The Stack Behind Long-Running AI Agents

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The Stack Behind Long-Running AI Agents

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Meta's Muse Wonder puts persistent memory and error recovery at the centre of its latest agent models, raising the stakes for long-run reliability and private context. We also examine Microsoft's Maia 300 inference plan, Britain's preparations for autonomous-AI incidents, Google's Data Agent Kit, AMD's Helion rack design and Mistral's managed workflow workers. In What Changes for You: urgent GitLab patches for self-managed installations, and the checks Kiro CLI users should make before V3 becomes the default. Full transcript: https://www.owenonthenet.com/podcast/2026-10-13/ Sources: https://research.meta.ai/blog/muse-wonder https://www.axios.com/2026/10/12/meta-new-ai-models-anthropic-openai https://blogs.microsoft.com/blog/2026/10/12/microsoft-to-expand-azure-maia-system-to-multi-gigawatt-deployments https://www.gov.uk/government/news/britain-steps-up-national-defences-against-growing-ai-risks https://cloud.google.com/blog/products/data-analytics/bringing-you-the-data-agent-kit https://github.com/GoogleCloudPlatform/data-agent-kit-plugin https://ir.amd.com/news-events/press-releases/detail/1301/introducing-amd-helion-agentic-ai-compute-at-rack-scale https://docs.mistral.ai/resources/release-notes https://docs.mistral.ai/studio/workflows/managing-workflows-in-production/managed-deployments/how-it-works https://docs.gitlab.com/releases/patches/patch-release-gitlab-19-4-2-released https://www.cert.europa.eu/publications/security-advisories/2026-016 https://kiro.dev/blog/cli-3 https://kiro.dev/docs/cli/v3

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