Antigravity AI Agent Not Working: Troubleshooting Guide
Quick, practical steps to diagnose and fix issues when an antigravity AI agent not working. Learn common causes, actionable fixes, and best practices to restore operation fast.
If antigravity ai agent not working, start with a quick triage: verify API keys and credentials, confirm network access, and inspect the agent state for errors or stalled tasks. In most cases, misconfigurations, expired tokens, or paused workflows cause the problem, and these are fixable within minutes with the right checks. Also review recent config changes, dependency versions, and any quota limits. If the issue persists after these checks, proceed with deeper diagnostics.
Overview: what it means when the antigravity ai agent not working
When an AI agent stops performing as expected, you’re often looking at a mismatch between configuration, credentials, and the runtime environment. The phrase antigravity ai agent not working signals issues across connectivity, authentication, and task orchestration. According to Ai Agent Ops, the most common culprits are misconfigured prompts, expired tokens, or a paused workspace. The goal of this section is to frame the problem and set expectations: most fixes are quick, staged, and reversible. You’ll gain a clear mental map of where to look first, then move into concrete checks that avoid guesswork. By understanding the typical failure modes, you can reduce mean time to recovery and keep agent loops healthy for your customers and teams.
Root causes at a glance
Most agents fail for one of several reasons: authentication failures (invalid keys or revoked access), network or endpoint issues (DNS, firewall, or proxy problems), configuration drift (recent changes not propagated), or quota limits (rate limits or resource ceilings). In many cases, the issue is only one small misconfiguration or a transient outage. Ai Agent Ops analysis shows that diagnosing the exact cause often requires correlating logs, metrics, and recent deployment records. Keep a calm, methodical approach and document each hypothesis as you test.
Environment and configuration checks
The environment is the fabric that supports your agent. Start by validating that the runtime environment matches the deployment manifest and that any required services (or microservices) are reachable. Check the agent’s startup logs for initialization errors and verify that environment variables are correctly loaded. Ensure that any feature flags or conditional workflows are in their intended state. If you recently updated dependencies, confirm compatibility with the current agent version. This stage prevents chasing ghost issues and sets up reliable baseline conditions for deeper diagnosis.
Data, models, and dependencies
An antigravity ai agent not working can stem from data mismatches or unavailable models. Confirm that input schemas align with what the agent expects, that any required models are loaded and reachable, and that there are no stale caches that could feed obsolete prompts or responses. Verify that prompts and templates haven’t changed unexpectedly and that input data isn’t violating schema constraints. If you rely on external data sources, test their availability independently to avoid cascading failures.
Authentication: API keys, tokens, and access control
Expired or revoked credentials are a frequent cause of downtime. Confirm that API keys, OAuth tokens, and service accounts are valid, not expired, and scoped correctly for the agent’s operations. Rotate credentials only using the approved workflow, and update all dependent services in tandem. Keep credentials out of logs, and enable rotation notifications so you’re alerted before tokens fail.
Network and hosting considerations
Network health often explains intermittent failures or complete outages. Check connectivity to required endpoints, DNS resolution, TLS certificates, and firewall rules. If your agent runs behind a proxy, verify proxy configuration and authentication. Latency spikes and regional outages can disrupt streaming or real-time tasks; ensure load balancers, ingress routes, and uptime checks reflect expected performance. Consider routing health checks to a dedicated status endpoint to surface any bottlenecks quickly.
Agent state, workflows, and orchestration
Sometimes the agent is healthy, but the workflow it orchestrates is stuck or misrouted. Inspect task queues, worker status, and any stateful stores (databases, caches). Look for stalled tasks, deadlocks, or out-of-order events. Confirm that the latest workflow definitions are deployed and that versioned tasks are not mismatched with their orchestrator. If a workflow is paused or paused by policy, re-enable it with a controlled test.
Recovery, rollback, and verification
When you fix root causes, validate recovery with a controlled test: run a minimal scenario, capture logs, and compare results with expected outcomes. If issues persist, consider a rollback to a known-good configuration or older agent version. After restoration, run end-to-end tests and monitor key metrics (latency, error rate, and throughput) to confirm stability before returning to production workloads.
Prevention: monitoring, alerts, and best practices
Preventing the next outage requires proactive monitoring and a disciplined change-management process. Establish health checks for credentials, endpoints, and dependencies; instrument detailed logs and traces; and set rate-limit safeguards with exponential backoff. Maintain a runbook for common failures and practice regular disaster-recovery drills. Documentation of changes, plus automated alerts, reduces MTTR and helps teams respond faster in future incidents.
Steps
Estimated time: 30-45 minutes
- 1
Verify prerequisites and health endpoints
Check that all required services are reachable and that the agent’s health endpoint reports OK. Confirm time synchronization across components to avoid clock drift affecting tokens or time-based queries.
Tip: Use automated health probes and store their results for trend analysis. - 2
Reauthenticate credentials
Refresh API keys and tokens in the credential store, then rebind them to the agent. Validate that the credentials are correctly loaded by the agent during startup.
Tip: Rotate credentials using the official secret-management workflow. - 3
Check and restart agent process
Restart the agent process and observe startup logs for initialization errors. If you’re in a distributed setup, restart related workers to flush stale state.
Tip: Graceful restart reduces risk of data loss. - 4
Inspect logs and traces
Search logs for errors or stack traces around the failure time. Correlate traces with request IDs to pinpoint where failures originate.
Tip: Enable verbose logging temporarily to capture context. - 5
Validate workflows and prompts
Ensure that the invoked workflows, prompts, and templates align with the current agent version. Look for recent changes that could desynchronize inputs and outputs.
Tip: Lock workflow definitions to tested versions during fixes. - 6
Run a controlled test
Execute a minimal scenario that exercises the failing path. Compare results to a baseline and monitor latency and error rates in real time.
Tip: Document outcomes for future reference and audits.
Diagnosis: Agent fails to respond or completes tasks with errors
Possible Causes
- highExpired or invalid API credentials
- highNetwork connectivity issues or DNS resolution failures
- mediumConfiguration drift after recent changes
- lowRate limits or quota exhausted
Fixes
- easyValidate and refresh API keys, tokens, and credentials used by the agent
- easyTest network connectivity to all required endpoints; ensure DNS, firewall, and proxies allow traffic
- mediumReview recent config changes; rollback or lock-step update to known-good state
- mediumExamine rate limits/quotas; increase limits or implement backoff retry logic
Questions & Answers
What is the first thing to check when antigravity ai agent not working?
Begin with credentials, network access, and the agent’s health state. These basics catch the majority of outages quickly. Review recent changes if the basics pass.
Start with credentials, network access, and the agent’s health. If those are okay, check recent changes.
Can expired tokens cause the agent to stop functioning?
Yes. Expired or revoked tokens prevent authentication, halting all calls to required services. Renew and verify the token scope to restore operations.
Yes. Expired tokens stop the agent from calling needed services.
Where should I look in logs to diagnose the issue?
Check startup logs for initialization errors and runtime logs for errors during task execution. Use trace IDs to correlate across services.
Look at startup and runtime logs, use trace IDs to trace problems.
How long should it take to recover after fixes?
Recovery depends on the root cause, but most credential and network fixes resolve within minutes. Validate with a controlled test to confirm stability.
Most credential and network fixes resolve within minutes; test to confirm.
When should I contact support or a professional?
If the issue persists after all troubleshooting steps or involves critical infrastructure, escalate to your platform’s support or Ai Agent Ops for expert guidance.
If it doesn’t improve after troubleshooting, contact support.
Should I rollback code or configuration changes?
Yes, if the failure started after a recent change, rollback to a known-good state and re-apply changes incrementally with monitoring.
If a change caused the issue, revert it and reapply gradually while watching effects.
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Key Takeaways
- Verify credentials and network first
- Check logs for actionable errors
- Implement retry with backoff and monitor quotas
- Document changes and monitor health
- Escalate to professionals if unresolved
