Forem: Kaeso

How to Design OAuth for AI Agents Without Creating a Permission Mess

Kaeso — Sat, 28 Mar 2026 11:41:37 +0000

Everyone is building AI agents.

They can write code, summarize documents, search the web, create tickets, update databases, send messages, and automate workflows. But the moment an agent has to interact with real services like GitHub, Slack, Google Drive, Notion, or Stripe, the same problem appears:

authorization becomes messy very fast.

Most teams treat OAuth as a solved problem because traditional SaaS apps have used it for years. But AI agents change the shape of the problem.

A normal app usually performs a limited set of actions with a relatively clear boundary. An agent platform is different. It is dynamic, multi-step, sometimes autonomous, and often sits between users, third-party services, and other applications. That adds a completely different permission model.

If you do not design that model carefully, you end up with one of two bad outcomes:

a terrible user experience with constant re-auth and unclear permissions
or a dangerous system where apps and agents get far more access than they should

I have been thinking a lot about this while designing Kaeso, and one conclusion became very clear:

OAuth for AI agents should not be treated like ordinary app OAuth.

The real problem is not just authentication

When people talk about OAuth, they often simplify it to “sign in with X” or “connect your account.”

That is not the hard part.

The hard part is everything after that:

what exactly is connected
what scopes were granted
which application is allowed to use that access
whether access is read-only or write-capable
whether the access is one-time or continuous
whether background execution is allowed
how the user can review and revoke all of it later

With AI agents, this becomes even more important because the actor using the access is often not just a static UI. It may be an agent runtime, a workflow engine, a background job, or a third-party app calling into your platform.

So the real design question is not:

“How do we let users connect GitHub?”

It is:

“How do we let users delegate the minimum necessary access to a system that may act on their behalf across multiple services, while keeping that understandable, revocable, and secure?”

That is a much harder problem.

Why traditional OAuth patterns break down

A lot of existing OAuth implementations assume a much simpler world.

Typical flow:

User clicks “Connect GitHub”
App redirects to GitHub
User approves scopes
App gets tokens
App uses those tokens directly

That works fine for a single product with a narrow purpose.

But now imagine an AI platform where:

the user connects multiple services
third-party apps run on top of the platform
agents may use those services in the background
each app may need a different subset of permissions
and the user expects to understand what is happening

If you use the same simple pattern, you quickly get permission chaos.

For example:

one app gets a broad GitHub token even though it only needs repo metadata
another app reuses the same connection without the user fully realizing it
users do not know whether Slack access was granted to the platform, to the app, or to both
the system cannot cleanly explain what is already authorized and what is still missing

At that point, the product becomes hard to trust.

The three-layer model

The cleanest mental model I have found is to separate authorization into three distinct layers.

1. User to platform

This is just identity.

The user signs into your platform. In my case, that is Kaeso. This layer answers:

who is the user
what account do they have
what apps and services are associated with them

This is not the same thing as service authorization.

2. User to service through the platform

This is where the user connects GitHub, Slack, Google, and so on.

The platform receives provider-specific OAuth tokens and stores them securely. At this point, the user has not necessarily granted every app permission to use those services. They have only granted the platform the ability to broker access.

This is a critical distinction.

3. User to app through the platform

This is where a Kaeso app, agent, or integration asks for access to certain capabilities.

The app should not just get raw provider tokens by default. Instead, it should request platform-level permissions, and the platform should decide whether the required service connections and scopes already exist.

If they do, the user can approve the app.

If they do not, the platform should guide the user through the missing service authorization or scope upgrade.

This creates a much cleaner architecture because every permission request has a clear place in the system.

The mistake: giving apps raw provider tokens

This is the easiest bad design to fall into.

A user connects GitHub to your platform, and then you let apps directly receive or use that GitHub token with minimal separation.

It feels convenient, but it creates several problems:

poor permission isolation
weak auditability
harder revocation
provider-specific logic leaking everywhere
much larger blast radius if an app is compromised

The better model is to let the platform act as a broker.

That means:

users connect services to the platform
the platform stores and refreshes provider tokens
apps receive only platform-issued tokens
apps call the platform API
the platform performs downstream calls to GitHub, Slack, Google, and others

This preserves a clean boundary.

The app is authorized to use platform capabilities. It is not automatically entitled to direct possession of all underlying service credentials.

That one design choice makes the whole system easier to secure, explain, and operate.

The permission model should be platform-native

This is where I think most agent infrastructure will need to evolve.

Apps should request platform-native scopes, not raw provider scopes.

For example, instead of exposing a GitHub scope directly, the platform might define scopes like:

services.github.repositories.read
services.github.repositories.write
services.slack.channels.read
services.google.drive.files.read
agents.jobs.background
identity.profile.read

Then the platform maps each platform scope to whatever provider scopes and service states are actually required underneath.

This gives you several advantages:

a consistent permission language across providers
a better user experience
tighter product control
less provider-specific complexity for developers
clearer audit logs

Users should see permissions in the language of what the app is trying to do, not just in the language of whatever a provider happened to name its scopes.

What the user should actually see

A lot of consent screens are technically correct but product-wise terrible.

For agent systems, the consent UI has to be extremely explicit.

A good consent screen should answer all of these questions immediately:

what app is asking
who built it
what actions it wants to perform
which connected services are involved
whether those permissions already exist
what is still missing
whether access is read-only or write-enabled
whether background execution is allowed

Something like this is much clearer than a generic “Authorize app” button:

This app wants to:

Read selected GitHub repositories
Read selected Slack channels
Run background sync jobs

Kaeso will access on your behalf:

GitHub: repository metadata and contents
Slack: channel list and messages in approved channels

Missing requirements:

Slack is not connected yet
GitHub connection needs an upgraded scope

Actions:

Connect Slack
Upgrade GitHub access
Approve app
Cancel

That is the kind of UX I think agent platforms need.

The goal is not just to be compliant with an OAuth flow. The goal is to make the system understandable enough that the user can make a real decision.

Incremental authorization matters a lot

One of the biggest UX mistakes is forcing users through the entire permission process again every time something changes.

A better model is incremental authorization.

If the user already connected GitHub and already approved a previous app for repository read access, and a new app requests the same capability, the system should say so clearly.

If a new app requires one additional permission, the user should only see that delta.

That keeps consent manageable and avoids training users to blindly click approve.

For AI agents, this matters even more because workflows often evolve over time. A user might start with read-only access and later enable background jobs or write access. The system should support that progression cleanly instead of treating every change as a brand-new opaque authorization event.

Resource narrowing is as important as scopes

OAuth scopes are often too broad for what users actually want.

Saying “this app can read Slack” is not enough. In practice, users often want more granular control, such as:

only this workspace
only these channels
only this GitHub organization
only these repositories
only this Drive account
only this folder

That is why product-layer restrictions matter.

The OAuth provider might only offer broad scopes, but your platform can still narrow the effective access by introducing resource-level controls. For agent platforms, this is one of the most valuable trust features you can build.

It makes authorization much closer to what users actually intend.

Online access and background access should be separate permissions

This is another place where agent systems differ from normal apps.

A lot of actions happen asynchronously:

scheduled syncs
monitoring
notifications
long-running tasks
automatic follow-up actions

That means an app may need continued access even when the user is not actively using it.

That should never be hidden inside a generic permission grant.

Background execution is a materially different level of authority and should be presented separately.

For example:

use my connected services during this session
keep access for background automations
allow recurring sync
allow write actions without manual confirmation

Those are very different trust levels.

If users cannot distinguish them, the platform is not communicating honestly.

Revocation is part of the product, not an afterthought

A good authorization system is not only about granting access. It is also about removing it cleanly.

Users should be able to open one place and see:

connected services
scopes granted to the platform
apps with current access
which services each app can use
whether background access exists
when each app last used that access
buttons to revoke service access, app access, or both

Without this, even a technically strong OAuth system will feel unsafe.

For agent infrastructure, auditability and revocation are not “enterprise extras.” They are part of the core product.

What I think the correct architecture looks like

This is the model I currently believe makes the most sense for a system like Kaeso:

Identity layer

The user signs into the platform.

Connection layer

The user connects external services like GitHub, Slack, or Google.

Token vault

Provider tokens are stored securely by the platform and refreshed when needed.

App authorization layer

Apps request platform-native scopes, not raw provider scopes.

Consent engine

The platform compares app requirements against existing service connections and scopes, detects missing requirements, and presents a clear consent screen.

Broker API

Apps call the platform API, and the platform performs downstream service actions on the user’s behalf.

Audit and revocation layer

Every action is attributable, reviewable, and revocable.

That architecture is more work than a basic “connect service and store token” implementation, but it solves the actual problem instead of pushing it downstream.

Why this matters more as agents get better

As models improve, infrastructure weaknesses become more visible.

When an agent is limited, weak authorization design is partly masked because the agent cannot do much anyway.

When agents become more capable, the opposite happens. The cost of unclear permissions goes up.

A system that can reason, plan, and act across multiple tools is only as safe and usable as the authorization model underneath it.

So I think one of the next important infrastructure layers in the AI ecosystem is not just better agents.

It is better authorization systems for agents.

Not more impressive demos.

Not broader access by default.

Not “just give the model a token and see what happens.”

A real permission architecture.

Closing thought

The interesting part of AI agent infrastructure is not only what the agent can do.

It is also:

what it is allowed to do
who approved that
through which app
using which service
with which scope
for how long
and how easily that can be reviewed or revoked

That is why I think the right model is not “every app handles OAuth however it wants.”

It is a brokered system where the platform becomes the control layer between users, apps, and external services.

That is the direction I am exploring with Kaeso.

Because if AI agents are going to become real infrastructure, their authorization layer has to become real infrastructure too.

If you are building in this area, I would love to know how you are thinking about it.

Are you giving apps direct provider access, or are you building a broker layer in between?

Kaeso Official Launch Date Confirmed: June 1, 2026

Kaeso — Tue, 10 Mar 2026 15:20:00 +0000

Kaeso now has an official launch date.

After months of planning, architectural design, and early development work, Kaeso will officially launch on June 1, 2026 (UTC+1).

Kaeso is closer than ever.

This milestone marks an important step for the project. Until now, the focus has been on defining the platform architecture, building the core infrastructure, and refining the central idea behind Kaeso.

What Kaeso Is

Kaeso is being built as an OAuth hub for AI agents.

The platform provides a centralized connection layer where users can securely link services such as Google, Slack, GitHub, and other platforms. Kaeso manages OAuth authentication and token handling while offering a unified interface for AI agents to interact with connected services.

Instead of each agent or application implementing its own integrations, Kaeso acts as the central integration layer. It handles authentication, token management, and secure access in a structured and consistent way.

This enables agents and applications to interact with external services through a unified API while maintaining clear permission boundaries, visibility, and control over activity.

Why the Launch Date Matters

Setting a fixed launch date establishes a clear timeline for development and preparation. The June 1, 2026 release marks the transition from internal development to a publicly available platform.

In the months leading up to launch, development will focus on:

core platform infrastructure
secure OAuth token management
the unified API layer
permission and audit controls
reliability and platform stability

Additional updates, development insights, and feature previews will be shared as the launch date approaches.

Looking Ahead

Kaeso is currently in active development. The goal is to deliver a solid first version of the platform at launch, providing the foundation for building and running agents that can securely interact with external services.

More information about features, architecture, and early access will be announced in the coming months.

Official Launch Date: June 1, 2026

Timezone: UTC+1

Launching soon Kaeso v1

Your Call to Action Heading

Originally published on kaeso.ai.

How AI Agents Connect to APIs and External Services

Kaeso — Mon, 09 Mar 2026 00:02:54 +0000

AI agents are becoming increasingly capable. They can reason, plan tasks, and generate solutions to complex problems. However, their real power only appears when they interact with external systems.

To perform useful actions in the real world, agents need access to services such as:

• Google Drive

• Slack

• GitHub

• databases

• internal company APIs

Without these connections, agents remain limited to reasoning and text generation.

The ability to interact with APIs is what transforms AI agents into automation systems.

Why APIs Matter for AI Agents

APIs allow software to communicate with other software. For AI agents, APIs are the bridge between reasoning and action.

For example, an AI agent might:

• create a GitHub issue

• retrieve files from Google Drive

• send a message in Slack

• update a CRM system

• trigger an internal workflow

All of these actions require access to APIs.

However, this introduces an important challenge.

The Authentication Challenge

Most modern APIs require authentication. Services must ensure that only authorized applications can access user data.

The most common method used today is OAuth authentication.

OAuth allows applications to access resources on behalf of a user without exposing passwords. Instead, services issue access tokens that represent permission to interact with certain APIs.

While OAuth is secure, it introduces complexity.

Developers building AI agents must typically implement:

• OAuth authorization flows

• secure token storage

• token refresh mechanisms

• permission management

• API connection logic

For a single service this is manageable. But when agents interact with many services, integration complexity increases rapidly.

The Integration Explosion

Modern AI systems rarely connect to just one service.

A typical automation agent might need to access:

• multiple APIs

• internal systems

• cloud platforms

• collaboration tools

Each integration adds new authentication requirements, API structures, and permission models.

This leads to a recurring pattern: developers repeatedly rebuild the same integration logic across projects.

Over time this becomes inefficient and difficult to maintain.

Infrastructure for AI Agent Integrations

As the AI ecosystem evolves, a new architectural pattern is emerging.

Instead of implementing integrations individually inside every agent system, developers are beginning to treat authentication and integrations as infrastructure layers.

These layers handle:

• OAuth authentication

• token storage and refresh

• permission management

• unified API access

By separating integrations from agent logic, systems become easier to maintain and scale.

Agents can focus on reasoning and decision making, while infrastructure handles service connections.

The Emerging Ecosystem

We are likely to see a new generation of infrastructure tools supporting AI agent systems.

Just as cloud infrastructure simplified server management, integration infrastructure may simplify how agents interact with external services.

This shift will make it easier to build complex automation systems powered by AI.

Conclusion

AI agents are evolving quickly from experimental prototypes into real software infrastructure.

However, to become truly useful they must interact with the broader digital ecosystem.

APIs provide the mechanism for this interaction, but authentication and integration management remain major challenges.

As the ecosystem matures, dedicated infrastructure layers will play an important role in simplifying how AI agents connect to the services they depend on.

Originally published on kaeso.ai.

Why AI Agents Need an OAuth Infrastructure Layer

Kaeso — Sun, 08 Mar 2026 18:41:41 +0000

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:

https://kaeso.ai

Originally published on kaeso.ai.

The Path

Kaeso — Sun, 08 Mar 2026 17:15:50 +0000

Life begins with time.

Time is the only resource that cannot be recovered. Money can be regained. Systems can be rebuilt. Knowledge can be relearned. But time moves in only one direction. Once a moment passes, it is gone permanently.

This simple constraint defines everything that follows.

A human life averages around 79 years. When compared to the scale of history or the scale of the universe, that duration is almost nothing. It is a brief moment. A short gust of wind that appears and disappears without leaving much trace.

Because of this, every hour matters.

From the moment you are born, your brain begins collecting information. Every experience, every observation, every conversation becomes an input. The world continuously sends stimuli into your mind.

Most of these inputs appear small and unimportant.

You might notice the color of leaves while walking through a park.

You might read a single sentence that stays in your mind.

You might hear a conversation between strangers on a train.

You might discover a small detail while solving a technical problem.

None of these moments appear important in isolation. But the brain stores them anyway.

Over time these experiences accumulate into something larger. A reservoir of stimuli. A constantly growing collection of impressions, ideas, memories, and patterns. This reservoir slowly forms the structure through which you interpret the world.

In that sense, a human being is a system with memory.

Your brain processes the information it has collected and forms automatic routines. Most thoughts, reactions, and behaviors follow patterns created by biology, habit, and previous experience. A large part of life runs on these automatic processes.

But there is one crucial difference between humans and purely automatic systems.

You are not only the system.

You are also the pilot.

You can observe your own thinking.

You can interrupt routines.

You can examine the information stored in your reservoir of experiences and decide how it should influence your next action.

This ability leads directly to decisions.

Every decision you make is an investment of time. When you choose to do something, you allocate a portion of your limited life to that action.

Most decisions are positive investments even when they fail. Failure still produces information. Information improves your understanding of the system you are navigating.

The only truly negative investment is repeating the same mistake again and again until it becomes pure time loss.

A mistake itself is acceptable.

Repeating the same mistake is a failure to learn.

Because of this, analyzing your own decisions becomes essential. You must constantly ask yourself

Why did I do that

What was the reasoning

What would I change next time

However even when you analyze carefully and make the best decision you can think of, success is not guaranteed.

The world itself is not fair.

Sometimes you do everything correctly. You work, you push forward, you have the hunger and still the result does not appear. Opportunities may be blocked. Circumstances may limit you. External variables you cannot control may interfere.

This reveals something fundamental.

Life is a system with an enormous number of variables.

There are far too many parameters to calculate a perfect path. Every stimulus your brain receives slightly alters your perception which alters your decisions which alters your future direction.

Even the smallest input can shift outcomes.

Because of this complexity certainty is impossible.

You cannot calculate the perfect decision. You can only increase probabilities.

In that sense life resembles probabilistic systems such as artificial intelligence models. They do not know the future. They operate inside vast spaces of possible outcomes and attempt to generate the most optimal result given the information available.

Human decision making works in a similar way.

Every action you take slightly increases or decreases the probability of certain outcomes. Your goal is not to eliminate uncertainty. Your goal is to gradually improve the probability of reaching the outcomes you want.

This requires autonomy.

Many people simply follow paths defined by others. Social expectations, cultural defaults, inherited goals. They rarely question who defined those goals in the first place.

But if time is the most valuable resource you possess then the decisions that allocate that time must be yours.

You think about them.

You accept the risks.

You act.

And if the outcome is failure you extract information from it.

You adjust.

You continue.

You improve the next probability.

Because time is still moving forward.

You do not stop.

You do not rage.

You continue.

And next time you do not make the same fucking mistake again.

"Living is taking your own decisions."

Devin Oldenburg

This text was translated from German to English by Kaeso.

Originally published on kaeso.ai.

Redefining Kaeso

Kaeso — Sun, 08 Mar 2026 17:15:49 +0000

Kaeso was not defined wrong. Sometimes a product changes. Not because the idea was wrong, but because working in such problem-solving areas naturally leads to discovering more things you want to change. I personally decided that, since we haven't even started yet, it makes sense to turn the ship around as long as it is not too expensive to do so.

The Original Idea

At the start, I didn’t really strive for anything specific. I mainly wanted a motivation, a binding reason to work on such a project and to enter this environment: working with AI.

I already knew what the market was seeking. Almost all current inventions regarding AI revolve around one major topic: AI agents. Whatever it is, a support agent in a telecommunication company, a coding agent, everybody is thinking about agents.

So I thought about centralizing this idea by creating a platform that could use all kinds of models created by large companies to execute workflows with various tools and integrations. I already knew that agents need data. A construction worker cannot do what you want without a plan given by you. A lawyer cannot represent you without knowing your case.

Why a New Idea?

While working on this platform, I realized that it is extremely annoying to build a tool for the agent to use for every single small connection you want to create to any other data source. The agent only works with data, and there is a design principle that many developers, designers, and creators follow: "The User Is Stupid." An agent requires idiot proof design.

This leads to a simple question: how should the agent get data without the user manually giving it every time?

The solution is simple: the agent should be able to do it on its own. That is what users expect. This is also why people liked projects such as OpenClaw (ClawdBot).

You connect the agent to a connector and it retrieves whatever data it needs by itself.

The New Idea

The average human is lazy. Most people do not even want to set up a single gateway for an agent to access their data, not to mention several different ones. Every service has its own login, its own email, password, two factor authentication, and so on.

At the same time, people still expect privacy while often ignoring the complexity behind it.

This is where Kaeso solves the problem.

Kaeso creates a unified API gateway that agents connect to once. It provides a well documented and structured API where you can define permissions once and control which API keys are allowed to access which services.

It also allows multiple accounts per service, making it possible for an agent to access significantly more data than it would normally be able to on its own.

So this is Kaeso v1.

Stop linking up connectors

Originally published on kaeso.ai.

Pricing

Kaeso — Sun, 08 Mar 2026 17:07:01 +0000

Kaeso is designed as an OAuth hub for AI agent integrations.

Its purpose is simple: connect services once and allow AI agents to securely access them through a unified interface. Instead of every developer building their own OAuth flows, token storage, refresh logic, and permission systems, Kaeso provides a centralized infrastructure layer.

Because of this role, the platform should be accessible to developers and builders without creating financial friction.

Free for Users

Individual users will not be charged for using Kaeso.

The platform is meant to simplify integrations and enable experimentation. Charging users for connecting their services would work against that goal. People should be able to connect their tools and explore what AI agents can do without worrying about costs.

This will remain the case for the entire first year after launch. In reality, I expect this policy to continue even longer.

Developer Friendly Pricing (Only If Necessary)

The only situation where pricing might become necessary is infrastructure cost.

Running an OAuth infrastructure for AI agents involves servers, secure token storage, request processing, and potentially large volumes of API traffic generated by agents.

If infrastructure costs grow beyond what I can sustain personally, Kaeso may introduce very generous pricing specifically for developers who use extremely high volumes of API requests.

Even in that case, a free tier will always exist so that smaller projects and experimentation remain possible.

Donations

To help keep the platform accessible, people who want to support the project can donate. Donations help cover infrastructure costs and allow Kaeso to stay open and developer friendly.

Support Kaeso here:

https://donate.stripe.com/5kQbJ08mDfI72UBdLF0RG04

The Goal

Kaeso is infrastructure.

Infrastructure should be reliable, simple, and fair to use. The intention is to make connecting services for AI agents easier, not more expensive.

Originally published on kaeso.ai.

Why I Am Building Kaeso

Kaeso — Sun, 08 Mar 2026 17:07:00 +0000

My name is Devin Oldenburg, and I am the person building Kaeso.

I have always been interested in systems. Not just software itself, but how technology connects together to form larger infrastructures. Many products solve individual problems, but the most interesting ones build foundations that other developers can build on top of.

That way of thinking eventually led me to start building Kaeso.

Why I Started Building

When working with modern AI tools and agents, I noticed a repeating pattern. Almost every project needed to connect to external services. Whether it was Google, Slack, GitHub, or other APIs, the process always involved the same challenges.

Authentication flows
OAuth tokens
Token refresh logic
Permission management
Secure storage

Developers end up rebuilding the same infrastructure again and again.

Instead of solving the actual problem they want to work on, they spend time dealing with integrations.

That is the problem Kaeso is designed to solve.

What Kaeso Is

Kaeso is an OAuth hub for AI agent integrations.

The idea is simple. Connect services once and allow AI agents to securely access them through a unified API.

Instead of every developer implementing OAuth flows and token storage themselves, Kaeso provides the infrastructure layer that handles these tasks in a secure and structured way.

The goal is to make building AI-powered tools and agents easier.

My Approach to Building

I am building Kaeso with a focus on simplicity and long-term usefulness.

Many platforms try to optimize immediately for monetization or rapid growth. My focus is different. I believe the most important step is building something that developers actually want to use.

Infrastructure projects especially need trust. They must be reliable, predictable, and developer-friendly.

That is the direction I am focusing on.

Looking Forward

Kaeso is still in development.

The rise of AI agents will create an increasing need for infrastructure that connects services securely and efficiently. My goal is to build a platform that supports this ecosystem and simplifies integrations for developers.

If you are interested in the project or want to follow its development, you can always learn more here -> in our Kaeso Blog.

Stop building OAuth flows

Originally published on kaeso.ai.