AI Agents
7 focused explainers for readers who want the useful answer, a clear example, and a practical next step.
Claude for physical AI: connect language models to real systems cautiously
Anthropic highlighted work bringing Claude into physical-AI settings through an industry case study. Keep simulation, bounded commands, safety controllers, human override, and hardware interlocks outside the model.
Gemini Interactions API: design resumable agent conversations
Google positions the Interactions API as a primary interface for Gemini models and managed agents. Store interaction identifiers, tool results, approval state, and idempotency keys outside the UI.
Gemini Managed Agents: understand the remote Linux sandbox
Gemini Managed Agents can provision a remote Linux environment through an API call for tool use and web work. Treat each environment as untrusted compute with scoped inputs, secrets, egress, quotas, and cleanup.
Build AI Studio apps on Google Workspace data without oversharing
Google announced direct Workspace access for apps built through AI Studio. Use a dedicated test workspace, minimum scopes, per-user authorization, and visible data boundaries.
Multi-agent orchestration with Antigravity: avoid duplicate and conflicting work
Google describes Antigravity primitives including subagents, hooks, and asynchronous task management. Give each agent an owned output, dependency graph, budget, and merge rule.
Antigravity SDK: when to host an AI agent on your own infrastructure
Google announced an Antigravity SDK for using its agent harness while hosting on your own infrastructure. Choose self-hosting only when a named requirement justifies the operational burden.
Gemini computer-use agents: permission boundaries before automation
Google integrated computer-use capability into Gemini 3.5 Flash for desktop, mobile, and browser automation. Use test accounts, domain allowlists, confirmation gates, and reversible environments.