Corsair vs Zapier: Building AI Agent Integrations When No Connector Exists

Every integration platform eventually runs into the same wall: your AI agent needs to talk to an app that simply is not in the catalog. Maybe it is an internal tool your company built years ago, a vertical SaaS product with a small but loyal user base, or an API that launched last month. When that happens, a list of prebuilt connectors stops being useful and the real question becomes how you build the connection yourself. This post looks at why prebuilt integration catalogs run out, what it actually takes to turn a piece of API documentation into a working AI agent integration, and how Corsair's open source plugin model compares to Zapier's approach to custom integrations when you are the one filling the gap.

What Happens When Your AI Agent Needs an App That Your Integration Platform Doesn't Support

Nothing happens automatically. Your agent's tool list only contains the apps someone has already built a connector for, so if the app you need is not in the catalog, the request fails or gets routed to a human. The integration platform does not know the API exists until you tell it, and until then your agent cannot read a record, send a message, or trigger an action in that system, no matter how well it can reason about the request.

This is a common moment for teams shipping AI products. A customer asks for a connection to an ERP the vendor never listed, a support agent needs to read tickets from a niche help desk tool, or an internal ops bot needs to call a company's own scheduling API. At that point you have two real options:

• Reach for a generic passthrough primitive, such as a raw HTTP call or a webhook, to solve the one request in front of you
• Build a proper integration: something typed, reusable, and safe to hand to an AI agent that will call it again across different users and conversations

For a single internal workflow, the first option is often enough. For anything a production AI agent needs to depend on, especially across multiple customers, the second is the better investment, because the agent needs a predictable, callable tool rather than a one time script.

The Missing Connector Problem: Why Prebuilt Integration Catalogs Can't Cover Every API

Prebuilt catalogs are finite because a person has to research, build, test, and maintain every connector, and the number of APIs in the world grows faster than any team can keep pace with. No integration platform, open source or closed, ships with a connector for every app that exists. The question is only how big the gap is and how much control you have once you hit it.

A few reasons the gap never fully closes:

• Internal and private APIs: company built tools and internal systems have no public listing anywhere, so no vendor has a reason to build a connector for them
• Niche or vertical SaaS: a smaller user base rarely justifies the engineering investment a prebuilt connector requires
• Newly launched APIs: providers ship new products constantly, and a connector takes review and testing time to catch up
• Partial coverage: even apps that are already supported often expose only a fraction of their full API surface as prebuilt actions

Closed platforms make this harder to work around because you are limited to whatever a vendor has chosen to prioritize. If the app you need is not on their roadmap, you wait, or you route around the platform entirely. That ceiling on what you can ship, one unsupported endpoint at a time, is worth reading more about in why closed source integration tools box you in.

How Do You Add an Integration That Isn't Already Available

You add it by either wiring a one off connection with generic tools the platform already gives you, or by building a custom integration using the platform's developer framework, whether that is an SDK you run yourself or a hosted builder. Which path makes sense depends on how many users need it, how often it gets called, and whether an AI agent needs to invoke it as a structured tool rather than a step in a fixed sequence.

In practice there are three general paths:

1. A generic primitive for one workflow: a webhook or a raw API request step. Fastest to set up, but not typed and not reusable across other parts of your product
2. A full integration inside a developer framework: more setup up front, but the result becomes a reusable, first party connector your agent can call like any other
3. Fully custom code outside any framework: complete control, but you own authentication, retries, rate limits, and error handling yourself from the start

Choosing between a quick workaround and a proper API integration is really a smaller version of the classic decision between building something custom and buying it off the shelf. What Is API Integration? covers that tradeoff in more depth, including when a fully custom integration makes more sense than leaning on iPaaS or a unified API.

Connecting AI Agents to Niche, New, and Internal APIs Without Prebuilt Connectors

Connecting an AI agent to an API without a prebuilt connector means the integration has to behave like a tool the model can call mid conversation, with typed inputs and outputs and credentials already resolved, rather than a step that only runs on a schedule or a trigger. That distinction is what separates an integration built for classic workflow automation from one built for an agent that decides in real time which app to touch and when.

A few requirements that only show up once an AI agent is the one calling the integration:

• Real time invocation: instead of firing on a schedule or an incoming webhook, the agent calls the integration whenever the conversation actually calls for it
• Typed input and output: the model needs a predictable schema to reason over, not a raw response straight from the underlying API
• Multi tenant credentials: a product with many customers needs each customer's own token for the new API, isolated from every other customer's data
• Permission gates: a brand new internal API deserves the same approval flow as any other integration before it can send an email, delete a record, or move money

Build this from scratch and you are not just wiring one endpoint. You are rebuilding auth storage, token refresh, tenant isolation, and permission checks, which is exactly the plumbing that turns a quick script into an ongoing maintenance job.

Turning API Documentation Into a Working AI Agent Integration

Turning documentation into a working integration comes down to three steps: figure out how the API authenticates, map the specific endpoints you need into actions with defined inputs and outputs, and register the result so your agent can call it the same way it calls every other integration. The mechanics of that last step are where Corsair and Zapier diverge.

On Zapier:

• For a single, one off connection, Webhooks by Zapier or a Code step in Python or JavaScript lets you call an undocumented API inside one Zap
• For a reusable connector, the Zapier Developer Platform offers a no code Platform UI, built from your API's endpoint URLs, headers, and parameters, or a Platform CLI for local development
• Building a full private integration means defining an authentication scheme, adding individual triggers and actions, testing them, running Zapier's validation checks, and then publishing privately or to the public app directory
• The result plugs into Zapier's trigger and action model as a step other Zaps can use

On Corsair:

• If no plugin package exists for the API yet, the SDK's generator scaffolds a new plugin with its own package and the standard structure for auth, actions, and webhooks already in place
• You wire the target API's authentication and map its documented endpoints into typed actions
• Because the surrounding SDK already handles OAuth storage and refresh, multi tenant credential isolation, permission gating, and webhook or polling based caching, building the plugin is mostly about that one API rather than reimplementing integration infrastructure
• The new integration is called the same way as every built in plugin once it is registered, so the agent can invoke it as a real time, typed tool call
• Because it is open source TypeScript, you can also open a pull request so the plugin becomes available to everyone instead of staying private to your project

For a closer look at what that typed, code first workflow feels like in practice, see building AI agent integrations in TypeScript.

How to Add New Actions and Endpoints as Your Integration Requirements Grow

You add new actions and endpoints by extending the integration you already built rather than starting over, and how easy that is depends on whether you own the underlying code or are working inside a hosted builder's versioning system. Requirements rarely stay fixed. The API you integrated with six months ago adds a new endpoint, or your product needs a new action against a service you already connected.

A few things worth planning for as requirements grow:

• Versioning: adding a new trigger or action inside a Zapier private integration means updating the integration definition and, for anything used in production, running through validation again before republishing
• Code ownership: adding a method to a Corsair plugin is a code change in a package you own, whether that is your own fork or the shared plugin, so it ships the moment you deploy
• Backward compatibility: existing workflows or agent tool calls should keep working while the new capability rolls out, regardless of which model you use
• Collaboration: local development with version control suits teams that already work in a code first workflow, while a hosted builder suits teams that would rather not maintain that tooling themselves

Corsair vs Zapier: Which Approach Gives Developers More Flexibility Beyond Prebuilt Connectors

The honest answer depends on what you are building, but the flexibility gap comes down to three things: how the integration executes once it exists, who owns the code behind it, and how much of the surrounding plumbing, such as auth, permissions, and multi tenancy, you have to rebuild every time the catalog is missing something.

• Execution model: Zapier is built around Zaps, trigger and action workflows that run on a schedule or an incoming event. Corsair is built around typed methods that your code, or an AI agent, calls directly at runtime, which fits both scheduled automation and real time tool calling inside a conversation
• Where the missing connector lives: a private integration you build on Zapier lives inside Zapier's developer console, versioned and published through their platform. A new Corsair plugin is TypeScript in your own repository, or a contribution to the shared open source one, and either way you can self host it
• Source visibility: Zapier's core platform and its existing app connectors are closed source, so a bug or a gap in a connector you did not build is something you report and wait on. Corsair's plugins, including the ones you did not write yourself, are open source, so you can read, patch, or extend them directly
• What you inherit for free: a new Zapier integration still needs its own authentication setup defined from scratch inside the developer platform. A new Corsair plugin inherits OAuth handling, multi tenant credential isolation, permission gating, and caching from the SDK it plugs into
• Fit: Zapier's model is a strong fit for scheduled, trigger based automation between business apps. Corsair's model is purpose built for AI products that need real time, multi tenant, permissioned access to whatever app or internal API the agent needs next

Whichever integration platform your team is already using, the app your AI agent cannot reach yet is usually just a documentation page away from becoming a real integration. Corsair takes on the OAuth, multi tenant credentials, permissions, and caching that normally eat up most of that build time, so adding the connector your catalog is missing takes an afternoon of wiring instead of a rebuild of your auth layer. You can explore the open source plugins, the SDK, and the self hosting option at corsair.dev to see how quickly a new API turns into a typed, callable integration your agent can use today.