- Where the work sits when an agent acts in someone's apps
- The trap of reading only the headline
- Four jobs these platforms do
- Mistakes teams make with agent permissions
- Eight platforms in one table
- Platform by platform, A to Z
- A one-afternoon trial with a real agent
- For founders wiring their first agent into customer tools
- Frequently asked questions
An AI agent integration platform handles the plumbing between an agent and the apps it acts in: sign-in and tokens for each user, the tools the agent can call, and limits on what it may do. Shortlist by the job you need, whether that's managed tools, a governed actions layer, integrations inside your product or a workflow builder.
Most teams assume that giving an agent access to SaaS apps is mostly a prompting problem. Get the instructions right and the agent will book the meeting or update the record. In practice the prompt is the easy part. The work sits in who the agent is acting as, which accounts it can reach and what happens when a token expires halfway through a task. The eight platforms below, in no particular order, exist to take that work off developers and technical founders.
Where the work sits when an agent acts in someone's apps
Every action an agent takes in a third-party app needs three things. First, credentials: usually an OAuth connection made by a real user, stored safely and refreshed when it expires. Second, a tool definition the model can understand, describing what the action does and what inputs it needs. Third, limits, so the agent can read a calendar without being able to delete one, and so a request from one customer can never reach another customer's data. Every tool description in this list comes from the vendor's own site.
Build that for one app and it's an afternoon. Build it for twenty apps, each with its own auth quirks and API changes, and it's a team's whole quarter. That's the gap these platforms fill, and it's why most of them now talk about MCP, the protocol many AI clients use to discover and call tools.
The trap of reading only the headline
Several vendors here have rewritten their own headlines around agents. Merge's homepage now calls it the universal context engine for AI, with its unified API listed as one product beside Agent Handler and a Gateway. Pipedream calls itself the integration layer for AI agents. Other vendors on this list now lead with agents too.
That's a fair reflection of where demand is, but it creates a trap. If what you need is the older integration job, such as syncing a customer's CRM records into your product every night, check that the product you're shortlisting still does it well, not only the agent pitch. Read the docs for the exact job, look for the endpoints or sync features you'd use, and ask the vendor plainly whether that part of the product is still where their effort goes.
Four jobs these platforms do
Managed tools and MCP gateways
Composio gives agents managed tools and auth behind one MCP endpoint, with triggers and a firewall layer. Zapier MCP lets AI clients act in apps a person has already connected, with auth and permissions the vendor says you control. The integration layer mentioned above sits here too, with an SDK for developers.
Governed actions
Arcade positions itself as an actions runtime between agents and the systems they touch, handling authorization, execution and policy from one control point. Its own line is one control point to enforce, execute and govern every action, so it's the group to look at when every agent action needs a rule behind it.
Integrations inside your own product
Paragon is an embedded integration platform for product teams connecting their agents to customer data. Nango covers auth, MCP tools, triggers, sync, webhooks and a request proxy, so it serves both agent features and classic product integrations. The context engine named earlier belongs in this group as well.
Workflow builders
n8n is the one workflow automation platform on the list, built for agents and workflows where you can open each step and see what happened. It suits teams who want to see exactly what each step did after a run.
Mistakes teams make with agent permissions
The most common one is a shared key. A single admin token is quick to set up, and it means every user's agent can do whatever the admin can do. Use per-user connections wherever the platform supports them. The second is giving an agent every action an app offers when it needs two; most platforms let you limit the tool list, so do it from the start. The third is skipping logs. When an agent sends the wrong email, you'll want to see which tool it called, with what inputs, as which user.
Eight platforms in one table
| Tool | Kind of platform | Who it suits |
|---|---|---|
| Arcade | Agent actions runtime and MCP gateway | Governing what agents can do |
| Composio | Tools, auth and MCP gateway for agents | Developers giving agents managed tools |
| Merge | Unified API and agent tools | Agent tooling from a unified API company |
| n8n | Workflow and agent builder | Agents and workflows you can inspect |
| Nango | Auth, sync and tools for agents | Auth, sync and live context together |
| Paragon | Embedded integration platform | Connecting agents to customer data |
| Pipedream | Managed auth and MCP tools | Agents calling actions in connected apps |
| Zapier MCP | No-code automation vendor MCP | AI clients acting in everyday apps |
Platform by platform, A to Z
Alphabetical, with one line each from the vendor's own description. Vendor claims are marked as such.
Arcade
Best for: Teams that want one control point between agents and their systems, handling authorization, execution and policy for every action, per the vendor.
Composio
Best for: Developers who'd like agents to reach managed tools and auth through one MCP endpoint, with triggers and a firewall layer included.
Merge
Best for: Teams wanting agent tooling, an LLM gateway and a context layer from a unified API company; vendor claim: agents connect to thousands of pre-built tools.
n8n
Best for: Building agents and workflows where each step can be opened and inspected, which the vendor frames as agents you can follow.
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Nango
Best for: Engineering teams needing auth, MCP tools, triggers, data sync, webhooks and a request proxy in one place; vendor claim: live context for your RAG.
Paragon
Best for: Product teams connecting their agents to customer data, with syncing, permissions data, auth and provider changes handled; the vendor's line is keeping upkeep off your roadmap.
Pipedream
Best for: Developers whose agents need to call actions in connected apps through managed auth and a remote MCP server, with an SDK alongside.
Zapier MCP
Best for: People who want AI clients such as Claude, ChatGPT or Cursor to act in their connected apps, with managed auth and permissions they control.
A one-afternoon trial with a real agent
- Pick one app your users live in and one action that matters, such as creating a calendar event or updating a deal.
- Connect a test account through each of two shortlisted platforms, as a user would, and note every consent screen.
- Point the same agent at both and give it the same instruction, then check the result in the app itself.
- Revoke the connection and run it again, to see how each platform reports the failure.
- Open the logs and confirm you can tell exactly what the agent did and on whose behalf.
For founders wiring their first agent into customer tools
Keep the first release narrow: one or two apps, a short list of actions, per-user auth from day one and logs someone reads. Choose the platform whose main job matches yours, managed tools for quick reach, a governed runtime when every action needs a rule, an embedded platform for integrations inside your product, or a workflow builder if non-developers will maintain it.
For the wider picture, my roundups of MCP servers and AI agent frameworks cover the pieces on either side of these platforms, and these Zapier alternatives help when the job is plain automation rather than agents.
Do you make an agent integration, MCP or auth platform? Tool makers can ask to be considered for a featured place on this list.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
What is an AI agent integration platform?
It's the layer between an agent and the apps it works in. It stores each user's connections, exposes actions as tools the model can call, and limits what the agent is allowed to do.
Do I need MCP to connect an agent to apps?
Not strictly, but many AI clients now discover and call tools through MCP, so platforms that offer an MCP server or gateway make it easier to use the same tools across different clients.
Should every user connect their own account?
Usually, yes. Per-user connections mean the agent can only do what that user could do, which is far safer than one shared admin key.
Can these platforms handle classic product integrations as well?
Some can, and some have moved their focus almost entirely to agents. Check the docs for the exact sync or API job you need before you assume it's covered.
Is a workflow builder enough for agent work?
For internal automation, often. For agents inside a product you sell, you'll usually want per-customer auth and permissions that a dedicated integration platform handles.
How do I keep track of what an agent did?
Pick a platform with logs that show each tool call, its inputs and the user it ran as, and test that during your trial rather than after launch.
