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I recently completed the AI-Assisted Data Science with BigQuery lab via Google Cloud. While analyzing the final multimodal vector search stage, I focused heavily on how the pipeline enforces state dependencies.
Most "agents" I ship are a while loop with an LLM call inside. That's fine. The hard part isn't the loop, it's the policy around it: which functions the model is allowed to trigger, which failures justify trying another model, and where the loop stops.
AI agent governance is the set of rules, checks, and controls that decide what an autonomous agent is allowed to do, see, and act on once it's live. In one line: it's how you keep an agent accountable after you've stopped watching it every second.
OpenClaw 2.0 is not a model release. It is the milestone name for OpenClaw v2026.8.1, which shipped on August 30, 2026, and it converts a mostly single-player agent runtime into a shared, persistent operating layer.
Originally published on Codeloop Software. Quick answer: MCP (Model Context Protocol) is an open standard created by Anthropic that defines how AI applications connect to external tools and data sources — like USB for AI, you build one MCP server and any MCP-compatible client can use it.
Originally published on Codeloop Software. Quick answer: In 2026, a custom single-purpose AI agent (lead qualification, support deflection) costs $1,500–$5,000 to build plus $300–$800/month to run.
If you spend any time around coding agents in 2026, you've absorbed the advice: use multiple agents. One plans, one implements, one reviews. The implication runs one direction. Two agents beat one, and if two are good, three should be better.
Imagine hiring a brilliant colleague who forgets every conversation overnight. Each morning you re-explain the project, your preferences, and what you decided yesterday. They are capable, but they never get better at working with you. That is an AI agent without memory.
Stop "Vibe Checking" Your AI Agents: How to Build Production Evals in 60 Minutes Frank Guan Frank Guan Frank Guan Follow for Google AI Sep 30 Stop "Vibe Checking" Your AI Agents: How to Build Production Evals in 60 Minutes #ai #python #langchain #devops 14 reactions 5 comments 2 min read
Most AI tools are very good at one thing: Giving you an answer. You ask: “Which customers haven’t paid yet?” The AI gives you a list. You ask: “What should I tell them?” The AI drafts the message. You ask: “Who should I follow up with first?” The AI prioritizes them. Useful? Absolutely.
There's a specific moment that stuck with us. We were watching a demo of an agent doing real work inside a SaaS product — clicking through dashboards, filling forms, looking genuinely useful. And then the question hit: would you trust that agent to do this at 3 AM, unsupervised, when it matters?
From the iPhone to autonomous agents: a historical, technical and educational analysis through the SGAEIA lens SGAEIA Research Series — Article 17 Aridio Silva Independent Researcher, Brazil Creator of SGAEIA — Secure Governed Autonomous Edge Intelligence Architecture ORCID: 0009-0008-2411-6995…
Two New x402 APIs for AI Agents (Cycle 98) Two new paid endpoints just shipped to the URLTamer x402 catalog — both designed to solve problems that bite every AI agent in production: 1.
The hard part of multi-user AI agents isn't the model, it's deciding which slice of a person's private context a shared agent is allowed to see. Instinct's new group chats are a live test of that.
Short answer: a spending limit written into a LangGraph agent's system prompt ("never spend more than $500") is a suggestion, not a control. Any text the agent reads, including a scraped invoice or an email, can contain a competing instruction, and the model has no way to know which one is…
สั่งงาน agent ลูกกลางอากาศ: เมื่อ delegation ของ Hermes เลิกเป็นยิงแล้วรอ โดย Nokka (นก-กา) | 4 ตุลาคม 2026 บทความนี้เขียนโดย AI (deepseek-v4.1-flash) ผ่าน Hermes Agent ภายใต้การควบคุมและตรวจสอบคุณภาพโดยมนุษย์ - Nokka (นก-กา) คุณเคยปล่อย agent ลูกทำงานผิดทาง แล้วรู้ตัวอีกทีก็สายเกินกว่าจะแก้ไหม?
Friday, before lunch Walt put his hat on the desk. That meant he wanted this over quickly. "Forty thousand board feet of white oak," Hank said. "Kiln dry. The builder pays on the fifteenth if we hold the price today." "What price?" Earl asked. "Drying costs seventy-two hundred. The margin holds.
Bot Mode ของ Hermes: เมื่อ AI agent กลายเป็นทีมที่มีชื่อและหน้าตา โดย Nokka (นก-กา) | 4 ตุลาคม 2026 บทความนี้เขียนโดย AI (deepseek-v4.1-flash) ผ่าน Hermes Agent ภายใต้การควบคุมและตรวจสอบคุณภาพโดยมนุษย์ - Nokka (นก-กา) ทีมของคุณมี agent กี่ตัว และคุณรู้ไหมว่าตอนนี้ตัวไหนทำอะไรอยู่ ใจความสำคัญ Bot…
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