agent-engineering

LangChain Academy launches the first certification for the full Agent Development Lifecycle, offering a 50% discount for the first two months.

The article explains the 'Tool Rot Paradox' where installing many static agent skills causes context window degradation and security issues in production, and advocates for a dynamic discovery approach using a meta-skill that fetches tools on demand to keep the system prompt lean.

The author reflects on how long-running AI agents encounter failures unrelated to the initial prompt, arguing that environment design (tools, docs, validation, architecture rules) matters more. They discuss concepts like harness engineering, keeping AGENTS.md small, using linters, and evaluator agents, while noting the cost trade-offs.

This article argues that building AI agents is easy, but providing them with sufficient business context remains the hard and unsolved infrastructure problem, proposing the need for a dedicated 'context layer' akin to GitHub for code.

An engineer tracked his coding agent's token usage over a week, finding that only 0.67% of tokens were spent on actual tasks, with 99% consumed by tool directories, skill descriptions, and system prompts. He provides optimization strategies, including shell output filtering which saved 46.9% of tokens.

This article explores the trend of upgrading prompt engineering to Agent engineering, emphasizing structured context management of AI agents through methods like AGENTS.md, and shares a minimal closed-loop workflow methodology.

Recommending Harrison Chase's blog post on multi-agent system design. The key insight is that multi-agent systems split into 'reading' and 'writing' tasks; reading is easy to parallelize while writing leads to conflicts. It also summarizes suitable and unsuitable scenarios and engineering prerequisites.

Former OpenAI engineer Ryan Lopopolo joins Google Cloud as Chief Agent Engineer, bringing OpenAI's Agent engineering methodology to the cloud platform.

Microsoft open-sourced SkillOpt, a framework that treats agent skill documents as trainable artifacts, optimizing them through iterative feedback loops without modifying model weights. It is available as a Python library with support for self-evolving agent skills.

作者分享了在GIAC 2026深圳会议上关于Agent Engineering的演讲，强调构建可靠、有状态的代理的重要性。

A thread explaining the 5 core mental shifts needed to transition from traditional software engineering to agent engineering, emphasizing why conventional patterns like hard-coded routes and binary tests fail with AI agents.

The article criticizes attempts to reverse-engineer Fable 5 by copying surface behaviors, instead introducing Hephaestus Stormbreaker—a robustness control layer for coding agents that enforces scope locking, evidence loops, regression tests, and gate checks to prevent agent drift and early quitting.

The author discusses the unglamorous but critical aspects of engineering reliable AI agents in production, including monitoring mid-flight runs, resuming failed runs, and providing UI status, and asks the community about common pain points and off-the-shelf solutions.

PandaProbe Cloud offers a fully managed agent engineering service, as announced on Product Hunt.

An open-source learning roadmap project called Agent-Learning-Hub, which breaks down AI Agent learning into 8 stages from building a minimal Agent loop to production deployment, providing executable todo lists and recommended resources, maintained by members of the Datawhale community.

The tweet discusses the problem of bloat in AI agent harnesses, agreeing with Kaffu's critique that harnesses become "rich man's toys," and advocates for a composable architecture of small, replaceable workers to reduce drift and keep systems cheap and debuggable.

Recommends a free open-source technical book 'How to Build a 7×24 AI Agent from Scratch', systematically explaining AI Agent engine, digital human protocol, AI browser, production-grade scheduling and other practical content, based on the real 300,000-line open-source project Halo, and written in a human-machine collaboration manner.

This article summarizes four common pitfalls encountered when deploying AI Agents from demo to production: unreliable function calling, cumulative failure rate of multi-step tasks, improper memory management, and security permission issues, along with corresponding solutions.

The author introduces 'Autoharness', a tool that uses Claude Code to autonomously optimize agent harnesses by iterating on prompts and hyperparameters. This resulted in a 40% performance increase on the tau2-airline benchmark.

AG Kit is an Antigravity-first agent engineering kit providing rules, skills, specialist agents, workflows, persistent memory, MCP guidance, orchestration, and a native safety hook.