The question facing every enterprise technology company is simple but urgent: Will you be an essential expert agent that first agents rely on, or will you become a legacy system that agents occasionally query?
The next five years of enterprise software will be shaped by a fundamental shift in how humans interact with technology. We’re moving from a world where people navigate multiple systems to one where conversational agents handle that complexity on our behalf. But the success of this transformation hinges on something most companies haven’t yet grasped: the critical difference between first agents and expert agents.
The Two-Agent Architecture Taking Shape
Enterprise AI adoption is organizing itself around a natural hierarchy. At the top sit what we can call “first agents”: the conversational interfaces that humans interact with directly. Think of these as universal translators between human intent and system execution. They excel at understanding natural language, maintaining context across conversations, and orchestrating workflows across multiple systems.
Below them, a layer of expert agents is emerging. These specialized systems possess deep domain knowledge, proprietary data access, and years of pattern recognition within specific business functions. While first agents know how to have a conversation, expert agents know how to solve problems.
This architecture mirrors how enterprises actually operate. No single person holds all the expertise needed to run a complex business. Leaders rely on specialists who understand the nuances of their domains. The same principle applies to AI systems.
Why First Agents Need Expert Partners
Consider what happens when a sales executive asks their AI assistant about deal pipeline health. The first agent can parse the question, understand the intent, and even access CRM data. But determining whether a deal is truly at risk requires understanding subtle patterns in customer communication, recognizing behavioral signals from past buyer interactions, and interpreting activity data that lives outside traditional systems.
A first agent attempting this analysis alone is like a brilliant generalist trying to perform specialized surgery. They might understand the theory, but they lack the deep pattern recognition that comes from focused experience.
Expert agents bridge this gap. They bring domain-specific intelligence that transforms raw data into contextual understanding. When integrated properly, the combination creates something powerful: conversational accessibility backed by specialized expertise.
The Requirements for Expert Agent Success
Building effective expert agents demands more than just training models on industry data. Several critical capabilities separate truly valuable expert agents from sophisticated chatbots:
Contextual Data Foundation Expert agents must capture and understand the full context of business operations, not just the structured data in systems of record. In revenue operations, this means tracking actual seller activities, understanding communication patterns, and recognizing the difference between what’s reported and what’s really happening.
Behavioral Intelligence Past patterns predict future outcomes, but only if you’ve captured the right patterns. Expert agents need extensive historical data across many organizations to develop reliable behavioral models. A single company’s data tells you about that company; thousands tell you about how markets actually work.
Proactive Reasoning: Valuable expert agents don’t wait for questions. They continuously analyze their domain, surface emerging risks, and identify opportunities that humans might miss. They should push insights to first agents rather than simply responding to queries.
Operational Integration Knowledge without action has limited value. Expert agents must move beyond analysis to enable actual workflow execution. This means deep integration with enterprise systems and the ability to translate insights into specific, executable steps.
The Context Engineering Challenge
The core challenge in building expert agents lies in what we might call context engineering: systematically transforming business activity into actionable intelligence. This involves three interconnected processes:
First, creating comprehensive awareness by gathering data on actual business activities, not just recorded outcomes. Most enterprises have vast blind spots where critical activities occur outside their systems of record.
Second, developing a true understanding by mapping unstructured activity to structured business objectives. This requires sophisticated pattern matching, data enrichment, and the ability to filter signal from noise at scale.
Third, enabling activation by analyzing patterns to identify the most impactful actions and seamlessly initiating those actions through both human and automated channels.
Companies that master context engineering will power the expert agents that make the agentic future actually work.
See also: Taming AI Agent Sprawl in Industrial Organizations
The Competitive Implications of the Expert Agent
As enterprises adopt first agents as their primary interface, software companies face an existential choice. Those who can’t integrate with agent architectures risk becoming invisible to users. When employees prefer working through conversational AI, traditional user interfaces become secondary touchpoints at best.
But integration alone isn’t enough. The real competitive advantage will come from building expert agent capabilities that first agents depend on. This creates a fundamentally different moat than traditional software: you’re not locking in users through habit or switching costs, but through irreplaceable intelligence.
The companies thriving in five years will be those providing specialized intelligence that improves agent decision-making. They’ll own the context engineering for their domains, built on years of pattern recognition and behavioral analysis that new entrants can’t replicate quickly.
See also: Next Role for AI Agents: Recommending and Acting on Real-Time Choices
Building for the Agentic Enterprise
The path forward requires a dual approach. Organizations need to prepare for a world where first agents mediate most user interactions while simultaneously developing or partnering with expert agents for critical business functions.
For technology providers, this means rethinking product architecture around agent integration. Instead of optimizing for human users clicking through interfaces, the focus shifts to providing rich context and executable actions to agent systems.
For enterprises, success requires identifying which domains need specialized intelligence and ensuring their agent architecture can access that expertise. Not every function requires an expert agent, but critical areas like revenue operations, financial planning, and supply chain management certainly do.
See also: Edge AI Is Having a Moment
The Human Element Remains Central
Perhaps counterintuitively, the rise of agent architectures makes human expertise more valuable, not less. Expert agents encode and scale human knowledge, but they don’t replace the need for human judgment, creativity, and relationship building.
The most successful implementations will use agents to eliminate the procedural complexity that keeps humans away from high-value activities. When salespeople can access account intelligence through conversation instead of clicking through five different systems, they spend more time building customer relationships. When financial analysts can query complex models through natural language, they focus on strategic implications rather than spreadsheet mechanics.
The Window of Opportunity
We stand at an inflection point. The foundational technologies for both first agents and expert agents exist today. Early implementations are demonstrating real value. But the full transformation of enterprise operations around agent architectures has barely begun.
For established software companies, the next 18 to 24 months represent a critical window. Those who move quickly to build expert agent capabilities can establish themselves as essential components of the new architecture. Those who wait risk watching newer entrants define the intelligence layer for their industries.
The winners won’t necessarily be those with the best conversational AI. There’ll be those who provide the specialized intelligence that makes conversations valuable. In the agentic future, expertise scaled through technology becomes the ultimate differentiator.
The question facing every enterprise technology company is simple but urgent: Will you be an essential expert agent that first agents rely on, or will you become a legacy system that agents occasionally query? The answer will determine who thrives in the next era of enterprise software.
