Ship faster with always-on API coverage that keeps up.

The Checksum API Agent is a continuous testing agent for your backend APIs. You give it your API surface, through specs, HAR files, Postman collections, or existing traffic, and it generates journey-based tests that reflect how your product actually works.

From there, it runs in the background on its own. The agent keeps learning your API as it evolves: expanding coverage, picking up on new endpoints and flows, detecting when a contract changes, without anyone needing to re-prompt it. Engineers aren't writing and maintaining hundreds of tests by hand; they're reviewing what the agent surfaces.

The goal isn't to generate tests once. It's to keep a living test suite that stays current with your API, on its own, continuously.

AI coding tools ship code faster than any team can manually verify. That's the problem. More code, more endpoints, more edge cases, and the same amount of human time to check it. API testing is where that gap closes. If your verification layer doesn't keep up with your output layer, broken code ships faster. That's not a QA problem. It's an engineering velocity problem.

Testing an endpoint tells you it returns a 200. Testing a flow tells you your product actually works. A real user doesn't hit one endpoint in isolation; they create a resource, update it, trigger a downstream effect, and expect the state to be consistent throughout. Checksum generates journey-based tests that chain those calls together, pass dynamic values between steps, and verify that the full sequence behaves correctly. That's where the bugs that matter actually live.

Claude Code and Cursor write tests when you ask them to, and stop when you stop asking. Checksum works differently. You define the scope once: point it at your API, tell it what to cover, and from that point it runs on its own. It doesn't just execute a fixed set of tests on a loop; it keeps learning your API as it changes, picking up new endpoints, adjusting to contract changes, and expanding coverage without anyone re-prompting it.

That's the real distinction: it's not just automation, it's an agent that gets smarter about your API over time instead of only knowing what you told it on day one. With coding tools, every change to your API is another prompt you have to remember to write. With Checksum, the maintenance burden disappears after the initial setup.

Postman is a tool for building and running API requests manually. It's excellent for exploration and debugging. Checksum is an agent that automatically generates multi-step journey tests across your entire API surface, maintains them as your API changes, and runs them continuously in your CI pipeline. Postman requires someone to build and maintain collections. Checksum does that work autonomously, and keeps doing it as your product evolves.

They can, until they can't. Manual API testing works at small scale, with a stable API, and a team that has time for it. Most teams don't have all three anymore.

AI coding tools have changed the math. Recent industry research shows monthly production incidents climbing sharply since AI adoption accelerated, with most enterprise engineering leaders now reporting a direct increase in production issues tied to AI-generated code. Code is shipping faster than any manual process can verify it; the bottleneck was never writing the code, it's confirming it actually works. The teams that treat API testing as something developers handle ad hoc are the same teams debugging production incidents at 3am.

No. Checksum is designed for engineering teams that don't have large QA functions, and for QA teams that are stretched too thin to keep up with AI-driven development velocity. The agent generates and maintains the suite automatically. You don't need someone whose job it is to write and babysit API tests. If you do have a QA team, Checksum removes the low-leverage maintenance work so they can focus on exploratory testing and release strategy.

Engineers can trigger runs from the Checksum UI, schedule them automatically, or integrate via GitHub Action, API token, or webhook. Some teams use it as a pre-merge check, others run it post-deploy or nightly; Checksum fits into how you already ship, not the other way around.

Every test the agent generates is written in Python using PyTest, so what's running isn't a black box; engineers can open any test and read exactly what it's doing step by step.

Engineers work from a product surface that shows API coverage, generated journeys, run results, and agent sessions, so instead of digging through hundreds of test files, they can focus on what changed or what broke.

When a test fails, engineers can inspect the exact step that failed and the request/response behind it, and determine whether it's a real product bug, an intentional API change, or stale test data. Confirmed bugs get routed into the team's issue tracking workflow; intentional API changes prompt the agent to propose an updated test. The goal is to make every failure actionable, not a generic red test with no context.

Onboarding takes a few minutes. The agent analyzes your API surface and begins generating journey-based tests as soon as it ingests your spec. Most teams have meaningful coverage within days, not the months it would take to write the same tests manually. For large API surfaces with hundreds of endpoints, the agent works across the entire surface in parallel rather than one endpoint at a time.