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.
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.
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.
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
How to add OpenTelemetry spans to your own agent loop so a failure can be read in a few lines. Covers span structure, what to keep and drop in attributes, a failure_class taxonomy, a retry table, three dashboard views, cost attributes, and PII handling — with code that runs.
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.
When the Deploy Was Green but Users Still Saw the Old Build: Field Notes on a Gate That Verifies Your Shipped Commit Reached the Edge
When a deploy reports success but production keeps serving the old build, a post-deploy gate that verifies your shipped commit actually reached the edge via a build stamp closes the gap. Field notes with real operational numbers.
Pour Your Unattended Run Logs into SQLite and Count Failures Across Every Run
Running Antigravity CLI on an unattended schedule leaves a growing pile of JSON Lines logs. Instead of grepping files, I pour them into a tiny SQLite database and answer 'which step fails, and when' with a single query. Here is the ingest script, six diagnostic queries, and six weeks of real numbers.
When A2A Tasks Sat Stuck in 'working' — Field Notes on Tracing State Transitions to Detect Stalls
Antigravity's A2A coordination fails less by crashing and more by sitting in 'working' without progress. This walks through recording state transitions as one timeline and naming a stalled task down to which agent and which edge froze, along with what running agents daily has taught me.
When Successful Automation Quietly Stops Earning Its Keep: Designing for Value Decay and Retirement
A dashboard full of green success logs often hides the most dangerous kind of failure. Here is a design — with working code — for surfacing automations that keep succeeding while producing no value, and deciding whether to retire, repair, or keep them.
Keep a Tamper-Evident Audit Log of Your Autonomous Agent's Actions
To record the decisions and actions an Antigravity agent takes autonomously in a form you can trace and verify later, design an append-only audit log whose hash chain detects tampering. Includes the implementation.
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.