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O'Reilly Radar — AI/ML

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Stop Getting Good at Protocols. Get Good at Agent Experience. Nouveau

Stop Getting Good at Protocols. Get Good at Agent Experience.

In 2025, if you weren’t building with MCP, you weren’t serious about agents. The Model Context Protocol dominated the agent conversation for the better part of the year. Conference talks, roadmaps, hiring plans, all of it revolved around MCP. Then late 2025 into 2026, AI Skills arrived and the backlash was immediate. Engineers declared MCP […]

O'Reilly Radar — AI/ML
Principal Drift Récent

Principal Drift

Over the past year I’ve reviewed enterprise agent architectures at roughly two dozen organizations, including banks, retailers, healthcare systems, and a couple of regulators. The architecture diagrams have been reliably impressive. There are boxes for the MCP gateway, the tool registry, the vector store, the orchestrator, the policy engine, and the observability stack. There are […]

O'Reilly Radar — AI/ML
Loop Engineering Récent

Loop Engineering

The following article originally appeared on Addy Osmani’s blog and is being reposted here with the author’s permission. Loop engineering is replacing yourself as the person who prompts the agent. You design the system that does it instead. A loop here can be thought of as a recursive goal where you define a purpose and […]

O'Reilly Radar — AI/ML
This Week in AI: Fable 5, the Clone Wave, and Uber’s AI Reality Check

This Week in AI: Fable 5, the Clone Wave, and Uber’s AI Reality Check

This week, egghead.io cofounder John Lindquist joined host YK Sugi, founder of CS Dojo and developer experience manager at Eventual, to cover the latest AI news. First on the agenda was the contested release of Claude Fable 5. They also examined the financial shifts reshaping the technology industry, including the rising costs associated with agentic […]

O'Reilly Radar — AI/ML
The Case Against Building Your Own Agent Platform

The Case Against Building Your Own Agent Platform

You know the meeting. The board wants an AI agent strategy by end of quarter. Someone on the leadership team has read a McKinsey report. You’ve been voluntold to build the platform. The slide deck says “AI-native.” The acceptance criteria are vague. Somebody mentions LangGraph, and somebody else says, “We’ll just wrap it ourselves.” You […]

O'Reilly Radar — AI/ML
This Week in AI: The Next-Gen Recommendation Experience

This Week in AI: The Next-Gen Recommendation Experience

This week Miguel Fierro, a former Microsoft principal researcher who recently founded his own company, RecoMind, joined data and AI evangelist Christina Stathopoulos to talk about the state of recommendation systems. Christina also ran through the latest AI news she’s been watching, from Anthropic’s continued rise to responsible AI, announcements from Google’s I/O 2026 conference, […]

O'Reilly Radar — AI/ML
When Context Collapses: Teaching Agents to Detect and Recover from Lost Memory

When Context Collapses: Teaching Agents to Detect and Recover from Lost Memory

This is the eighth article in a series on agentic engineering and AI-driven development. Read part one here, part two here, part three here, part four here, part five here, part six here, and part seven here. “640K ought to be enough for anybody.”—Bill Gates (allegedly) If you’re building AI agents that do complex, multistep work, you’re going to run into context […]

O'Reilly Radar — AI/ML
The PM’s Playbook for Shipping AI Features That Actually Work in Production

The PM’s Playbook for Shipping AI Features That Actually Work in Production

The demo to production Death Valley If you’ve worked on an AI feature, you know the feeling. You start building something that you are excited about, set launch timelines. The model spits out a perfect response, the prototype works magically, and everybody in the room is mentally calculating how big this product will be when […]

O'Reilly Radar — AI/ML
Long-Running Agents

Long-Running Agents

The following article originally appeared on Addy Osmani’s blog and is being reposted here with the author’s permission. A long-running AI agent can keep making progress over hours, days, or weeks. It can do this across many context windows and sandboxes, recover from failure, leave structured artifacts behind, and resume where it left off. For […]

O'Reilly Radar — AI/ML
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