A developer's test of two to-do list MCP servers with identical functionality found that one consumed nearly five times more input tokens due to poor tool design. The inefficient server returned incom…
A developer has built an asynchronous human-in-the-loop AI agent system using Spring AI that avoids blocking virtual threads during approval workflows. The approach serializes the agent's ReAct loop s…
Researchers introduced Reasoning Primitive Induction, a single-pass method that mines successful ReAct agent traces to extract recurrent reasoning moves into a compact library of typed pseudo-tools. T…
Researchers introduced ToolGate, a lightweight external controller that predicts whether to execute or skip tool calls proposed by vision-language agents, addressing the pre-call control problem where…
A developer detailed the architecture of agentic AI systems, building on the 2022 ReAct paper from Google Brain that enabled language models to reason, act, and revise their thinking in a loop. The po…
The article describes the Plan-and-Solve agent architecture, which improves upon the ReAct model by first generating a complete action plan before executing any steps. Unlike ReAct's locally optimal, …
This article is a practical, production-oriented guide to building AI agents, focusing on engineering patterns rather than hype or specific products. It explains why agent demos often fail in producti…
The article explains that AI agents often get stuck in infinite loops, repeatedly calling the same tool with slightly different queries, which wastes tokens and increases costs. This is identified as …
The article explains the ReAct (Reasoning and Acting) agent paradigm, introduced by researchers from Princeton and Google in 2022, which improves upon Chain-of-Thought (CoT) reasoning by allowing a la…