AI
AI Agents are a new Actor that will reshape how software is designed.
A fundamental shift in system architecture is already underway. According to Gartner, over 80% of enterprises will have used or deployed GenAI by 2026. What this means is that we're experiencing the emergence of a 3rd Actor in enterprise applications:
AI agents aren't just another user type to accommodate. They represent an entirely new category of Actor. This shift demands that we rethink how we design, build, and scale enterprise systems.
The traditional two-actor model that has governed system design for decades is being rapidly expanded into a three-actor model.
For as long as we've been building enterprise systems, we've recognized two fundamental types of actors:
Humans interact through user interfaces. Whether it's a graphical interface on your laptop, a voice interface with Siri, or a mobile app on your phone, humans need presentation layers designed for their cognitive and sensory capabilities.
Systems communicate through APIs. When your CRM needs to talk to your payment processor, or your inventory system needs to update your e-commerce platform, they use structured protocols designed for machine-to-machine communication.
This binary model has served us well. It's clean, logical, and maps perfectly to how we've traditionally thought about system interactions. But AI agents don't fit neatly into either category.
Consider what happens when an AI agent needs to interact with your system. It has the automation capabilities of a system actor, operating 24/7 without fatigue, processing vast amounts of data, and executing complex workflows. Yet it communicates using natural language, understanding context and nuance like a human actor.
Current solutions force AI agents into one of these existing categories. Computer Use technologies have agents clicking through GUIs designed for humans. Protocol layers like MCP (Model Context Protocol) try to standardize agent-to-system communication. Our bet is that these are temporary solutions to bridge AI with existing interaction modes.
The unique nature of AI agents becomes clear when you examine their operational characteristics:
Unlike humans, agents can:
Unlike traditional systems, agents can:
Not all of the properties above are present in AI today, but these properties are expected to emerge with time.
This combination creates requirements that neither human-centric UIs nor system-centric APIs can efficiently address.
Today's agent communication methods reveal our struggle to adapt:
Computer Use allows agents to interact with existing GUIs by simulating mouse clicks, keyboard input, and screen reading. It's a hard problem that requires advanced engineering, but it's also inefficient. Every interaction is resource-intensive, error-prone, and scales poorly.
Model Context Protocol (MCP) attempts to create a standardized way for agents to access context and tools. It's a step in the right direction, acknowledging that agents need something different from traditional APIs.
These transitional technologies serve a purpose. They allow us to integrate agents into existing architectures without massive rewrites. But they're not optimal long-term solutions.
As agent usage grows relative to human usage, the inefficiencies become untenable. Imagine a future where 70% of your system interactions come from agents, but they're all clicking through GUIs designed for humans. The computational overhead alone would cripple your infrastructure.
The question isn't whether AI agents will become a dominant force in system interactions. The data already shows explosive growth in agent adoption across industries.
Organizations that recognize agents as a distinct third actor are building competitive advantages today. They're creating systems that leverage the unique capabilities of agents rather than forcing them into ill-fitting interfaces. They're preparing for a future where the majority of system interactions might come from non-human actors.
The traditional two-actor model served us well in an era of human users and deterministic systems. But that era is ending, rapidly. The three-actor model is a fundamental shift in how we think about system design and automation.
The organizations that thrive in the agent era will be those that fundamentally rethink their system design around three distinct actors: humans, systems, and agents. If you're ready to explore how to transform your business, the team at Sentrix Labs specializes in designing and implementing agents that drive measurable business value.