Agent Terminated: HTTP 503 MODEL_CAPACITY_EXHAUSTED on stateful sessions

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Hi Engineering Team and Community,

I am experiencing severe workflow disruptions due to constant 503 Service Unavailable errors on the Antigravity and Firebase Studio (prototyper) platform. I am doing full “no-code” development, relying heavily on the agent for deep architectural audits using a strict, high-density XML prompting protocol (which requires maintaining a precise, deterministic state).

Unfortunately, my agent keeps getting abruptly terminated mid-task. Here is the exact trace log I received today:

JSON

Trajectory ID: 252b74ca-69ad-48c1-abe4-bdf8cadbded2
Error: HTTP 503 Service Unavailable
Sherlog: 
TraceID: 0x1c94dee335faad7f
Headers: {"Alt-Svc":["h3=\":443\"; ma=2592000,h3-29=\":443\"; ma=2592000"],"Content-Length":["527"],"Content-Type":["text/event-stream"],"Date":,"Server":,"Server-Timing":["gfet4t7; dur=264"],"Vary":,"X-Cloudaicompanion-Trace-Id":["1c94dee335faad7f"]}

{
  "error": {
    "code": 503,
    "details":,
    "message": "No capacity available for model gemini-3-flash-agent on the server",
    "status": "UNAVAILABLE"
  }
}

The Architectural Issue: I understand that server-side traffic spikes and TPU capacity shortages happen. However, the core issue is how the Antigravity IDE handles these spikes.

Currently, when the backend throws this MODEL_CAPACITY_EXHAUSTED error, the IDE’s feature-management service instantly terminates the agent and destroys the active session context. For developers running long-lived, complex agentic workflows, losing the entire context tree due to a transient 32-second server overload is devastating.

My Questions for the Antigravity Team:

  1. Are there any plans to implement a resilient, graceful exponential backoff (e.g., pausing the agent’s execution and retrying automatically) directly within the IDE, rather than outright killing the session upon a 503?

  2. Is there an ETA for infrastructure stabilization regarding the gemini-3-flash-agent capacity pools ?

Having to constantly restart the agent and rebuild the context window manually is making the IDE unusable for professional, stateful agentic development. Any insights or workarounds from the engineering team would be greatly appreciated.

Thank you.

3

To give the engineering and SRE teams some context on why this specific 503 MODEL_CAPACITY_EXHAUSTED behavior is so destructive, I want to share my use case. I’m hoping this catches the eye of someone on the architecture team.

I am a solo developer working from a rural area with extreme hardware constraints. My daily driver is an Intel Celeron N3350 with exactly 4GB of RAM, running Debian 13 XFCE. Running local models, Docker clusters, or heavy traditional IDEs is physically impossible for me.

Because of this, I rely 100% on the Antigravity/Prototyper (Firebase Studio) platform to act as my “brain” and orchestrator for heavy industrial projects - specifically “Saabiste” (an automotive reverse-engineering agent for Saab modules) and algorithmic things.

To make this work, I use a “Zero-Waste” software architecture. I don’t use the agent as a conversational chatbot. Instead, I feed it strict, deterministic XML prompts. The agent analyzes my entire codebase and documentation, and outputs idempotent, strict XML deployment plans that I blindly apply.

The core issue: This protocol requires highly dense, stateful sessions.

I know the community is loud right now, and I don’t expect magic fixes to global compute shortages. But from an architectural standpoint: is there a platform or something to know what happen when the API returns a 503 ?

If any Googler or product manager is interested in seeing a hardcore edge-case of this being used for deterministic industrial architecture on ultra-low-end hardware, I’d be happy to share my XML protocols. Any insights on future resilience updates would be greatly appreciated !

4

Good morning, me too I rely a lot on zero approach for my AI Model project. Every time I try to work with the agent it can’t do a single request it crashes immediately saying: “Agent terminated due to error. You can prompt the model to try again or start a new conversation if the error persists. See our for more help.”

5

Hey,

I feel your pain. To be honest, I wouldn’t get my hopes up too high regarding a direct fix or a response from the engineering team. We have to face the reality of the tier-based priority system: in a world of limited compute, resources will always be prioritized for paying customers. It’s the “price” we pay for using these services for free, and we have to accept being at the bottom of the queue.

Since I’m in the same boat, here is how I manage to keep my projects moving despite the 503 errors:

  • Strategic Scheduling: I’ve noticed the load is much lower during off-peak hours. I usually run my heavy sessions late at night or very early in the morning (CET time). It requires adjusting your schedule, but it’s the only way to get a stable connection.

  • The “Brute Force” Retry: Sometimes, it’s just a matter of persistence. I often have to “retry” in a loop until a slot opens up. It’s frustrating, but it works eventually.

  • Context Downsizing: Instead of feeding the agent the entire project at once, try to segment your prompts. By forcing the model to focus on small, isolated modules rather than the whole architecture, you reduce the compute demand and trigger fewer capacity errors.

The real solution would be to build a custom agent on Google Cloud and integrate it via VS Code, but that means leaving the Antigravity/Firebase Studio environment, which isn’t always ideal depending on your workflow.

In short: don’t expect a miracle from the devs. We aren’t the priority, so we have to adapt our own methods to bypass these constraints.

Hang in there and keep pushing !