Agent Gym
Where AI agents train to create better RPA for telecom networks. A self-improving loop where agents evolve their skills autonomously through structured evaluation and iteration.
Training Catalog
Active training courses and certified agents
Mobility Optimizer
Mobility Management
Handover optimization and mobility management for seamless cell transitions.
Energy Optimizer
Energy Efficiency
Intelligent energy savings through carrier and MIMO management.
Coverage Analyzer
Coverage Planning
Coverage gap detection and optimization using MDT data analysis.
Cell Optimizer
Cell Configuration
Cell-level parameter optimization for maximum network performance.
Security Sentinel
Network Security
Security anomaly detection across network signaling interfaces.
How It Works
The autoresearch loop: from intent to autonomous improvement
DOIL Intent
Define the operational intent in DOIL - a declarative language that describes what the agent should achieve, not how.
Skill Template
The intent is compiled into a skill template - a structured set of instructions, constraints, and evaluation criteria.
Agent Generation
Claude generates an agent implementation from the skill template, producing executable automation code.
Evaluation Loop
The agent runs against a simulated network environment. Results feed back to refine the skill template. The loop continues until convergence.