The race to deploy AI agents is on. Companies are rapidly rolling out Model Context Protocol (MCP) servers, building agentic workflows, and offering AI-powered services to their customers. These agents are calling APIs, accessing databases, and integrating with external services at unprecedented scale. But there's a critical gap in how we're approaching agent deployment: while building the infrastructure, we're neglecting proper entitlement management.
While the industry has focused heavily on authentication and authorization for AI agents, we've largely ignored the entitlement layer, the system that governs what agents can access, how much they can consume, and when those permissions expire. Companies are deploying MCPs and agentic services without proper guardrails, essentially handing over the keys to the kingdom without spending limits or automatic shutoffs.
This oversight is creating significant business risks. Traditional identity and access management systems simply weren't designed to handle the unique challenges of agent-scale operations, leaving organizations exposed to runaway costs, resource exhaustion, and accountability gaps.
Understanding Entitlements
Before diving into the challenges, let's clarify what we mean by entitlements. While authentication answers "who are you?" and authorization answers "what can you do?", entitlements answer "how much can you consume?" and "under what conditions?"
Entitlements are business rules governing resource consumption, such as storage quotas, feature access, and usage-based metering. They determine usage limits and costs, often tied to subscription plans and enforced via counters or time-based resets.
For human users, this model works reasonably well. But when AI agents enter the picture, consuming resources at machine scale and operating on behalf of multiple stakeholders, traditional entitlement models break down completely.
The Current State: "Just Say Yes" Access Control
Today's agent access control often looks like a series of permission dialogs. When using an MCP client like Claude or Cursor, when the agent wants to trigger a tool, it may ask for the user’s explicit permission before running it, though not always. After several of these prompts, most users will simply end up granting full access, just to get the job done and save time without thinking about the possible repercussions. This "trust and pray" approach sometimes works, but it can also backfire, with the agent burning through your OpenAI credit budget, getting stuck in a loop and making 50,000 API calls, or other unintentional side-effects, especially when you lack observability into what agent used which resource..
This reactive permissions approach is a significant and growing business risk as agent adoption accelerates.
Why Traditional Entitlement Management Falls Short
To understand why we need a new approach, let's examine the fundamental differences between human and AI agent access patterns:
For every issue, different solutions are required. Some are handled on the authentication and authorization side, and others are the responsibility of the entitlement management system.
Existing flows are pretty straightforward - users authenticate via OAuth or an API key, get authorized based on the role or attribute, and then have subscription based entitlement metered or boolean access to the right features.
As we evolve, and allow agentic access on behalf of the user, this entire flow changes. We’ll need to leverage delegated OAuth tokens for authentication, derive the access based on both parent user permissions as well as the task scope, and then have the entitlement management system allocate the required quota for the task.
- Authentication: instead of sharing personal API keys with agents, we need delegated OAuth tokens that maintain a clear audit trail. When an agent authenticates, the credentials should trace back to a human or service account while providing additional context about the session.
- Authorization: becomes contextual - we don’t want to just grant the agent access to all resources the user has access to. It needs to be contextual, based on the session information and agent intention.
- Entitlements: instead of unlimited access to a user's quota, we need allocated entitlements. If a user has a 1000 API calls per month entitlement, he might want to allocate just 200 of those for this specific agentic session.
Your entitlement management system should act as an expense management for compute resources, allocating and de-allocating quotas as needed.
The Evolution Required
To support agent-scale operations, entitlement management systems need to evolve across four key areas:
- High-Throughput Infrastructure: as agents make thousands of calls per minute, your entitlement system must ingest usage, aggregate it, and gate access within milliseconds to avoid bottlenecks.
- Context-Aware Metering: traditional usage tracking with simple counters isn't enough. We need a system that supports ingesting events with multiple dimensions, capturing what agent performed an action, during which session, and on behalf of which user. This will enable specific agent access gating, as well as observability into “spend management”, allowing customers to plan and allocate resources.
- Dynamic Limit Allocation: static monthly quotas give way to just-in-time allocation. For example: allowing an agent to use up to 50 credits per support ticket (=session) it handles.
- Time-Based Entitlements: traditional permissions are binary and persistent. Agent permissions need to be temporary, expiring when tasks complete or based on pre-defined rules, such as business hours.
Real-World Example: E-commerce Image Generation
Consider a SaaS company providing image processing services. A customer can have a subscription to the Pro plan, that includes monthly limits for image generation, image description, and image uploads. Now assume our customer wants to deploy an AI agent to automatically generate product images for their e-commerce catalog.
Traditional entitlement management would either give the agent full access to the user's quota or no access at all.
With proper agent entitlement allocation:
- Just-in-Time Allocation: when the agent needs to run, it receives a specific allocation - say 10 image generations, 20 descriptions, and 10 file uploads for this specific task
- Scoped Access: the allocation is limited to this agent, the specific session, and time window
- Automatic Enforcement: the agent cannot exceed its allocation or access other users' allocation
- Transparent Control: users can see exactly how their quota is consumed, by which agents, and for what purposes
- Predictable Costs: no surprise bills from runaway agents or quota exhaustion
This is the difference between handing someone your credit card versus giving them a prepaid gift card with specific merchant restrictions and an expiration date.
The Path Forward
AI agents aren't going away - they're becoming more prevalent, powerful, and autonomous. Companies that solve agent entitlement management now, before it becomes a crisis, will be the ones that thrive.
The key requirements are clear:
- Purpose-built solutions that recognize agent access patterns are fundamentally different from human patterns
- Context-aware metering that provides visibility into agent resource consumption
- Just-in-time allocation that prevents resource abuse while enabling functionality
- Time-based entitlements that automatically expire based on business logic
- Design for accountability that maintains clear audit trails and usage observability
While the industry focus is in preventing agentic security breaches, evolving your entitlement management system is about more than that. It’s about driving innovation and enabling your customers to safely operate AI agents at scale, using the agentic tools you provide. We're still in the early stages of figuring this out, but the organizations that start building these capabilities now will have a significant competitive advantage as the agent economy matures.
Facing challenges with AI agent monetization or quota enforcement?
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