Most enterprises worried about agentic artificial intelligence (AI) haven't even looked at how many agents are already running inside the business. The answer is usually more than they expected.
Key Takeaways
- Gartner predicts up to 40% of enterprise applications will include integrated task-specific AI agents by the end of 2026. That’s a significant increase from less than 5% in 2025.
- Deloitte found nearly three-quarters of companies plan to deploy agentic AI within two years, but only 21% of them have a mature model for agent governance.
- Gartner also predicts over 40% of agentic AI projects will be canceled by the end of 2027 because of escalating costs, unclear business value, or inadequate risk controls.
The Agent Footprint Most Enterprises Don’t Know About
Gartner’s projected increase in agent-enabled enterprise applications shows how quickly agents are moving from experiments into everyday workflows. But embedded agents are only part of the picture. Developers build them to solve specific problems. Product teams add them to existing processes. Power users create automated pipelines through no-code tools. Each path can move faster than governance can respond, especially when teams don’t have to open a formal procurement or approval request.
In Larridin enterprise audits, the average first scan finds 47 orphaned agents with no current owner. That isn’t an edge case. It’s what happens when organizations deploy AI at speed without infrastructure to track who owns each agent, what it touches, what it costs, and whether it still needs to run. Agent expenses can rack up before finance has a chance to intervene.
Why Agents Are Different from Regular AI Tools
When an employee uses a generative AI tool, the interaction starts and ends with a person. Agents remove that checkpoint.
A well-designed invoice processing agent might receive an invoice, classify it, cross-reference it against a purchase order, route it for payment, and log the transaction without a human approving each step. That’s the value proposition. It’s also why governance works differently.
If an employee makes a bad AI-assisted decision, the blast radius is usually limited to that decision. If an agent makes a classification error, it can repeat that error across every transaction it processes until someone catches it. The risk scales with the agent’s activity level, not with the number of people watching it.
The Four Governance Gaps That Matter Most
1. No Complete Agent Inventory
Most organizations can’t tell you how many agents are active, which systems they touch, what data they access, or how much they cost. This is a direct extension of the broader shadow AI problem, but the stakes are higher because agents can act, not just answer.
2. Human and Agent Spend Mixed Together
When agent spend is folded into general AI tool costs, there’s no way to see what agent activity actually costs. A high-volume agent running overnight can spike costs before finance sees a meaningful pattern. Token Spend & Insights separates human-driven and agent-driven spend so each can be evaluated independently.
3. No Outcome Attribution
An agent is only worth the value it produces. Without connecting agent activity to measurable outcomes, leaders can’t tell whether the agent is saving time, creating revenue, reducing risk, or just consuming budget. AI Impact makes that connection.
4. No Early Warning on Cost Overruns
Agent costs are based on consumption, which makes them harder to predict than traditional software expenses. Without projected overage alerts, the first sign of a problem may be the bill.
What Agent Governance Requires
Effective agent governance needs a real-time operating picture, not a static inventory. That includes:
- A continuously updated inventory of all agents running across the enterprise, including agents embedded in Software as a Service (SaaS) platforms and agents built by individual teams
- Separation of human and agent spend so each can be tracked, attributed, and evaluated independently
- Attribution of every agent to an owner, a use case, and a budget line
- Real-time overage alerts before agent costs exceed plan
- Outcome data that connects agent activity to the business metrics it’s supposed to move
Frequently Asked Questions
What is agentic AI and why does it require different governance?
Agentic AI refers to autonomous systems that take actions and make decisions within workflows without human approval at every step. Governance needs to be different because problems scale with the agent’s activity level instead of staying contained to individual human decisions.
How do I know how many AI agents are running in my organization?
Most organizations can’t answer this accurately without automated discovery. Agents are deployed across many platforms, by many teams, and through many mechanisms. Larridin surfaces agent activity across the enterprise as part of the broader AI tool discovery process, including agents that were never reported to information technology (IT).
What is an orphaned AI agent?
An orphaned agent is an autonomous AI system that’s actively running and consuming resources with no current owner. It often happens when a project ends or a team member leaves and the agent is never formally decommissioned. Larridin’s initial scans find an average of 47 orphaned agents per enterprise.
How does agent spend differ from regular AI tool spend?
Regular AI tool spend is generally tied to seat licenses or human-initiated model usage. Agent spend is consumption-based and scales with how much the agent does. Agents running around the clock on high-volume workflows can generate costs that don’t look like a typical software line item.
Get Visibility Into Your Agent Footprint
Most agent inventories are incomplete by the time finance or IT reviews them. Larridin surfaces your complete agent footprint, separates agent spend from human AI use, and connects both to the outcomes they produce.
Book a discovery call to see your agent footprint.