Antigravity Agent Shadow Mode Production Rollout Guide — A Safer Way to Test New Versions
How to safely roll out new versions of an Antigravity AI agent by mirroring real production traffic to the new version without affecting users — design, implementation and rollout playbook.
An Observability Blueprint for Antigravity Agents in Production
A definitive guide to designing observability for AI agents running on Antigravity in production. Presents a practical framework that unifies traces, metrics, and outcome logs.
Building Validation Loops in Antigravity Agents: 3 Patterns for AI-Verified Output
Make your Antigravity agents verify their own work. This guide walks through three practical validation loop patterns — Self-Verifier, Critic-Approver, and Test-Driven — with working code examples in AgentKit 2.0.
Giving Antigravity Agents Safe Write Access — Production Permission Boundary Design
A practical guide to designing Permission Boundaries that let AI agents safely touch production databases, deploys, and billing APIs — with dry-runs, approval queues, and audit logs.
Implementing Agent-to-Agent Communication in Antigravity Using the A2A Protocol
A practical guide to wiring agents together with the A2A protocol on Google Antigravity. Covers the design rationale, endpoint shapes, authentication, error handling, and the production-readiness checklist I run through.
Antigravity × Local LLM (Ollama / LM Studio / LM Link): A Production Connection Playbook
Getting Antigravity to connect to Ollama, LM Studio, or LM Link is the easy part. Running on that setup for eight hours a day, week after week, surfaces disconnects, model-swap hangs, stale sessions after lunch, and VRAM pressure from other processes. This playbook covers hardening the connection layer, picking the right backend for the task, and designing the fallback path for when local goes silent.
Production Multi-Agent Systems with Antigravity AgentKit 2.0: Patterns, Failure Modes, and What the Demos Don't Show
AgentKit 2.0 makes multi-agent systems look effortless in demos, but running them in production is a different problem. This guide covers Planning vs. Fast mode, three real orchestration patterns, and the failure modes — infinite loops, cost blowouts, prompt injection — that bite on day one.
Semantic Caching for LLM Responses in Antigravity — Estimating Your Own Savings Before You Build It
Building a semantic LLM response cache with Antigravity, pgvector, and Gemini. Covers migrating off the retired text-embedding-004, normalizing truncated vectors, and a formula that turns hit rate and unit-cost ratio into an actual savings estimate.
Cutting Antigravity Agent Costs in Half Without Sacrificing Quality — A Practical Optimization Playbook
Running Antigravity agents full-time can drive your API bill up fast in the first month. Across four sub-agents in my own production setup, I cut monthly token consumption almost in half while keeping quality identical. Here is the before/after breakdown, the model-routing break-even, and the mistakes I made along the way.
Antigravity × Firebase Genkit: Building Production AI Apps—From Flow Design to Deployment
Field notes from building production AI apps with Firebase Genkit and Antigravity: type-safe flow design, RAG pipelines, streaming, schema changes on deployed flows, and deployment—all with working code.
Antigravity AI Agent Design: Multimodal Production Implementation Patterns
A complete guide to building multimodal AI agents with Google Antigravity (Gemma 4). Covers image+text integration, Function Calling, async batch processing, state management, error handling, and cost estimation — with production-ready code.
Google Antigravity Python SDK Production Masterguide: Multimodal, Agents, and RAG Pipelines from Design to Deployment
The complete guide to using the Google Antigravity Python SDK in production. Covers multimodal input, tool calling, RAG pipelines, streaming, cost optimization, and Cloud Run deployment with working code examples.