LLM research
New research introduces embedded reasoning to improve how LLMs handle function parameters, addressing a critical bottleneck in AI agent reliability for tool-using applications.
AI Research
New research demonstrates AI agents can autonomously generate complete system software for deep learning, marking a significant step toward self-improving AI development pipelines.
AI Agents
eBay updates terms of service to prohibit unauthorized AI agents from making purchases, signaling e-commerce platforms are drawing boundaries around autonomous AI systems.
AI Agents
Researchers propose a software engineering framework for building AI agents that combines LLM capabilities with codified human expert domain knowledge for improved reliability.
AI Agents
IBM Research releases AssetOpsBench, a benchmark testing AI agents on realistic industrial asset management tasks, revealing gaps between lab performance and real-world deployment challenges.
AI Agents
AI agents analyzing video content face a critical challenge: goal drift. As they process complex visual data, they lose sight of original objectives, requiring new architectural solutions.
AI Agents
Learn how to isolate AI agent code execution with secure sandbox environments. This guide covers containerization, permission models, and safety patterns for autonomous AI systems.
AI Agents
Master the architecture behind intelligent AI agents with LangGraph's graph-based approach to state management, conditional routing, and multi-agent orchestration.
AI Agents
AI agents often fail after several steps due to error compounding and context degradation. Deep Agents architecture introduces new mechanisms to maintain coherence across extended task execution.
LLM
New research proposes proactive memory extraction for LLM agents, moving beyond static summarization to enable more dynamic knowledge retention and recall in autonomous AI systems.
AI Agents
Beyond prompt engineering, context engineering is emerging as the critical discipline for building reliable AI agents—managing what information models see, when, and how.
Agentic AI
As AI agents tackle complex multi-step tasks, traditional memory systems are hitting fundamental scaling limits. New architectural approaches are emerging to handle persistent context across extended workflows.