AI-AGENTS

A deep dive into the six-library stack developed after 60+ enterprise deployments, focusing on governance, authorization, and reliability for AI agents.

Roblox has expanded its AI assistant with agentic capabilities that allow creators to automate the entire lifecycle of game development, from initial planning to automated testing and debugging.

A comprehensive guide on building production-grade AI agents for under $5/month using OpenRouter, LangChain, and cost-efficient open-source models like Llama 3 and DeepSeek.

OpenAI's latest update to the Agents SDK introduces native sandbox execution and model-native harnesses, revolutionizing how developers build secure, long-running AI agents.

An in-depth analysis of Anthropic's 'Sleeper Agents' paper, exploring why standard safety training like RLHF fails to prevent deceptive behavior in large language models and what it means for AI agent security.

Vercel CEO Guillermo Rauch discusses the platform's evolution from a frontend tool to an AI powerhouse, fueled by the rise of AI agents and a clear path toward a public offering.

Explore why agent harnesses are the future of AI development and how their relationship with memory affects your control over intellectual property and performance.

Learn how to bridge the gap between institutional knowledge and tacit expertise by incorporating human-in-the-loop systems into your AI agent development workflow.

A deep dive into the upcoming Interrupt 2026 conference, focusing on the technical shift from simple LLM wrappers to complex, multi-agent systems at enterprise scale, featuring implementation strategies with LangGraph and high-performance API routing.

A deep dive into the technical failures and architectural solutions discovered while running a production multi-agent system for half a year, covering cost optimization, reliability, and persistence.

Explore Deep Agents Deploy, a production-ready, model-agnostic agent harness designed to replace proprietary managed agent solutions with open-source flexibility.

An in-depth review of the ALTK-Evolve framework, exploring how AI agents can transition from static inference to dynamic, on-the-job learning using trajectory reflection and iterative refinement.