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How Teams Work Faster with Puppet AI

Can AI actually improve infrastructure operations? Without sacrificing control? In this webinar, see how teams use Puppet AI to understand infrastructure with natural language, reduce operational effort, and move from insight to action faster—all within trusted automation workflows. Watch a live demo of detecting and mitigating a real-world vulnerability, and learn how context-aware AI helps teams scale safely with built-in governance.

Claude Mythos pricing in 2026: Fable 5 costs, Mythos 5 costs, and what every model actually runs

Claude Mythos is now available to the public through Claude Fable 5, released June 9, 2026. Claude Fable 5 pricing is $10 per million input tokens and $50 per million output tokens, exactly 2x Claude Opus 4.8 ($5/$25). Claude Mythos 5 (the restricted Project Glasswing version) has identical pricing. Prompt caching cuts input spend by 90%. Batch API pricing is $5/$25 (50% off). In April 2026, Anthropic announced a model it said was too dangerous to release.

Agent Hooks + Chunk sidecars: Stop Broken AI Code Before It Hits CI

AI agents write code fast, but the feedback loop usually can't keep up. In this tutorial, you'll see how to wire Chunk sidecars into your agent's hooks so basic failures get caught before they ever reach your CI pipeline. We'll walk through the two hooks that chunk init writes automatically: Both hooks return exit 2 on failure, blocking the commit or keeping the turn open so the agent can fix its own mistakes with no manual prompting required.

Five Principles of an Accountable AI Agent Network: How to Evaluate Any Governance Platform

The first post in this series argued that AI agent governance hasn’t kept pace with deployment. The second laid out the five pillars of accountability, and what is required. The third walked through why network policies, API gateways, MCP/A2A protocols, DIY security patterns, and Role-based Access Control (RBAC) each leave critical accountability gaps. So what does good look like? The five pillars define what AI agent accountability requires.

A field guide to the agents in your cluster

You know every service in your cluster by name. You know which team owns each one, what it talks to, how it scales, where its logs go. The agents are a different story. That’s not a criticism, it’s an observation, and it’s one we keep running into. Every company we talk to is shipping agents of some kind, from scales of 10s to 1000s. Customer service bots that field tier-one tickets. Internal copilots that draft emails and summarise meetings and write the boring half of every PR.

Balance AI innovation and governance with Sumo Logic AI and ML apps

AI is changing how teams work. Developers are generating code faster, security teams are automating investigations, and employees across the business are using AI tools to accelerate research, content creation, and decision-making. But this adoption comes with a catch. As usage explodes, it introduces a new set of security risks: a rapidly expanding attack surface, faster attack timelines, potential data exposure, and an alarming lack of visibility into how these tools are being used.