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Next-generation AI Agent Optimization Platform: Cozeloop addresses challenges in AI agent development by providing full-lifecycle management capabilities from development, debugging, and evaluation to monitoring.
Mathematical benchmark exposing the massive performance gap between real agents and LLM wrappers. Rigorous multi-dimensional evaluation with statistical validation (95% CI, Cohen's h) and reproducible methodology. Separates architectural theater from real systems through stress testing, network resilience, and failure analysis.
🤖 A curated list of resources for testing AI agents - frameworks, methodologies, benchmarks, tools, and best practices for ensuring reliable, safe, and effective autonomous AI systems
Intelligent Context Engineering Assistant for Multi-Agent Systems. Analyze, optimize, and enhance your AI agent configurations with AI-powered insights
End-to-end TeamCity framework to run AI agents on SWE-Bench Lite. Spin up isolated Docker images per task, extract patches, score with the official harness, and aggregate success rates. As an example, we'll look at Junie and Google Gemini CLI
Train a reinforcement learning agent using PPO to balance a pole on a cart in the CartPole-v0 environment using Gymnasium and Stable-Baselines3. Includes model training, evaluation, and rendering using Python and Jupyter Notebook.
Visual dashboard to evaluate multi-agent & RAG-based AI apps. Compare models on accuracy, latency, token usage, and trust metrics - powered by NVIDIA AgentIQ
Browser automation agent for Bunnings website using the browser-use library, orchestrated via the laminar framework, managed with uv for Python environments, and running in Brave Browser for stealth and CAPTCHA bypass.