Pipelines puts your agents through simulated real-world environments before deployment, so you can see how they behave, where they fail, and whether they're ready for production.
Drop your email, or see how it works first.
Built by a team from
Teams everywhere are racing agents into real operational workflows. Almost none have a structured way to know how those agents will behave before users depend on them.
Once an agent takes actions, calls tools, and talks to customers, the question isn't whether a model can produce a good answer — it's whether it behaves reliably across the messy range of conditions it will actually hit. That demands dedicated environments for pre-production testing.
Just as CI/CD brought discipline to software, Pipelines brings it to agents. That rigor is what we're built for.
Agents don't fail on single answers. They fail across multi-step tasks — calling tools, changing state, recovering from errors. Neither evals nor observability can see that before you ship.
Even multi-turn eval suites run against pre-written inputs that never change. They can't model a world that reacts to each tool call, evolves its state, or throws a failure mid-task.
Tracing and monitoring surface what went wrong once it already happened — in production, in front of real users, with real consequences.
Run your agent through stateful scenarios that respond to its every action and inject the failures you fear. See how it behaves — and where it breaks — before deployment, not after.
Pipelines turns ad-hoc agent testing into a repeatable loop — connect, simulate, grade, and iterate until you're sure.
Route your agent's tool calls through Pipelines with a few lines of our SDK. Your prompts, model, and logic stay exactly as they are.
1 @tool 2 def lookup(query): 3 # your tool, routed through 4 # Pipelines — simulated, never live 5 return proxy("lookup", query) 6 7 agent = Agent(tools=[lookup], model="gpt-5") 8 pipelines.serve(agent)▋
Evaluating an agent is not the same problem as evaluating a model's output. One is a fixed input-output mapping; the other is behavior that emerges from interaction with an environment that responds. Most tooling solves the first problem — agents pose the second.
Everyone agrees AI needs better evaluation. But what does that mean in practice? Here's the anatomy of an evaluation system designed for reproducibility, not just vibes.
Your team made a hundred model and prompt decisions in the last six months. How many can you defend with data? Rigor isn't about slowing down -- it's about building a foundation your team can actually stand on.
We're opening Pipelines to a small group of early collaborators. Work closely with us to shape the platform.