Supervising Long-Running Antigravity Agents — Watchdog and Tiered Recovery
Eight weeks of running AdMob revenue optimization on Antigravity background agents revealed three quiet failure modes. Here is the watchdog plus tiered recovery design I landed on.
Record & Replay for Antigravity Agents
How to deterministically replay a failed Antigravity Agent run offline, drawn from a month of running it across four production sites. Covers boundary recording, R2 + KV storage costs, PII masking, and a working TypeScript harness.
Cost Attribution for Antigravity Agents — A Showback Architecture That Maps Execution Cost Back to Tenants Across Multi-Product Operations
A multi-tenant Showback architecture for Antigravity agents running across multiple products, with the schema, propagation patterns, and seven months of production numbers from running 4 sites and 6 apps in parallel.
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.
Designing Knowledge Freshness for Antigravity AI Agents — A Runtime Architecture for Model Cutoffs, Corpus Staleness, and Real-World Time Drift
Antigravity agents have to juggle three independent time axes — model cutoff, RAG corpus update, real-world clock — or they will confidently cite six-month-old documentation. Here is the runtime architecture I use, with working TypeScript code and the TTL thresholds I run in production.
Designing an HITL Approval Pipeline That Survives Production — Routing Probabilistic Actions Safely With Antigravity Agent
How I run a Human-in-the-Loop approval pipeline for Antigravity Agent in production — risk tiers, confidence-based routing, queue schema, audit logs, and the graduation criteria that move actions toward automation, drawn from six months of indie-app operations.
Using Claude Opus 4 / Sonnet 4 in Antigravity — Model Selection Strategy and Production Patterns
A practical guide to using Claude Opus 4, Sonnet 4, and Haiku 4.5 in Antigravity. Learn the decision framework and production implementation patterns for balancing cost, speed, and quality in real projects.
Tracking AI Agent Decisions in Antigravity: Implementing Decision Logs and Explainability
Learn how to design decision logging systems that capture why your AI agents make specific choices. Includes working Python code examples, Before/After patterns, and a quality improvement cycle for production Antigravity agents.
Canary Deployment with Auto-Rollback for AI Agents — Protecting Production with Antigravity and Burn-Rate SLOs
A practical playbook for shipping new AI agent versions through canary deployment on Antigravity, with automatic rollback driven by burn-rate SLOs. Includes a lightweight setup that solo developers can sustain.
Giving Your Antigravity AI Agents a 'Time Budget' — A Production Scheduling Design That Unifies Timeouts, Priorities, and Deadlines
Your AI agent's response time creeps up, users drop off, and unexpected costs pile on. This guide walks through giving Antigravity agents a single 'Time Budget' object that unifies timeouts, priority, and deadlines, drawing on Masaki Hirokawa's production experience.
Designing Confidence Scores for Antigravity Agent Outputs: Auto-Approve the Certain, Escalate the Ambiguous
Reviewing every Antigravity Agent output by hand does not scale. Attach a confidence score to each output, auto-approve the certain ones, and only escalate the ambiguous to humans. This guide walks through implementation and threshold calibration end to end.
The Self-Critique Architecture for AI Agents — Four Reflection Patterns That Make Antigravity Outputs Trustworthy
Four production-tested patterns for adding self-critique loops to Antigravity agents, with implementation code, stopping conditions, confidence calibration, and cost-control strategies.