Artificial intelligence agents are rapidly becoming a new interface for software. Instead of humans directly interacting with applications, agents can perform tasks on behalf of users such as reading emails, managing documents, updating issues, scheduling meetings, or interacting with APIs.
However, there is a major infrastructure problem that becomes obvious once you try to build real agents.
Agents need access to external services.
And that access is much harder to manage than most people initially assume.
The Integration Problem
A useful agent cannot operate in isolation. To be practical, it needs access to services such as:
- Google Workspace
- Slack
- GitHub
- Notion
- CRM systems
- Internal APIs
Each of these platforms requires authentication, usually through OAuth.
At first this seems manageable. But once agents need access to multiple services, the complexity increases quickly.
Developers must implement:
- OAuth authorization flows
- Secure token storage
- Token refresh logic
- Permission management
- Auditing and logging
- Service specific integrations
Every new service adds another layer of complexity.
The Hidden Cost of OAuth
OAuth was designed for applications acting on behalf of users. But when agents begin interacting with multiple systems simultaneously, the problem becomes infrastructure level.
For example, imagine an agent that:
- Reads emails from Gmail
- Creates tasks in Notion
- Posts updates to Slack
- Opens issues on GitHub
Each integration requires its own authentication flow and token lifecycle.
The developer building the agent now becomes responsible for:
- Securely storing tokens
- Refreshing tokens when they expire
- Handling revoked permissions
- Protecting access credentials
- Tracking which agent used which service
This is difficult to implement correctly and even harder to maintain securely.
The Missing Infrastructure Layer
Most developers solve this problem repeatedly in every project.
Each team builds its own solution for:
- Token storage
- Permission management
- Service connections
- Auditing
This duplication is inefficient and risky.
Instead, what if there were a central infrastructure layer that handled all of this?
An infrastructure layer where:
- Users connect their services once
- Tokens are stored and refreshed securely
- Agents access services through a unified API
- Permissions can be controlled centrally
- Every action is logged and auditable
This is the core idea behind Kaeso.
Introducing Kaeso
Kaeso is designed as an OAuth hub for AI agents.
Rather than each agent implementing authentication separately, agents can interact with Kaeso as a secure integration layer.
Users connect their services to Kaeso once. From there:
- Tokens are securely stored
- Refresh logic is handled automatically
- Integrations are standardized
- Agents access services through a unified interface
This allows developers to focus on agent logic rather than authentication infrastructure.
Why This Matters
As AI agents become more capable, they will increasingly interact with external systems.
Authentication, permissions, and integration management will become one of the most important infrastructure layers for agent ecosystems.
Without a reliable solution, every team will continue rebuilding the same fragile systems around OAuth and token management.
The goal of Kaeso is simple.
Provide the connect layer that allows AI agents to safely interact with the real world of software services.
More about the project can be found at:
Originally published on kaeso.ai.
Top comments (0)