Designing Production Incident Runbooks for Antigravity Agents: A Practical Framework from Detection to Recovery
A complete guide to designing incident runbooks for production Antigravity Agents — detection, triage, mitigation, and postmortem, with working code you can drop into your stack today.
Versioning and A/B Testing Prompts for Production AI Agents in Antigravity
Hard-coding prompts in production turns improvement into guesswork. This guide walks through registry design, A/B traffic routing, statistical promotion, and rollback — with code you can ship inside your Antigravity environment.
Building Self-Healing Antigravity Agents — Detection, Diagnosis, and Recovery in Production
A practical three-layer pattern for keeping Antigravity agents alive in production: signal-based detection, deterministic diagnosis, and graduated recovery — with full AgentKit 2.0 code and the production traps I learned the hard way.
Designing a Context Compression Sub-Agent in AgentKit 2.0 — A Production Pipeline That Stops Long-Running Tasks From Drowning in Their Own History
When you run an AgentKit 2.0 agent for hours inside Antigravity, response time and accuracy degrade together as the conversation history balloons. This premium guide walks through a production-grade dynamic compression sub-agent — schema, prompt, integration hooks, and an A/B evaluation harness — so you can decide rollout based on numbers, not gut feel.
Antigravity × Cloudflare Edge SaaS Architecture Guide: Production Patterns with D1, Workers, R2, and Workers AI
A complete production guide for building edge SaaS with Antigravity + Cloudflare D1, Workers, R2, and Workers AI. Covers real pitfalls including N+1 queries, CPU limits, and cost management.
Designing Antigravity Retry for Production — Idempotency, Backoff, Cost Ceilings, and Failure Triage
A production blueprint for Antigravity agent retry: idempotency, exponential backoff, token-budget ceilings, failure triage, and resumable checkpoints.
SRE for Antigravity Agents — Taming Probabilistic Systems with SLOs and Error Budgets
AI agents are probabilistic by nature, so running them in production without SRE thinking is risky. This guide applies SLIs, SLOs, and error budgets to Antigravity agents with working code, and shows why the textbook 14.4x burn-rate alert never fires on a loose SLO.
Keeping the Antigravity Python API Stable in Production — Retries, Timeouts, and Circuit Breakers That Actually Work
A deeply practical guide to keeping Python services built on the Google Gen AI SDK alive under real traffic. We cover retry, timeout, circuit breaker, rate limit, and cost budgeting patterns with runnable code from an Antigravity workflow.
Prompt Injection Defense in Antigravity — A Production Security Playbook for LLM Apps
Build a four-layer prompt injection defense inside Antigravity with working Python. Includes measured heuristic recall across eight obfuscations and the normalization bug that breaks detection for non-Latin scripts.
Three Safeguards Every Antigravity Python API Deployment Needs Before Production
Retries, timeouts, and circuit breakers — the three production safeguards you need around your Antigravity Python API calls, with working code for each.
Running Gemma 4 on a Self-Hosted vLLM Server from Antigravity — A Practical Guide to Building High-Throughput Inference
Gemma 4 runs beautifully in Ollama for a single user. The moment a handful of teammates start hitting the same endpoint, throughput collapses. This guide walks you through replacing Ollama with a self-hosted vLLM server, wiring it into Antigravity as a custom endpoint, choosing a quantization level with real numbers, and hardening the stack with rate limits and LoRA routing — end to end.
Prompts Are Assets: Building a Production-Grade Prompt Management Platform with Antigravity — Versioning, A/B Testing, and Quality Evaluation
A hands-on implementation guide for treating prompts as first-class code — with versioning, A/B testing, and automated quality evaluation. Design patterns and working code for running AI agents on Antigravity with safe, continuous prompt improvement.