AI Agents
Production AI systems overwhelmingly favor deterministic workflows over autonomous agents. This analysis reveals the technical reasons behind this choice, examining reliability, error handling, and real-world deployment challenges.
AI Agents
Comprehensive technical guide to agentic AI design patterns, covering reflection mechanisms, planning strategies, tool integration, and multi-agent collaboration architectures that define autonomous AI systems in 2025.
agentic AI
Researchers propose a planner-centric framework that enhances how language models use external tools for complex reasoning tasks, showing improvements over the widely-used ReAct approach through better planning and execution separation.
AI safety
Researchers introduce CTRL-ALT-DECEIT, a novel benchmark for evaluating whether AI systems conducting automated R&D can engage in sabotage. The framework tests adversarial behaviors in AI agents with specific technical metrics.
agentic AI
New research reveals how LLM agents lose track of their identities when communicating with each other, creating 'echoing' behavior where agents converge on similar responses and lose their distinct perspectives.
agentic AI
A technical guide to constructing autonomous AI agents that plan, execute, and verify their own data operations using locally-deployed Hugging Face models, eliminating dependency on external APIs while maintaining robust performance.
agentic AI
New research proposes comprehensive framework for monitoring AI agent reliability across execution, including failure detection, root cause analysis, and automated recovery mechanisms for production deployment.
agentic AI
Small language models are outperforming larger counterparts in agentic AI workflows due to speed, cost efficiency, and specialized task performance. Technical analysis reveals why compact models excel at autonomous decision-making.
AI Agents
A technical walkthrough of creating an autonomous AI agent that can browse the internet, reason through tasks, and execute multi-step plans using Python, LLMs, and web scraping tools.
agentic AI
New research shows that incorporating procedural knowledge into agentic LLM workflows significantly improves performance. The study demonstrates how structured procedures enhance agent reliability and task completion rates.
LLM Agents
New research introduces grounded test-time adaptation for LLM agents, enabling dynamic learning during deployment by anchoring adaptations to factual knowledge and environmental feedback.
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.