The way we pay for software is broken.
For decades, enterprise software has followed the same playbook: per-seat licensing, feature tiers, and consumption-based pricing that scales with data volume. You pay for access to tools. What you do with those tools—and whether they actually solve your problems—is your concern.
This model made sense when software was a productivity multiplier. Give an engineer better tools, and they become more efficient. But something fundamental has changed.
AI agents don't multiply productivity. They replace work.
And that changes everything about how we should think about pricing.
The Problem with Traditional Software Pricing
Consider how most AIOps and monitoring platforms charge today:
Per-Seat Licensing
- $50-200 per user per month
- More engineers = higher costs
- No correlation between cost and outcomes
Data Volume Pricing
- $X per GB ingested
- Costs scale with infrastructure complexity
- Penalizes comprehensive monitoring
Feature Tiers
- Basic, Pro, Enterprise
- Pay for features you might use
- Upgrade pressure regardless of value delivered
These models share a common assumption: you're paying for capability, not outcomes. The software gives you the ability to do something. Actually doing it—and getting value from it—requires your team's time and expertise.
This creates a perverse dynamic: the more problems you have, the more engineers you need, and the more you pay for software to help those engineers. Your costs scale with your problems, not your solutions.
What AI Agents Actually Do
AI agents fundamentally change this equation. They don't give engineers better tools—they do the work engineers would otherwise do.
Consider what happens when a network incident occurs:
Traditional Approach (with monitoring software):
- Alert fires → Engineer investigates
- Engineer logs into 3-5 systems
- Engineer correlates data manually
- Engineer identifies root cause
- Engineer applies fix
- Engineer documents resolution
Time: 45-90 minutes Cost: Engineer salary + software licenses
With AI Agents:
- Agent detects anomaly
- Agent correlates across all data sources
- Agent identifies root cause
- Agent suggests or executes fix
- Agent documents everything
Time: 4-8 minutes Cost: Agent subscription
The AI agent doesn't help your engineer work faster. It does the work. The engineer reviews, approves, and handles exceptions—but the cognitive labor of detection, correlation, and diagnosis is handled by the agent.
Rethinking Cost: Agents vs. Engineers
Here's a thought experiment. What does it actually cost to have a human handle network operations tasks?
NOC Engineer (L1)
- Salary: $50-60K/year
- Benefits, training, management overhead: ~$4,500/month fully loaded
- Availability: 40 hours/week, minus PTO, sick time, training
- Knowledge: Limited to their experience and training
- Scalability: Linear (more work = more people)
Network Engineer (L2)
- Salary: $75-90K/year
- Fully loaded: ~$7,000/month
- Handles escalations from L1
- Deeper expertise, still limited availability
- High cost when they're handling routine work
Senior Engineer (L3)
- Salary: $100-120K/year
- Fully loaded: ~$9,500/month
- Should focus on architecture, strategy
- Often pulled into escalations
- Opportunity cost of routine work is enormous
Now consider an AI agent:
AI Agent
- Works 24/7/365—no PTO, no sick days, no turnover
- Learns from every incident across your entire operation
- Handles routine work instantly, escalates appropriately
- Consistent quality regardless of time of day
- Knowledge compounds over time
The question isn't "how much does the software cost?" It's "what work does this replace, and what's that work worth?"
The Agent-as-a-Service Model
This is why we think about AI agents differently. Instead of licensing software, you're essentially hiring specialized workers—workers that happen to be AI.
What You're Really Paying For:
When you deploy a Monitoring Agent, you're not buying monitoring software. You're hiring an always-on specialist who:
- Watches every metric, log, and event across your infrastructure
- Understands what "normal" looks like for your specific environment
- Detects anomalies that static thresholds would miss
- Enriches alerts with context before they reach your team
- Never gets distracted, never misses a pattern, never forgets
When you add Operational Agents, you're hiring a team that:
- Triages every incident automatically
- Correlates data across vendors and systems
- Performs root cause analysis in seconds
- Retrieves relevant historical knowledge
- Suggests or executes remediation
The Value Equation:
If these agents handle work that would otherwise require:
- An L1 engineer at $4,500/month
- An L2 engineer at $7,000/month for escalations
- Partial time from L3 engineers
Then the question becomes: what's a fair price for that capability?
Not what's a fair price for "software access." What's a fair price for outcomes delivered?
Why This Model Works Better
For Operations Teams:
Your engineers stop being ticket processors and start being problem solvers. L1 engineers with AI agents can handle work that previously required L2/L3 escalation. Senior engineers focus on architecture and strategy instead of routine troubleshooting.
The agents handle the cognitive grind—the log correlation, the pattern matching, the historical knowledge retrieval. Humans handle judgment, exceptions, and improvement.
For Finance:
Costs become predictable and outcome-linked. Instead of paying for tools and hoping your team delivers value, you're paying for work that gets done. Scaling doesn't require linear headcount growth.
For the Business:
24/7 coverage without night shift premiums. Consistent quality regardless of who's on call. Institutional knowledge that doesn't walk out the door when someone leaves.
New Revenue Opportunities
Here's something traditional software pricing can't offer: AI agents can create new revenue streams.
Consider Specialized Agents that translate network operations into business outcomes:
Energy Agent
- Monitors HVAC, lighting, occupancy patterns
- Identifies energy waste
- Generates efficiency reports and recommendations
- Value to customers: Reduced utility costs, sustainability reporting
Guest Experience Agent
- Monitors WiFi quality proactively
- Detects issues before complaints
- Correlates network performance with satisfaction
- Value to customers: Better reviews, reduced complaints
Security Agent
- Threat monitoring and anomaly detection
- Compliance monitoring and audit trails
- Incident response guidance
- Value to customers: Risk reduction, compliance evidence
These aren't features in a monitoring platform. They're specialized workers delivering specific business outcomes. Your cost for the agent is a fraction of the value you can deliver to customers.
The margin opportunity is enormous because you're not reselling software—you're reselling outcomes.
The Shift in Thinking
Traditional software asks: "What features do you need access to?"
The agent model asks: "What outcomes do you need delivered?"
This isn't just a pricing change. It's a fundamental shift in what you're buying:
| Traditional Software | AI Agents |
|---|---|
| Access to tools | Work completed |
| Capability | Outcomes |
| Features | Results |
| Seats | Capacity |
| Your team does the work | Agents do the work |
What This Means for Your Organization
If you're an MSP or service provider:
AI agents let you scale operations without scaling headcount. You can handle more clients per engineer, reduce expensive escalations, and offer new services built on agent capabilities. Your margins improve as agent costs stay flat while the value you deliver compounds.
If you're an enterprise:
AI agents give you 24/7 coverage without shift differentials. Your senior engineers can focus on strategic work instead of routine incidents. Institutional knowledge gets captured and applied automatically, reducing the impact of turnover.
If you're evaluating solutions:
Stop comparing feature lists. Start comparing outcomes. Ask:
- What work will this actually do for us?
- What would that work cost if humans did it?
- How does this change what our team can focus on?
The Bottom Line
We're at an inflection point. AI agents have crossed the threshold from "tools that help" to "workers that do."
The pricing models of the past—per-seat, per-feature, per-gigabyte—don't capture this. They're designed for software that enhances human work, not software that replaces it.
The organizations that thrive will be those that recognize this shift. That stop buying tools and start hiring agents. That measure value in outcomes delivered, not features accessed.
The question isn't "how much does this software cost?"
The question is "what is this work worth, and who—or what—should do it?"
Key Takeaways:
- AI agents do work, not just enable it — pricing should reflect outcomes, not access
- Cost comparison should be agent vs. engineer — not agent vs. other software
- 24/7 capability without linear headcount growth — agents scale differently than people
- New revenue opportunities — specialized agents deliver business outcomes you can monetize
- The shift: From buying tools to hiring AI workers that deliver results