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Llm agents

Build Hour: Agent RFT

Build Hour: Agent RFT

Will Hang and Theophile Sautory from OpenAI provide a deep dive into Agent RFT, a powerful method for fine-tuning large language models to become more effective, tool-using agents. They explain how Agent RFT enables models to learn directly from their interactions with custom tools and reward signals, leading to significant improvements in performance, latency, and efficiency on specialized tasks. The session includes a detailed code demo, best practices, and success stories from companies like Cognition, Ambience, and Rogo.

Introducing serverless reinforcement learning: Train reliable AI agents without worrying about GPUs

Introducing serverless reinforcement learning: Train reliable AI agents without worrying about GPUs

Kyle Corbett and Daniel from CoreWeave (formerly Openpipe) discuss the practical advantages of Reinforcement Learning (RL) over Supervised Fine-Tuning (SFT) for building reliable and efficient AI agents. They introduce Serverless RL, a new platform designed to eliminate the infrastructure complexities of RL training, and share a playbook for teams looking to get started.

When LLMs Go Online: The Emerging Threat of Web-Enabled LLMs

When LLMs Go Online: The Emerging Threat of Web-Enabled LLMs

Hanna Kim from KAIST explores the significant cybersecurity risks posed by web-enabled Large Language Model (LLM) agents. The research investigates how these agents, equipped with web search and navigation tools, can be misused to automate and scale cyberattacks involving personal data, such as PII collection, impersonation, and spear-phishing, while easily bypassing existing safety measures.

Deploying Executable Agent Workflows

Deploying Executable Agent Workflows

Gal Peretz introduces CodeAct, a paradigm where LLMs generate and execute Python code for tool interaction, offering a more flexible and powerful alternative to traditional JSON-based function calling for building complex, production-ready AI agents.

Making Your Data Agent-Ready with EnrichMCP // Simba Khadder // Agents in Production 2025

Making Your Data Agent-Ready with EnrichMCP // Simba Khadder // Agents in Production 2025

Simba Khadder explains that the primary bottleneck for LLM agents is not intelligence, but access to structured data. He introduces EnrichMCP, an open-source framework that creates a semantic layer over data models, enabling agents to discover, reason about, and query data sources like SQL databases effectively, moving beyond the limitations of RAG and direct API conversions.