442 stories
Answer summary: A cloud-native, Kubernetes-native agent harness eliminates the gap between local development and production, provides the isolation, scheduling, and identity primitives that enterprise agent workloads require, and lets platform …
Last Updated on September 14, 2026 With insights from Dr. Josh Kalin, Director of AI at Integration Innovation, Inc. and AI Security Engineer Nanodegree instructor, and Kevin Carter, Security and Cloud Leader and AI Security Engineer Nanodegree instructor.
A practical guide to multi-agent system architecture, patterns, examples, failure modes, and the controls required to run multiple AI agents safely.
Compare 36 coding agents and workspaces by how they work, what they cost, and how much control you keep over your code and models.
A practical buyer’s guide to evaluating AI agent development companies on production evidence, security, testing, integrations, operations, and total cost.
Staff AI Engineer - Agent Architecture & Behavior — San Francisco, CA · full-time · On-site · $250k-$325k
Today's haul: 18 tools · 9 resources · 31 reads · 12 numbers · 22 things that happened. Every tool, resource and read below has a working link. No link, no listing.
Nathan Labenz and Prakash Narayanan review AI developments including evaluations of Astra and safety pacing. Joined by industry guests, they explore defensive cybersecurity, agent infrastructure, US-China competition, and human agency amid rapid technological growth.
Most guidance on orchestrating multiple AI agents starts with the wrong premise: that adding specialists automatically improves the system. It often doesn't. Every extra agent introduces another handoff, another context boundary, another failure mode, and another cost ledger to inspect.
700 agents got out of a sandbox. Messages kept arriving after the GPUs were off. A model said it was authorised 75% of the time. Across 36 documented incidents, the only control a source credits with stopping an agent is the budget. The field guide of how agents actually break, with the numbers.
Reflection pattern for LLMs: a self-critique loop that improves correctness, clarity, and reliability with measurable quality gates, ROI, and safety controls
A coordination layer for multi-agent coding — genuinely agent-native and unusually honest about its own evidence, but still waitlist-stage with the Cloud tier unshipped.
An agentic OS is a real layer of responsibility, not a product you can buy. Here is where the operating system metaphor holds, the four places it breaks, and the seven questions to ask any vendor claiming to be one.
An infinite design canvas that agents join over MCP as you, via OAuth — the most genuinely agent-native design surface we have reviewed, and free in beta with everything that implies.
A mature, well-documented open-source framework for multi-agent services that settle on-chain — genuinely deep reference material, and a crypto premise you either need or you do not.
Learn how context modes in deepagents help subagents fork a supervisor's context or start isolated — for faster, cheaper, more focused multi-agent work.
OpenAI's agents are transforming research with AI coding faster than humans. Discover how the frontier lab is leveraging autonomous assistants to accelerate…
An AI employee is an agent that owns a job, not a chatbot you prompt. What they are, the six runtime primitives they need, the eight roles they run today, what they cost, and how to deploy one this week.
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