Your Antigravity LLM App Drifts on Cost and Quality While the Dashboard Stays Green — Instrumentation Field Notes
Watching only total cost and latency hides the slow drifts that hurt. These are field notes on attributing telemetry by feature, tenant, and prompt version so you catch quality regressions and cost spikes early.
Tracing Parallel Agents After the Fact: Observability with Structured Logs and Spans
Running multiple agents in parallel on the Antigravity 2.0 desktop makes it impossible to tell which one is doing what. I share an observability design that drops tangled print debugging for run_ids and spans you can trace afterward, with a solo-operator implementation and numbers.
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.
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.
Forensic Audit Trail for Antigravity Background Agent — Cloudflare R2 logging that reconstructs decisions six months later
How to design a decision-log audit trail for long-running Antigravity Background Agents so you can still reconstruct why an agent did what it did six months later — schema, R2 write layer, PII masking, and Polars queries.
Record & Replay for Antigravity Agents — A Production Pattern to Reproduce Failures in 3 Minutes
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.
Catching "Running but Doing Nothing" Antigravity Subagents — A 3-Layer Observability Pattern Across Six Production Apps
Running Antigravity subagents across six production apps surfaced more than ten silent failures every month — exits clean, logs green, but nothing actually happened. Here is the Heartbeat / Output Trace / Decision Log pattern I now use to catch them inside 60 seconds, with code, GCP costs, and four months of running numbers.
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.