Tile Labs builds open‑source stress‑testing tools that inject realistic faults into AI agent workflows and measure both task completion and correctness. Their framework provides failure taxonomies, in‑context learning loops, and RL benchmarks to help enterprises assess and improve the reliability of production LLM‑powered automation.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents and large language models are typically evaluated on clean, idealized tasks, which leads to overestimation of their performance when deployed in real-world production environments that contain timeouts, authentication failures, flaky APIs, and other sources of friction.
Solution
Tile Labs conducts applied research to quantify and improve the reliability of AI systems under realistic operational stress. The lab builds open‑source stress‑testing frameworks (e.g., EnterpriseOps‑Gym) that inject recoverable faults such as timeouts, auth errors, and malformed responses into agent workflows, then measures both full‑task success and intermediate verifier progress. By exposing failure modes, Tile Labs creates taxonomies that make problems actionable for engineers. The team also develops in‑context learning loops that store successful trajectories in a knowledge bank and surface few‑shot demonstrations at inference time, enabling agents to improve without costly fine‑tuning. Additionally, Tile Labs provides custom reinforcement‑learning environments with defined action spaces and verifiers to benchmark end‑to‑end correctness across multiple models, highlighting gaps between task completion and true correctness. These research outputs help organizations identify brittleness, prioritize robustness fixes, and adopt more dependable AI agents for production use.
Target Audience
Tile Labs’ outputs are aimed at enterprises and product teams that deploy AI agents or LLM‑powered automation in production, as well as AI research labs seeking rigorous reliability benchmarks.
Features
- EnterpriseOps‑Gym: a benchmark suite that runs identical agent tasks under baseline and stressed conditions, injecting realistic faults (timeouts, auth errors, tool‑call failures) and reporting full‑task success and verifier pass rates
- Taxonomy‑grounded failure analysis that categorizes stressors, enabling targeted engineering remediation and risk prioritization per deployment environment
- In‑context learning loop with a trajectory bank that indexes past successful runs and provides few‑shot demonstrations to agents at inference time, improving accuracy without model weight updates
- Custom RL environments with a constrained action space (search, add‑to‑cart, apply‑coupon, checkout, etc.) and a deterministic verifier that distinguishes between workflow completion and correct outcome
- Multi‑run statistical evaluation framework that captures variance under stochastic stress, ensuring robust performance estimates
- Open‑source tooling and detailed reporting dashboards that integrate with existing CI/CD pipelines for continuous reliability testing of AI agents