Forsy
Forsy.ai provides an AI observability and evaluation platform for cyber-physical systems, enabling teams to trace agent behavior in real time and surface recurring failure modes. The platform covers the full agent lifecycle—from live monitoring to decision integrity assessment—and converts deployment data into structured feedback loops for continual learning. It is built for engineering teams deploying AI in robotics, industrial automation, and autonomous operations.
- Artificial Intelligence
- AI Agents
- Robotics
- Industrial Automation
- Data & Analytics
- Software Only
- Developer Tools
Funding
Founders
Founder details are not available yet.
Product
Problem
AI agents operating in cyber-physical systems—such as robotics, industrial automation, and autonomous vehicles—face unique reliability challenges because their decisions directly impact physical environments. Unlike purely digital systems, these agents must contend with unpredictable real-world conditions, making failures costly and difficult to diagnose. Teams often lack the visibility needed to identify why agents fail and how to systematically improve their performance over time.
Solution
Forsy.ai provides an AI observability and evaluation platform purpose-built for cyber-physical operations. The platform enables teams to trace agent behavior in real time, monitor live operations, and evaluate whether decisions hold up under actual environmental conditions. It surfaces recurring failure modes across agents and deployments, giving engineers actionable intelligence to close reliability gaps. Forsy.ai also supports continual learning by turning every deployment into a structured feedback loop, allowing agents to improve iteratively. The platform covers the full lifecycle of cyber-physical agents—from live observability to decision integrity assessment to failure intelligence—so teams can build lasting reliability into their systems.
Target Audience
Primary customers are engineering and operations teams deploying AI agents in cyber-physical systems, including robotics companies, industrial automation providers, and organizations managing autonomous or physical-world AI operations.
Features
- Live observability: Real-time monitoring of AI agent behavior across cyber-physical operations
- Decision integrity: Evaluation of whether agent decisions remain valid under real-world conditions
- Failure intelligence: Automated surfacing of recurring failure modes across agents and deployments
- Continual learning: Structured feedback loops that convert deployment data into agent improvement cycles
- Event tracing: Detailed tracking of individual agent events with metrics such as pass rates and run times
- Issue detection: Automated flagging of critical issues that require engineering attention
- Skill encoding and activation: Ability to encode expertise from real workflows and activate it autonomously in context
- Deployment control: Controls over what gets deployed to agents, with security enforced across agents