Enterprise Evals 101
Imagine your worst nightmare: your agent passes every demo, then a customer asks something you never tested, and it fails in public.In April...
Topic
#production-ai
30 篇笔记
Software Factory Engineer: Job Description
An emerging engineering role for the transition from AI-assisted development to L4 Autonomous Software Factories.The RoleWe are looking for ...
What FDE means at OpenAI
What forward deployed engineering means at OpenAI
为什么我们需要 Agent 评测
Agent 评测已经成为了当前 Agent应用的基石。如果说年初大家都在关注 Harness，那目前的热点和深水区，一定是 Agent评测和 Agent Infra。我所了解到的是，基本每个做 Agent 产品的组都很看重评测的方法论以及效果。因此我专门开了一个系列，给大家详细介...
The 5 levels of AI agents. From a single prompt to a production agent (Complete course)
There are five words people throw around about agents right now. Context engineering, loop engineering, Jev engineering, harness engineering...
Your Agent Harness Should Repair Itself
Everything you need to understand how an agent harness can learn from production failures and repair itself. It covers procedural memory, tr...
Building the missing layer between AI spend and ROI
Author: René SultanAI telemetry tells us which models our agents use and what they cost, but not what we are paying them to do. We built a s...
Your Inference Server is Secretly a Learner: Open-Sourcing Reef for Continual Self-Improving Agents
1. Before all the “RSI” hype becomes realBefore today's excitement around RSI is fully realized, we want to open-source Reef, an infrastruct...
Agentic Kernels in Production
TL;DR: We’ve built an agentic kernel development framework that identifies model-level optimization opportunities, generates improved kernel...
How to Cut Agent Tokens by 2.7x (using an open harness)
Everything you need to understand where your agent's tokens actually go and what to do about it. It covers what a production harness owns be...
Codex as a platform: build on the open agent harness
Build Codex into the products and workflows your users already know.
An open source rival to Claude Managed Agents just launched
TrueFoundry's open source TrueForge challenges Claude Managed Agents with vendor-neutral model choice, claiming 50% lower agent costs and no lock-in.
Designing Loops for Production-Grade Work
What became standard practice after vibe-coding a production-grade BPE tokenizer trainer without reading a single line of code.
Optimizing for cost and intelligence
Balance cost and intelligence on the Claude Platform, with measured results for prompt caching, effort, model choice, budgets, and multi-model strategies.
How Production LLMs Reason Better at Inference Time (as researched by Google, OpenAI, and Anthropic)
8 techniques to make an LLM reason better at inference time, covered with tradeoffs and practical notes. Every one of them is running in pro...
Why model routing must be in the harness
Inference spend is rising as agents are deployed more widely, take on more complex tasks, and run longer autonomously. Enterprises are start...
Why managed agents are the next big thing in agent building
Today we’re launching Managed Deep Agents - the easiest way to build, run and deploy production agents. We have a full release blog and docs...
Why managed agents are the next big thing in agent building
Today we’re launching Managed Deep Agents - the easiest way to build, run and deploy production agents. We have a full release blog and docs...
所有想做 FDE 的人，都该读懂 Palantir 这套方法
大模型进入企业以后，最难的一步是把一家公司的数据、规则和权力关系，变成 AI 能够理解并安全执行的系统。2026 年 5 月，OpenAI 成立了一家专门负责企业部署的新公司。它准备投入超过 40 亿美元，并计划通过收购 Tomoro，一次带入约 150 名 Forward De...
Deep Agents vs LangChain vs LangGraph
Deep Agents, LangChain, and LangGraph each offer distinct approaches to building agents. In this post, we cover the key distinctions between...
最佳实践：从Multi-Agent到Agent Team
2w字长文，Agent Team 的最佳实践。
How Stripe Built Kai on Deep Agents in 1 Week
Learn how Stripe built Kai, a company-wide AI agent on LangChain, LangGraph, and Deep Agents, reaching 5,000 users in roughly 4 weeks.
Eval Engineering: build the gate that lets your agents merge without you (full 6-step course)
The end state is easy to describe. An agent finishes a change, opens it, and it goes in without a human reading it.Not because anyone decide...
Agency: Secure, scalable sandboxes for agents
This is part of our series on how we're AI-pilling Sierra.In our last few posts, we talked about what happens when every employee has a sing...
多模型路由进入 Agent 会话之后
模型路由在单轮问答中可以按难度分配成本，但进入 Agent 多轮会话后，还要处理上下文、工具状态、缓存和隐式状态。本文拆解会话级路由的智能与工程瓶颈，以及固定强模型、隔离子任务和有状态路由三条现实路线。
How we built our knowledge base
Authors: @hi_im_isaac_, @learnwdaniel, @gaozenghaonote: the interactive version of full technical blog available: https://www.cerebras.ai/b...
数据研发Multi-Agent架构的Harness工程实践
📝 摘要 本文基于阿里云数据研发团队的 Multi-Agent 实践，系统阐述了 Harness Engineering（智能体脚手架工程）的核心理念与落地方法。文章指出 AI 工程经历了 Prompt Engineering → Context Engineering → Harness Engineering 三代演进，并提出核心公式 Agent = Model + Harness：模型决定…
Agent 评测：方法论与体系设计
关于评测工程的体系化总结，本文或许能给出一些参考
15 AI Agent Design Patterns Every Engineer Must Know
Every team building AI agents hits the same wall.You start with one prompt and a few tools.It works. Then requirements grow. More edge cases...
Building Reliable Agentic AI Systems
AI helping pharmaceutical researchers query decades of information buried in PDF reports