Containing Failure in Antigravity Multi-Agent Systems: Three Boundaries That Stop Cascades
Antigravity multi-agent setups run beautifully in isolation but cascade in production, where one small failure drags the whole orchestration down. These notes organize the fix around three boundaries—layered control, trust separation, and observability with idempotency—down to the TOML and the correlation-ID wrapper.
Designing a 4-Tier Fallback Architecture for Antigravity Agents — Catching Model Degradation, API Outages, and Cost Overruns Across Layers
How to design a 4-tier fallback hierarchy for production AI agents on Antigravity, drawn from 24 months of running 11 agents across 6 indie apps. Includes the decision logic, code, and real demotion statistics.
Antigravity × Multi-Provider LLM Failover — A Production Guide for Gemini, Claude, and Local Gemma
When Gemini returns 503, does your agent stop responding? This guide walks through a production-ready router, circuit breaker, and graded fallback design, with code you can paste in today.
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.
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.
Resilient AI Agents in Antigravity — Retry, Circuit Breakers, and Fallback Strategies for Production
Build fault-tolerant AI agents in Antigravity with production-grade retry strategies, circuit breakers, model fallback chains, and checkpoint recovery. Every pattern you need to keep agents running reliably.
AI Agent Error Recovery Design: Building Pipelines That Don't Stop
Covers the typical failure patterns AI agents encounter in production and practical implementations of retry, fallback, and circuit breaker patterns to keep pipelines running.