The shift from AI assistants to AI agents
Much of the discussion surrounding artificial intelligence has focused on large language models, chatbots, and content generation.
While these technologies have captured headlines, a more significant shift is underway. Software vendors are increasingly moving beyond AI assistants and toward AI agents.
Unlike traditional AI tools that simply answer questions, AI agents are designed to perform work. They can retrieve information, access systems, execute workflows, interact with applications, and complete multi-step tasks with minimal human involvement.
For many organizations, AI agents may eventually become the most transformative aspect of enterprise AI. They may also create an entirely new category of software licensing, governance, and compliance challenges.
What is an AI agent?
The term "AI agent" is often used loosely, creating confusion.
An AI assistant generally responds to requests. An AI agent takes action.
For example, an AI assistant might answer: "What software licenses expire this year?"
An AI agent might:
- Identify expiring licenses
- Retrieve contract records
- Analyze renewal terms
- Create a renewal recommendation
- Open a procurement ticket
- Schedule stakeholder meetings
- Draft vendor communications
without requiring separate instructions for each step.
The key distinction is autonomy. Agents are designed to achieve objectives rather than simply generate responses.
Why vendors are investing so heavily in agents
Every major software vendor is investing aggressively in agent technology:
- Salesforce Agentforce
- Microsoft Copilot Agents
- Oracle AI Agents
- ServiceNow AI Agents
- SAP Joule Agents
- Workday Agentic AI
- Google Agentic Data Platforms
The reason is straightforward. Organizations do not ultimately want AI that merely provides information. They want AI that performs work.
Vendors view agents as the next evolution of enterprise software because agents can automate increasingly complex business processes.
The challenge is that agents introduce entirely new questions about system access, software consumption, and licensing accountability.
The user model is changing
For decades, enterprise software licensing was built around human users. Organizations counted:
- Named Users
- Authorized Users
- Employees
- Concurrent Users
- Device Access
The licensing model assumed that people were the primary consumers of software.
AI agents challenge that assumption.
Consider a scenario where 500 procurement employees use a platform and 2,000 AI agents operate within that same environment.
- Who is the user?
- Who consumes the license?
- Who is responsible for the activity generated by those agents?
Many organizations do not yet have clear answers. In many cases, vendors do not either.
Agents create new forms of consumption
Traditional users generate predictable activity. AI agents do not.
An employee may log into a system a few times per day. An AI agent may operate continuously.
An agent might query databases, retrieve documents, generate reports, update records, execute workflows, and access APIs thousands of times per day.
Even when no additional licenses are required, agent activity can drive significant increases in:
- Database utilization
- Cloud consumption
- Storage requirements
- API usage
- AI service charges
Organizations evaluating AI initiatives should consider not only how many users exist, but how many autonomous processes may eventually be operating within their environments.
The rise of agent-to-agent activity
Most discussions focus on humans interacting with AI. The longer-term trend may be agents interacting with other agents.
For example:
- A procurement agent identifies an upcoming renewal.
- That agent communicates with a contract analysis agent.
- The contract analysis agent communicates with a pricing intelligence agent.
- The pricing intelligence agent retrieves information from a software asset management platform.
Multiple systems may be interacting continuously without direct human involvement.
This creates a new layer of software activity that traditional licensing models were never designed to address.
Who owns the agent?
This question may seem simple. It is not.
Consider a procurement platform. An employee launches an AI workflow. The workflow accesses ERP systems, contract repositories, CRM platforms, document management systems, and financial applications.
The agent may touch numerous licensed products. Questions quickly emerge:
- Which user credentials apply?
- Which entitlements govern access?
- What permissions exist?
- Which contracts control usage?
As agents become more capable, organizations will need clearer governance frameworks to define accountability.
AI agents increase audit visibility
One of the least discussed aspects of AI adoption is telemetry.
Modern enterprise platforms increasingly record user activity, feature utilization, API consumption, AI interactions, and agent actions.
This information provides valuable operational insights. It also provides software vendors with unprecedented visibility into how their products are being used.
Historically, software audits often relied on inventory reviews, configuration analysis, and deployment records.
Future compliance reviews may include:
- AI feature utilization
- Agent activity logs
- API consumption records
- AI service usage metrics
Organizations should expect software vendors to become increasingly sophisticated in their ability to measure product utilization.
The emerging governance challenge
Many organizations are developing AI governance programs. Most focus on data privacy, security, responsible AI, and model accuracy.
These concerns are important. However, governance discussions often overlook operational and contractual considerations.
Questions organizations should begin asking include:
- What agents exist?
- What systems can they access?
- What activities can they perform?
- What software services do they consume?
- What costs do they generate?
- What contractual obligations apply?
Without clear visibility, organizations may struggle to understand the full impact of agent adoption.
What this means for software asset management
Historically, software asset management focused on licenses, deployments, entitlements, and compliance positions.
AI introduces a new layer of complexity. Organizations increasingly need visibility into:
- AI-enabled products
- Agent deployments
- Consumption metrics
- AI service utilization
- Contractual rights
- Vendor-specific AI terms
This does not replace traditional software asset management. It expands it.
The organizations that adapt early will be better positioned to manage both cost and risk.
Questions organizations should ask today
Before deploying AI agents at scale, technology leaders should understand:
What AI agents currently exist in our environment? Many organizations already have agent functionality available through existing software platforms.
What systems can those agents access? Understanding permissions is essential for governance.
What costs can those agents generate? Consumption often scales faster than expected.
What telemetry is being created? Visibility is increasingly becoming a vendor advantage.
What contractual rights govern agent activity? Many AI-related licensing terms remain new and poorly understood.
The bottom line
AI agents represent far more than another software feature. They represent a fundamental shift in how enterprise systems operate.
For decades, software licensing and governance revolved around human users. The next generation of enterprise software may increasingly revolve around autonomous agents acting on behalf of those users.
Organizations focused solely on AI productivity gains may overlook important questions surrounding cost, licensing, compliance, and governance.
The technology is advancing rapidly. The policies governing it are not.
The organizations that establish visibility and control early will be far better prepared as agent-driven computing becomes a standard part of enterprise operations.
