LangChain
A comprehensive technical guide to LangChain, the framework enabling developers to build sophisticated LLM applications with chaining, memory, and retrieval capabilities essential for modern AI systems.
LLM safety
New research demonstrates how multi-agent debate frameworks can evaluate LLM safety more efficiently than traditional methods, reducing costs while maintaining accuracy in identifying harmful model behaviors.
AI Agents
Analysis of how AI agents handle human tasks reveals critical performance patterns: exceptional speed paired with systematic flaws in reasoning, tool use, and decision-making that impact real-world deployment.
AI Agents
Anthropic's Model Context Protocol (MCP) establishes a standardized infrastructure layer enabling AI agents to access external data and tools. This protocol architecture is transforming how AI systems interact with real-world applications and services.
AI Research
New research introduces Jr. AI Scientist, an autonomous system that conducts scientific exploration from baseline papers. The study includes comprehensive risk assessment frameworks for AI-driven research automation.
AI safety
Researchers introduce comprehensive evaluation framework for control protocols designed to manage untrusted AI agents, addressing key safety challenges as autonomous systems become more capable and potentially misaligned.
AI Security
New research demonstrates how synthetic data generation can systematically optimize adversarial attacks against AI agents, revealing critical security vulnerabilities in autonomous systems through automated testing frameworks.
AI Agents
Deep dive into Agent-to-Agent (A2A) and Model Context Protocol (MCP) for AI agent systems. Technical comparison of architectures, use cases, and implementation strategies for multi-agent coordination.
AI Agents
Technical deep dive into designing agentic AI systems with human-like memory decay, self-evaluation capabilities, and persistent personalization. Explores implementation strategies for creating adaptive AI agents that learn and forget contextually.
AI Agents
New research introduces adaptive data flywheel methodology using Monitor-Analyze-Plan-Execute control loops to systematically improve AI agent performance through continuous feedback and refinement.
AI Agents
New research proposes integrating neural networks, symbolic reasoning, and causal inference into a unified architecture for AI agents that can handle complex multi-objective tasks with improved robustness beyond traditional prompt engineering.
AI Agents
Researchers propose machine-readable policy cards for governing autonomous AI agents at runtime, enabling standardized constraint enforcement and safety guardrails as AI systems gain more autonomy.