access control systems were built for predictable applications and human users. Agents cut across both.

Why Agents Are Different from Any Identity That Exists So Far

Traditional enterprise systems deal with predictable entities. Users have defined roles, applications have fixed behaviors, and services follow predetermined patterns. Security and governance frameworks are built around these assumptions.

Agents shatter those assumptions.

They are not users, they are not applications, and they are not services. They are something entirely new: adaptive, autonomous entities that blur the boundaries between all three.

They operate in a gray zone that traditional security and governance frameworks were not designed to handle. Even when every MCP tool is individually well scoped and monitored, the way agents chain these together creates a new, emergent attack and governance surface.

This is not just an evolution of existing computing paradigms. It is a shift that demands fundamentally new approaches to visibility, control, and trust.

In our next post, we will dive deep into why agents represent a completely new class of identity and the unique security and governance challenges they present. We will explore how their ephemeral nature, emergent behaviors, and ability to traverse traditional security boundaries make them unlike anything enterprise IT has encountered before.

About the authors

Apurv is the co founder of Aurva and a former AI engineering leader at Meta. He combines deep experience in building large scale AI systems with hands on security work at Aurva, and is passionate about bridging the gap between data and AI teams and security teams.

Basuraj is Head of AI Products at Uniphore and an AI strategist focused on applied machine intelligence. He has spent the last decade building conversational AI systems, leading AI engineering teams, and shaping product strategy across high-growth startups and enterprise platforms.

Aurva’s AIOStack provides visibility into agent operations using eBPF technology to monitor AI activities at the kernel level, helping enterprises understand what their agents are doing, which data they touch, and how they interact with the rest of the environment.

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