Best of Langchain — December 2025

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   Article

   LangChain·23w

   Agent Engineering: A New Discipline

   Agent engineering is an iterative discipline for building reliable LLM-based agents in production. It combines product thinking (prompt writing, defining scope), engineering (building tools, infrastructure, UI), and data science (evaluation, monitoring, analysis) in a continuous cycle of build, test, ship, observe, and refine. Unlike traditional software, agents handle unpredictable natural language inputs and non-deterministic behavior, making production deployment essential for learning what actually works. Successful teams treat shipping as a learning mechanism rather than an end goal, using tracing and evaluation to systematically improve agent reliability through rapid iteration.

   160
2. 2
   Article

   Machine Learning Mastery·24w

   The Roadmap for Mastering Agentic AI in 2026

   A comprehensive learning path for building autonomous AI systems that can plan, reason, and act independently. Covers foundational mathematics and programming, machine learning fundamentals, autonomous agent architectures, specialization areas like robotics and workflow automation, deployment strategies using Docker and cloud platforms, and portfolio development. Includes curated resources from beginner prerequisites through advanced topics like multi-agent systems, transformer-based decision-making, and reinforcement learning with human feedback.

   57

   4
3. 3
   Article

   The New Stack·21w

   Java Developers Get Multiple Paths To Building AI Agents

   Java is emerging as a strong contender to Python for enterprise AI development, with multiple frameworks now available for building AI agents. LangChain4j, Embabel, Koog, and Crew4J provide Java developers with tools for integrating LLMs and building agentic systems. While Python remains dominant for research and prototyping, Java's advantages in scalability, performance, concurrency, and enterprise integration position it well for production AI deployments. Industry analysts and framework creators predict Java could match or surpass Python for enterprise AI within 18 months to three years, particularly as the technology matures and production requirements become more critical.

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