pipelines.tech provides an evaluation platform that tests autonomous AI agents by simulating the real-world systems they act on and grading the outcomes. It runs agents against hundreds of isolated, stateful scenarios, checking not only the calls made but the final condition of the world left behind. The platform tracks variance by repeating identical runs to distinguish stable behavior from inherent agent randomness.
Funding
Funding not disclosed
Founders
Product
Problem
Autonomous agents are nondeterministic: the same input can produce different routes, calls, and outcomes. Traditional mocking and single-run evaluations fail to account for this variability, making it difficult to distinguish genuine improvements from random noise when scoring agent performance.
Solution
pipelines.tech builds a modeling layer that simulates the world an agent operates in, holding stateful systems where a read reflects a write the agent just made. It constructs a written suite of scenarios covering what the agent can do, runs each agent against hundreds of these isolated scenarios, and grades every run on both the actions taken and the state left behind. The platform compares results against user-declared constraints and failure patterns drawn from other domains, providing a repeatable evaluation methodology. It also tracks scenario variance by repeating identical agent runs, flagging unstable scenarios for stabilization or retirement before thresholds are set.
Target Audience
Engineering teams building and deploying autonomous AI agents that interact with external systems, particularly those needing reliable evaluation and regression testing for agent behavior.
Features
- Stateful world simulation where reads reflect previous writes made by the agent during the same run
- Scenario suite authoring that makes coverage an explicit, documented process rather than an ad-hoc engineering exercise
- Hundreds of isolated scenario executions per evaluation run, with parallel processing for scale
- Variance detection through repeated identical runs to identify nondeterministic agent behavior and unstable scenarios
- Post-run grading based on actual calls made, return values, and system state changes, not on a pre-scripted ideal path
- Agent self-account records held against call logs and state mutations to surface loops, dead ends, and repeated calls