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.
Three Weeks of Letting Antigravity Run wrangler tail Across Six Cloudflare Workers
I run six Cloudflare Workers in parallel — four Lab sites and two storytelling blogs — and the morning log review was quietly eating an hour and a half every day. Here is what changed when I handed wrangler tail interpretation to Antigravity's Inline Agent for three weeks, what I trusted it with, and what I kept for myself.
Replay-Driven Agent Design — Time-Travel Debugging for Production AI Agents
Reproduce one-off agent failures from production on your laptop. A practical three-layer replay design — event, state, and decision — built on top of Antigravity's Manager Surface, with TypeScript code you can drop into your own stack.
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 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 Antigravity Agent Traces That Tell You Why It Failed — Observability in Practice
Run Antigravity agents long enough and unreadable failure logs pile up fast. This piece walks span structure, attribute design, failure tagging, dashboards, cost visibility, and retry policy — backed by six months of production metrics — so you can cut post-incident debugging time in half.
Observing Antigravity AI Agents with Langfuse — A Practical Setup Before You Ship
Before you push an Antigravity AI agent to production, wire up Langfuse so you can actually see traces, token spend, and cost. A hands-on guide with real Python code and lessons from the field.
Antigravity × Prometheus + Grafana — Build an Application Monitoring Stack with AI Agents
Learn how to build a production-ready application monitoring stack with Prometheus and Grafana using Antigravity's AI agents. Covers metrics collection, alert rules, and dashboard creation step by step.
Assembling Observability with Antigravity × OpenTelemetry — Instrumentation, Sampling, Cardinality, and AI Anomaly Detection in Production
A record of running OpenTelemetry in production on a Node.js backend: measured instrumentation overhead, tail sampling configuration, avoiding cardinality explosion, and operating AI anomaly detection behind an approval gate.