Fiveonefour
Fiveonefour provides an experimentation platform that runs unbiased, large-scale simulations of coding agents interacting with developer products. Teams define experiments across prompts, agents, models, and environments, then execute them in isolated sandboxes to capture transcripts, system activity, and custom metrics. The platform supports Claude Code, Codex, and Cursor, with results queryable via CLI or MCP for product and marketing decisions.
- Developer Tools
- Artificial Intelligence
- Data & Analytics
- AI Agents
- Software Only
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
As coding agents become primary users of developer tools and APIs, product teams can no longer rely on human intuition or traditional UX best practices to predict how agents will interact with their products. Agents behave fundamentally differently from human users, making it difficult to know what they will discover, how they will navigate interfaces, and where they will fail—leading to poor adoption and underperformance in an increasingly agent-driven market.
Solution
Fiveonefour provides an experimentation platform that runs unbiased, large-scale simulations of coding agents interacting with a product. Teams define experiments that test variants across prompts, agents, models, products, and environments, then execute them in isolated, secure sandboxes to prevent bias and context pollution. The platform captures rich data—including agent transcripts, system activity, test outcomes, and custom metrics like tokens and cost—and normalizes it across agent harnesses for consistent analysis. Users can query results via CLI or MCP, generate shareable insights, and use the evidence to make product, documentation, and marketing decisions that improve agent discoverability and usability.
Target Audience
Primary customers are product teams, developer experience engineers, and go-to-market professionals at software companies building CLIs, SDKs, APIs, web apps, MCP servers, or documentation who need to understand and optimize how coding agents discover and use their products.
Features
- Experiment specification in YAML that defines agents, models, prompts, products, and environments for reproducible testing
- Managed sandbox infrastructure that spins up fresh, isolated environments per run with no conversation history to prevent bias
- Support for Claude Code, Codex, and Cursor as first-class agents with a managed gateway that uses short-lived, run-scoped tokens instead of provider API keys
- Automated test execution in separate sandboxes created from snapshots, preventing agents from accessing test definitions during runs
- Rich data capture including agent transcripts, system activity, test outcomes, tokens, cost, and wall-clock time, normalized across agent harnesses
- CLI and MCP queryable data with shareable insights for collaborative analysis
- Parallel cloud dispatch for large sample sizes and retained sandbox end-state for inspection without re-running experiments
- Post-run detection of whether agents noticed they were being tested, with flagged runs excluded from statistics unless explicitly included