Polymath
Polymath builds high-fidelity, long-horizon reinforcement learning environments that simulate production software engineering workflows, enabling AI coding agents to train and be evaluated across complex, multi-tool tasks spanning the full software development lifecycle. The platform's Horizon-SWE benchmark places agents in containerized environments with live traffic, 73 engineering tools, and 20,000+ human-authored commits to measure autonomous engineering capability beyond simple code generation.
- Developer Tools
- Artificial Intelligence
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
AI coding agents have become remarkably capable at generating code and executing CLI commands, but benchmarks for long-horizon, production-grade engineering remain largely unexplored. Training environments for improving these capabilities are scarce, limiting the ability of agents to operate as autonomous software engineers rather than coding assistants.
Solution
Polymath builds simulation environments for AI agents to practice and learn through experience, focusing on increasing the reliability and autonomy of agentic software engineering. The company's Horizon-SWE benchmark places an entire software engineering company in a containerized environment, consisting of a running app with live traffic and a set of 73 software engineering tools. Agents are asked to complete large, multi-step tasks: gathering information, implementing features, testing, deploying, monitoring services, and responding to incidents. Performance is measured in terms of feature correctness, deployment and devops, and engineering quality, with verifiable outcomes and partial credit assigned due to task difficulty.
Target Audience
Primary customers are leading model labs and AI research organizations seeking to train and evaluate autonomous coding agents in realistic, production-grade software engineering environments.
Features
- Production-grade systems with multiple components including frontend, backend, databases, object stores, caches, and queues
- Tool use via MCP server spanning issue trackers, knowledge base, communication tools, deployment tools, logs, metrics, alarms, and alerting tools
- Long-horizon reasoning tasks requiring planning, sequencing, and judgment beyond local code correctness
- Stateful, evolving environments with seeded data, traffic, background jobs, and metrics that change over time while ensuring reproducibility
- Real interactions with traffic flowing through real containerized services and tools
- Monorepo containing 20,000+ human-authored commits deployed into a running application
- 50 diverse tasks including multi-service feature rollout, feature flag implementation, security incident response, latency optimization, error rate reduction, and API migration