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Prefactor

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Agents behave differently in production Agents can pass pre-production evals and still fail when they encounter real users, tools, data and workflows. Most teams only discover those failures through sampled traces, support tickets or after something has already gone wrong. Prefactor helps engineering teams continuously evaluate how their agents are actually performing in production.\*\* How Prefactor works Connect your agents through our \*\*Python or TypeScript SDK, API, or existing observability stack\*\*. Prefactor captures production activity including: \* Agent runs and conversations \* Traces, spans and tool calls \* Task outcomes and failures \* Latency, token usage and cost \* Agent versions and deployment context Evaluate every run Instead of relying on periodic sampling, Prefactor evaluates production traffic continuously using: \* Built-in heuristics\*\* for behavioural and performance signals \* Your own evals\*\* and quality criteria \* Human feedback and review\*\* \* Real-time production signals\*\* such as failures, drift, latency and cost changes Teams can see when agent behaviour changes, identify the runs responsible and understand what happened with the full production context. Turn production failures into better agents When something goes wrong, Prefactor helps teams use that production behaviour to create new evaluation scenarios, test changes and measure whether the next version actually performs better once it reaches customers. Prefactor turns production agent traffic into a continuous feedback loop between monitoring, evaluation and improvement. Built for engineering teams shipping AI agents to real customers.

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