What Agentic Integrations Actually Need

Everyone is publishing "agentic integration" comparison guides right now. Here are the top 5 platforms, here are the top 10 gateways, here is our definitive 2026 review. Most of them are written by the companies being compared, which should tell you something.

I have been building infrastructure for twenty years. Before that word got attached to AI agents, it meant the same thing it means now: the boring, invisible layer that makes everything else work. The fact that we are adding "agentic" in front of "integrations" does not change what integrations fundamentally require. It just raises the stakes.

Let me explain what I mean.

The connector count trap

Every comparison guide leads with connector count. 250 integrations. 500 integrations. 850 integrations. The number goes up every quarter, and somehow the product never gets worse. Funny how that works.

Connector count is the vanity metric of the integration world. It tells you how many APIs someone has written a wrapper for. It tells you nothing about whether those wrappers actually work reliably, handle edge cases, recover from failures, or stay current when the upstream API changes (which it will, usually without warning, on a Friday afternoon).

What matters is not how many connectors you have. What matters is whether the ones your customers need actually work in production, every time, without surprises.

The three things that actually matter

When an AI agent calls a tool, three things need to be true:

1. The execution must be deterministic.

Same input, same output. Every time. This sounds obvious until you realize that most agent-to-API interactions involve auth token refresh, rate limiting, pagination, retry logic, and error handling -- any of which can introduce non-determinism. If your agent gets different results on the second call because a token expired mid-request, you have a reliability problem that no amount of "smart routing" will fix.

Deterministic execution means the integration layer handles all of this transparently. The agent sees a clean interface. The messy reality of APIs is abstracted away completely.

2. Auth must disappear.

Authentication is the single largest source of integration failures. OAuth flows expire. API keys rotate. Service accounts get deprovisioned. Scopes change. Every one of these failures looks different, and most of them manifest as confusing error messages that tell you nothing about what actually went wrong.

For agents, this is worse than for humans. A human can re-authenticate. An agent running autonomously at 3 AM cannot. If your auth layer is not actively managing token lifecycle, detecting expiry before it happens, and handling refresh transparently, your agent will fail silently at the worst possible time.

Auth should not be a feature. It should be invisible.

3. Every action must be traceable.

When an agent does something wrong -- and it will -- you need to know exactly what happened. Not "the agent called the Gmail API." You need: what parameters were sent, what response came back, how long it took, whether it retried, and what the agent did with the result.

This is not observability for dashboards. This is observability for debugging. When a customer asks "why did my agent send that email," you need a complete trace from the agent's reasoning through the tool call to the API response and back. Without this, you are flying blind.

MCP is a protocol, not a solution

The Model Context Protocol is excellent. It standardizes how agents discover and call tools. It is the right abstraction for the interface between an LLM and the outside world.

But MCP is a protocol. It defines the shape of the conversation. It does not solve the hard problems: reliable execution, auth management, error recovery, rate limiting, data transformation, or tracing. Those problems live in the layer behind the MCP server.

Saying you support MCP is like saying you support HTTP. Great. So does everyone. What matters is what your server does when it receives the request.

The demo-to-production gap

Here is the uncomfortable truth about the current state of agentic integrations: almost everything works in a demo.

You can spin up an agent, connect it to Slack and Gmail, show it sending messages and reading emails, and it looks magical. The demo takes 10 minutes. Getting that same setup to work reliably for 1,000 users over 6 months takes a fundamentally different kind of engineering.

The gap between demo and production is where the real value lives. It is not glamorous. It is retry logic, token refresh, webhook delivery guarantees, idempotency, pagination handling, and error messages that actually help you fix the problem. It is infrastructure.

Infrastructure should disappear. Not because it is simple, but because it is so well-built that you never have to think about it.

What to actually look for

If you are evaluating platforms for connecting AI agents to real-world systems, skip the connector count and ask these questions:

1. What happens when an API call fails? Does the platform retry? With backoff? Does it tell the agent what went wrong in a way the agent can reason about?

2. How is auth managed? Is token refresh automatic? What happens when credentials expire? Will I get paged at 3 AM or will it just work?

3. Can I trace a complete action? From agent reasoning to API call to response, can I see the full chain? Can I replay it?

4. How current are the integrations? When Salesforce changes their API (again), how quickly does the platform update? Do I find out from my customers or from a changelog?

5. What does failure look like? Show me the error messages. Show me the retry behavior. Show me what happens when things go wrong. That tells me more than any feature comparison.

The answers to these questions will tell you more about a platform than any comparison guide. Including this one.