AutoAgent is an autonomous framework designed for agent engineering, functioning similarly to autoresearch but focused on building and iterating on agent harnesses. The system allows a user to assign a task to an AI agent, which then autonomously modifies system prompts, tools, agent configurations, and orchestration over time. By running benchmarks and checking scores, the meta-agent performs a hill-climbing optimization, keeping improvements and discarding failures. The core workflow involves programming via a Markdown file called program.md, which provides context and directives to the meta-agent, while the meta-agent directly edits the agent.py harness file. This approach minimizes manual engineering by allowing the agent to optimize its own performance through continuous, automated experimentation.
AutoAgent is a revolutionary open-source library designed to automate the tedious process of agent engineering and prompt tuning. By employing a meta-agent, the library allows for the autonomous optimization of an agent's harness, including system prompts, tool definitions, and orchestration strategies, all without human intervention. During a 24-hour run, AutoAgent achieved impressive results, including the top score on SpreadsheetBench and a leading GPT-5 score on TerminalBench. This technology effectively transitions the human's role from a manual engineer to a high-level director, enabling rapid, self-improving agent development across various domains and benchmarks.
This research introduces a novel robot operating system (ROS) framework designed to seamlessly integrate large language models (LLMs) into embodied artificial intelligence. The framework enables robots to interpret and execute natural language instructions with greater versatility and reliability.
Key features include automatic translation of LLM outputs into robot actions, support for both code-based and behavior tree execution modes, and the ability to learn new skills through imitation and automated optimization.
Extensive experiments demonstrate the robustness and scalability of the framework across diverse scenarios, including complex tasks like coffee making and remote control. The complete implementation is available as open-source code, utilizing open-source pretrained LLMs.
Key features include automatic translation of LLM outputs into robot actions, support for both code-based and behavior tree execution modes, and the ability to learn new skills through imitation and automated optimization.
Extensive experiments demonstrate the robustness and scalability of the framework across diverse scenarios, including complex tasks like coffee making and remote control. The complete implementation is available as open-source code, utilizing open-source pretrained LLMs.
A-Evolve, a new framework developed by Amazon researchers, aims to revolutionize the development of agentic AI systems. It addresses the current bottleneck of manual tuning by introducing an automated evolution process. Described as a potential "PyTorch moment" for agentic AI, A-Evolve moves away from hand-tuned prompts towards a scalable system where agents improve their code and logic iteratively.
The framework centers around an ‘Agent Workspace’ with components like manifest files, prompts, skills, tools, and memory. A five-stage loop—Solve, Observe, Evolve, Gate, and Reload—ensures stable improvements. A-Evolve is modular, allowing for "Bring Your Own" approaches to agents, environments, and algorithms, and has demonstrated State-of-the-Art performance on benchmarks like MCP-Atlas and SWE-bench Verified.
The framework centers around an ‘Agent Workspace’ with components like manifest files, prompts, skills, tools, and memory. A five-stage loop—Solve, Observe, Evolve, Gate, and Reload—ensures stable improvements. A-Evolve is modular, allowing for "Bring Your Own" approaches to agents, environments, and algorithms, and has demonstrated State-of-the-Art performance on benchmarks like MCP-Atlas and SWE-bench Verified.
This handbook provides a comprehensive introduction to Claude Code, Anthropic's AI-powered software development agent. It details how Claude Code differs from traditional autocomplete tools, functioning as an agent that reads, reasons about, and modifies codebases with user direction. The guide covers installation, initial setup, advanced workflows, integrations, and autonomous loops. It's aimed at developers, founders, and anyone seeking to leverage AI in software creation, emphasizing building real applications, accelerating feature development, and maintaining codebases efficiently. The handbook also highlights the importance of prompt discipline, planning, and understanding the underlying model to maximize Claude Code's capabilities.
This article explores how agentic AI can revolutionize deep learning experimentation by automating tasks like hyperparameter tuning, architecture search, and data augmentation. It delves into the core concepts, benefits, and practical considerations of using agentic systems to accelerate and improve the deep learning workflow.
This article details seven pre-built n8n workflows designed to streamline common data science tasks, including data extraction, cleaning, model training, and deployment.
This practical guide uses SERP comparisons and Python to group keywords by intent, faster and more intuitively.
Optuna is an open-source hyperparameter optimization framework designed to automate the hyperparameter search process for machine learning models. It supports various frameworks like TensorFlow, Keras, Scikit-Learn, XGBoost, and LightGBM, offering features like eager search spaces, state-of-the-art algorithms, and easy parallelization.
Sakana AI introduces The AI Scientist, a system enabling foundation models like LLMs to perform scientific research independently, automating the entire research lifecycle.
