Gemini Enterprise Agent Platform−27 Negative

Operator Insights

What are teams learning from building on Gemini Enterprise Agent Platform and running it in production?

8 accounts: 2 failures, incidents or rollbacks, 3 run it beside another tool, 1 evaluated it, 1 adopted it and 1 reported by others

Failures, incidents and rollbacks2

Manish JadhavFounder, Inoltro DigitalInoltro Digital
Code review automation. A GitHub Actions workflow sends each pull request to Gemini through Agent Platform's global endpoint on pay-as-you-go billing.
GitHub issue, jadedm/pr-review-workflows, published Oct 21, 2026Read the issue (opens GitHub issue, jadedm/pr-review-workflows in a new tab)
Observation
Inoltro's pull-request review workflow had two Gemini calls on Agent Platform refused with HTTP 429 RESOURCE_EXHAUSTED on Oct 21, 2026.
Takeaway
On pay-as-you-go Gemini, a 429 can arrive with no project quota reached. Retry with exponential backoff over minutes, and price Provisioned Throughput before a workload depends on every call landing.
What happened
Two review jobs failed after three attempts each with HTTP 429 RESOURCE_EXHAUSTED, at 15:35 and 18:56 UTC on Oct 21, 2026. Sign-in succeeded both times, and only the generateContent call was refused. All three attempts of the second job fell within about 20 seconds. Six later reviews on the same project between 19:43 and 20:13 UTC succeeded. Jadhav traced the refusals to Dynamic Shared Quota, where a 429 means the model's shared pool was busy at that moment, and chose a longer backoff with jitter as the fix, since Provisioned Throughput is the only guaranteed capacity and is paid in advance.
- Public engineering write-up (issue in the founder's own GitHub repository)
- Run ids and UTC times stated
- Opened Oct 21, 2026
Deployment details
Code review automation. A GitHub Actions workflow sends each pull request to Gemini through Agent Platform's global endpoint on pay-as-you-go billing.
- Public engineering write-up (issue in the founder's own GitHub repository)
- Run ids and UTC times stated
- Opened Oct 21, 2026
Team
Inoltro Digital
Duration
Two refused jobs on Oct 21, 2026
Workload
Automated Gemini review of pull requests in GitHub Actions
Product version
Gemini on Agent Platform, global endpoint, pay-as-you-go
Model
gemini-4-argon
Measurement
Three attempts in about 20 seconds before the job gave up; six later reviews succeeded

Fabio ParraAug 12, 2026Reddit, r/googlecloudAn API key that Firebase Hosting exposed on MOVILIXA's landing page ran 131,950 Vertex AI image calls, US$6,562 against a US$25 monthly budget.
Fabio ParraRuns the three-person startup MoviliXa, as he states in the postMoviliXa
A three-person mobile app studio in Colombia, with the Vertex AI API enabled for image recognition in a safety-inspection app.
Reddit, r/googlecloud, published Aug 12, 2026Read the write-up (opens Reddit, r/googlecloud in a new tab)
Observation
An API key that Firebase Hosting exposed on MOVILIXA's landing page ran 131,950 Vertex AI image calls, US$6,562 against a US$25 monthly budget.
Takeaway
Restrict every API key in a project that has Vertex AI enabled to the APIs and sites it needs, and turn on App Check enforcement before anything goes public.
What happened
Firebase Hosting created a web API key with broad access and published it on the studio's static landing page. Between Jun 30 and Jul 16, 2026, someone used it through the Firebase AI Logic proxy to run Vertex AI image generation: 131,950 calls, about 100,000 images and US$6,562 in charges against a US$25 monthly budget. App Check recorded the requests as unverified but let them through, since enforcement was off by default (r/googlecloud, Aug 12, 2026).
Public practitioner write-up (Reddit post by the founder)
Calls and charges as the founder reports them
Deployment details
A three-person mobile app studio in Colombia, with the Vertex AI API enabled for image recognition in a safety-inspection app.
Public practitioner write-up (Reddit post by the founder)
Calls and charges as the founder reports them
Team
MOVILIXA, three people
Duration
Jun 30 to Jul 16, 2026
Workload
Image recognition for safety inspections; the abuse ran image generation
Product version
Vertex AI API through the Firebase AI Logic proxy
Model
Gemini image models
Measurement
131,950 calls and US$6,562 in charges against a US$25 budget (Aug 12, 2026)

Run it beside another tool3

Shaurya KethireddyLead Member of Technical StaffSlack
Slack AI infrastructure. Slack AI serves millions of users. Models served from AWS and Google Cloud.
Engineering at Slack, published May 28, 2026Read the write-up (opens Engineering at Slack in a new tab)
Observation
At Slack, Vertex AI joined Amazon Bedrock as a second model cloud so each Slack AI feature can run on the model that suits it.
Takeaway
A second model cloud pays off when the application can treat both clouds the same way. Build the error and rate-limit normalization first, then route each feature to the model that fits it.
What happened
Slack served Anthropic models on SageMaker, then moved to Amazon Bedrock as model releases reached Bedrock first. By early 2026 Slack added Vertex AI as a second serving cloud for redundancy and a wider model catalog. It built secretless authentication and a normalization layer so rate-limit errors from both clouds are handled the same way. Matching models to features brought about 10% better quality on complex reasoning tasks and about 67% lower latency on high-velocity, low-token workloads.
“By early-2026 we officially expanded our footprint to include Google Cloud Platform (GCP) Vertex AI, not just as a failover for redundancy, but as a strategic engine to accelerate product innovation through access to a broader catalog of state-of-the-art models.”
Shaurya Kethireddy
- Public engineering write-up
- Quality and latency changes reported by Slack
- No baseline period stated
Deployment details
Slack AI infrastructure. Slack AI serves millions of users. Models served from AWS and Google Cloud.
- Public engineering write-up
- Quality and latency changes reported by Slack
- No baseline period stated
Team
Slack AI infrastructure engineering
Duration
Vertex AI added in early 2026
Workload
Slack AI features for millions of users
Product version
Vertex AI beside Amazon Bedrock, behind an API normalization layer
Measurement
About 10% better quality on complex reasoning; about 67% lower latency on low-token workloads

IntercomAug 8, 2025Fin AI ResearchFin serves Claude through Vertex AI beside Anthropic's API and Amazon Bedrock, and shifts traffic to another vendor when one degrades.
Fin AI research teamAuthors of Fin's reliability write-upIntercom
Fin, Intercom's customer service agent, where each request uses several models.
Fin AI Research, published Aug 8, 2025Read the write-up (opens Fin AI Research in a new tab)
Observation
Fin serves Claude through Vertex AI beside Anthropic's API and Amazon Bedrock, and shifts traffic to another vendor when one degrades.
Takeaway
Buy Claude on at least two clouds with failover between them; Fin treats Vertex AI as one interchangeable lane, never the only one.
What happened
Fin's routing layer lists every vendor that can serve each model: Anthropic models on Vertex AI, Amazon Bedrock or Anthropic's own infrastructure, and OpenAI models on Azure or OpenAI. When one vendor degrades, traffic shifts to another, and Fin keeps capacity for two to three times its normal traffic (Fin AI Research, Aug 8, 2025).
“For example, Anthropic models can be served by AWS Bedrock, GCP Vertex, or Anthropic’s own infrastructure; OpenAI models are served by either Azure or OpenAI itself.”
Fin AI research team
Public engineering write-up (Fin AI Research)
No per-vendor metrics reported
Deployment details
Fin, Intercom's customer service agent, where each request uses several models.
Public engineering write-up (Fin AI Research)
No per-vendor metrics reported
Team
Fin AI engineering at Intercom
Workload
Customer service conversations, several models per request
Product version
Vertex AI beside Anthropic's API and Bedrock
Model
Anthropic and OpenAI models
Measurement
Buffer for two to three times normal traffic (Aug 8, 2025)
ZalandoAug 14, 2026Zalando EngineeringZalando's ML platform team serves Google Vertex models beside OpenAI and Amazon Bedrock through one LiteLLM proxy for the company's engineers.
Bartosz OcytkoExecutive Principal EngineerZalando
Zalando's internal LLM gateway, run by the ML platform team since January 2024.
Zalando Engineering, published Aug 14, 2026Read the write-up (opens Zalando Engineering in a new tab)
Observation
Zalando's ML platform team serves Google Vertex models beside OpenAI and Amazon Bedrock through one LiteLLM proxy for the company's engineers.
Takeaway
Put Vertex behind the same internal gateway as your other providers, so teams switch clouds by changing a model name and spend stays in one place.
What happened
Zalando's ML platform team deployed a LiteLLM-based API proxy in January 2024 that gives engineers models from several providers, which by August 2026 were OpenAI, AWS Bedrock and Google Vertex (Zalando Engineering, Aug 14, 2026). The post reports no usage or cost figures for the Vertex route.
“…our ML platform team deployed in January 2024 a LiteLLM based API proxy with access to models from different providers (now: OpenAI, AWS Bedrock, and Google Vertex).”
Bartosz Ocytko
Public engineering write-up (Zalando Engineering)
No metrics reported
Deployment details
Zalando's internal LLM gateway, run by the ML platform team since January 2024.
Public engineering write-up (Zalando Engineering)
No metrics reported
Team
Zalando ML platform team
Duration
Proxy since January 2024
Workload
Company-wide model access for engineers
Product version
Google Vertex behind a LiteLLM proxy, beside OpenAI and AWS Bedrock

Evaluated it1

Samuel ColvinFounder and CEOPydantic
Pydantic AI, an open-source agent framework. Model adapters for Gemini through Vertex AI and through the generative language API behind AI Studio.
Latent Space podcast, published Feb 6, 2025Read the transcript (opens Latent Space podcast in a new tab)
Observation
At Pydantic, live-model tests on every commit to main found Gemini through Vertex AI much more reliable than through the generative language API.
Takeaway
Run a few live-model tests on every commit for each route you support. They show when one route to a model fails far more often than another.
What happened
Pydantic AI runs a handful of tests against live models on every commit to main. Colvin reports that the generative language API returns 503 errors about 20% of the time, and in one week a test against it failed on every run. He states that Vertex serves the same API and is much more reliable.
“So like all of the models fail more often than you might expect, but like that one seems to be particularly likely to fail. But Vertex is the same API, but much more reliable.”
Samuel Colvin, at 28:29 in the recording
- Podcast episode (Latent Space)
- Failure rates as the founder recalls them
- No test counts published
Deployment details
Pydantic AI, an open-source agent framework. Model adapters for Gemini through Vertex AI and through the generative language API behind AI Studio.
- Podcast episode (Latent Space)
- Failure rates as the founder recalls them
- No test counts published
Team
Pydantic AI maintainers
Workload
A handful of live-model tests on every commit to main
Product version
Vertex AI beside the generative language API, both behind Pydantic AI model adapters
Measurement
About 20% of requests returned 503 on the generative language API

Adopted it1

PaulShinJun 9, 2025Hacker NewsMarkhub runs its heavy tasks on Gemini through Vertex AI and keeps small, frequent tasks on a self-hosted stack.
PaulShinBuilder at MarkhubMarkhub
Markhub, an AI collaboration product.
Hacker News, published Jun 9, 2025Read the write-up (opens Hacker News in a new tab)
Observation
Markhub runs its heavy tasks on Gemini through Vertex AI and keeps small, frequent tasks on a self-hosted stack.
Takeaway
Split traffic by task size: pay per token on Vertex for the heavy work and keep cheap, frequent calls on hardware you already run.
What happened
Markhub sends summarization, code generation and creative work to Gemini models on Vertex AI, and serves small high-frequency tasks from its own stack (Hacker News, Jun 9, 2025).
Public practitioner write-up (Hacker News comment)
No metrics reported
Deployment details
Markhub, an AI collaboration product.
Public practitioner write-up (Hacker News comment)
No metrics reported
Team
Markhub
Workload
Summarization, code generation and creative work
Product version
Gemini on Vertex AI beside a self-hosted stack

Reported by others1

AnthropicSep 17, 2025Anthropic EngineeringAnthropic's postmortem traces a routing incident that sent some Claude requests on Vertex AI to the wrong server pool for about three weeks in 2025.
Sam McAllisterAuthor of Anthropic's postmortemAnthropic
Model serving. Anthropic serves Claude through its first-party API, Amazon Bedrock and Google Cloud's Vertex AI.
Anthropic Engineering, published Sep 17, 2025Read the postmortem (opens Anthropic Engineering in a new tab)
Observation
Anthropic's postmortem traces a routing incident that sent some Claude requests on Vertex AI to the wrong server pool for about three weeks in 2025.
Takeaway
A model served through a cloud platform can degrade without any outage on the cloud's own status page. Run your own quality checks on the route you buy, and keep a second route to compare against.
What happened
A context-window routing bug sent some short-context Claude Sonnet 4 requests to servers configured for long contexts, which degraded those responses. On Vertex AI the incorrect routing affected less than 0.0004% of requests between Aug 27 and Sep 16, 2025, and routing was sticky, so some users were hit repeatedly. Anthropic deployed the fix on Sep 4, and the rollout to Vertex AI was complete by Sep 16.
Public engineering write-up (Anthropic engineering postmortem)
Share of affected Vertex AI requests stated
Published Sep 17, 2025
Deployment details
Model serving. Anthropic serves Claude through its first-party API, Amazon Bedrock and Google Cloud's Vertex AI.
Public engineering write-up (Anthropic engineering postmortem)
Share of affected Vertex AI requests stated
Published Sep 17, 2025
Team
Anthropic engineering
Duration
Aug 27 to Sep 16, 2025 on Vertex AI
Workload
Claude served to customers through Vertex AI
Product version
Claude on Google Cloud's Vertex AI
Model
Claude Sonnet 4
Measurement
Less than 0.0004% of Vertex AI requests misrouted

Failures, incidents and rollbacks2

Inoltro's pull-request review workflow had two Gemini calls on Agent Platform refused with HTTP 429 RESOURCE_EXHAUSTED on Oct 21, 2026.

An API key that Firebase Hosting exposed on MOVILIXA's landing page ran 131,950 Vertex AI image calls, US$6,562 against a US$25 monthly budget.

Run it beside another tool3

At Slack, Vertex AI joined Amazon Bedrock as a second model cloud so each Slack AI feature can run on the model that suits it.

Fin serves Claude through Vertex AI beside Anthropic's API and Amazon Bedrock, and shifts traffic to another vendor when one degrades.

Zalando's ML platform team serves Google Vertex models beside OpenAI and Amazon Bedrock through one LiteLLM proxy for the company's engineers.

Evaluated it1

At Pydantic, live-model tests on every commit to main found Gemini through Vertex AI much more reliable than through the generative language API.

Adopted it1

Markhub runs its heavy tasks on Gemini through Vertex AI and keeps small, frequent tasks on a self-hosted stack.

Reported by others1

Anthropic's postmortem traces a routing incident that sent some Claude requests on Vertex AI to the wrong server pool for about three weeks in 2025.