Context Window is the daily AI brief for applied AI builders. Subscribe free →
Feature
DIY Decision Models: A Shift in AI Infrastructure
Building your own decision model is gaining traction as developers seek more control over AI workflows.
Why it mattersThe shift towards custom decision models reflects a growing demand for AI systems that are tailored to specific tasks, offering developers more control and optimization opportunities. This trend reveals new challenges in model design, integration, and infrastructure support.
Read full article →Sign up for the daily AI brief
Each morning: models, tools, research, and conversations distilled from across the web — with why they matter for builders.
Around the Web
Nicolas Cage vs. AI
AI Agents in Action
Within the same harness, agents get inter-agent messaging by default. But what happens when you want two agents on two different harnesses (and hosts) to communicate? When on the same host, I tried having them both leav…
r/AI_Agents
Been reviewing a lot of agent-written PRs lately and honestly the hardest part is that everything looks finished. Tests are green, the description sounds confident, and it’s really easy to just merge. Few things that he…
r/AI_Agents
I recently talked with a few people who are building agents, and their use cases are showing some common patterns. A few examples of what they are building: 1. Spin up an agent read team to attack the agent under test t…
r/AI_Agents
Tools
Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right…
github | stars 32,316
为 DeepSeek Harness (DSH) 插件生态打造的现代化桌面端解决方案。万物皆「插件」，桌面本身也是「插件」。
github | stars 30,441
Build
Jev is TypeSafe AI’s first System One Model. It is 100x faster and cheaper than frontier LLMs.
blog.bytebytego
Research
Agents are deployed in applications from trip planners and stock trading to IT incident triage. In most cases, LLM agents work autonomously with minimal rule-based safeguarding, leading to cost and safety issues from ir…
arxiv
When retrieved evidence contradicts an agent's prior beliefs, does it revise its answer, acknowledge uncertainty, or persist with an incorrect conclusion? Existing evaluations of agentic systems focus primarily on task…
arxiv
Adversarial games have driven advances from heuristic search to reinforcement learning, yet learning and adapting strategies from limited samples remain challenging. AI agents offer an alternative by turning game experi…
arxiv
Playbooks
Most AI teams focus on the wrong things. Here’s a common scene from my consulting work: AI TEAM Here’s our agent architecture – we’ve got RAG here, a router there, and we’re using this new framework for… ME [Holding up…
hamel
Part 4 of Build your own claw and agent harness with Microsoft Agent Framework. Over the last three parts our personal finance assistant grew from a single tool into a genuinely capable agent: it plans, reads your portf…
devblogs.microsoft
See how LangGraph orchestrates Jev, TypeSafe AI's decision model, to build faster, cheaper production agents.
langchain
News
Agents Unpacked • Chapter 1: Tools let a system act, but who chooses what happens next?
thepythoncodingstack
This post shows you how to configure an AI model to perform structured, evidence-based vulnerability triage with the consistency of a seasoned security analyst. You’ll learn the design decisions behind five configuratio…
aws.amazon
Run AI agents like containers. Define them in YAML, share them through any OCI registry, and run them anywhere.
docker.github