Your software factory, defined in code.

Factories-as-code allows for a versioned and measured approach to optimizing costs and performance of your factories.

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The Factory definition is the shared foundation for every agent. It connects repositories, credentials, work sources, and cloud access, then sets the execution choices agents inherit.

>_[ fig. 1 — factory definition files ]⌗
factory.yamlyaml
schemaVersion: v1alpha1
name: web-server-factory
description: Autonomous software-development factory for a web application.
alias: web-factoryThe alias is the Foreman’s @-mention handle in Slack and Linear, so teammates can route work to the Factory by name. It can differ from the Factory name.
repositories: - owner: acme name: web-server - owner: acme name: web-clientRepositories define where Factory agents can work. Each run checks out the relevant code on its execution host so agents can inspect and change it.
secrets: - DEPLOY_API_TOKENSecrets give agents protected credentials for the tools and services they need. Values are encrypted at rest and injected as environment variables only during runs.
mcpServers: sentry: warpId: YOUR_MCP_SERVER_IDMCP servers connect every agent to shared tools such as Sentry or internal services. Each named connection references a centrally managed server.
cloudProviders: aws: roleArn: arn:aws:iam::123456789012:role/factory-agentCloud providers securely connect Factory agents to cloud accounts and resources for infrastructure work. Federated access lets runs assume an AWS role or use a GCP identity without embedding long-lived cloud keys in the definition.
integrations: - type: slack - type: linearIntegrations connect the Factory to work sources such as Slack and to Linear or Jira for task tracking, so the Foreman can coordinate work where your team already works.
agentDefaults: model: auto runner: standardAgent defaults establish the model or harness, runner, and environment that roles inherit, while specialized agents can override those choices when needed.
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case study

Cutting our cost per PR from $80 to $30

How we used Warp Factories Benchmarks to test models on our own engineering tasks and optimize for cost without sacrificing quality.

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Benchmark results comparing correctness against average cost across model configurations, with gpt-5.6-sol (high) called out as the benchmark winner at 95% correctness and $6.13 average cost.
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