ai platform

Your docs still say Vertex AI. Google calls it Gemini Enterprise Agent Platform, and the models keep retiring.

Your docs may still say Vertex AI, but Google folded it into Gemini Enterprise Agent Platform in April 2026. Gemini 2.5 Pro, Flash and Flash-Lite hit their retirement date on 20 October 2026. If anything of yours still calls them, we move it. New agents we build with ADK and run on Agent Runtime.

platform dossierGemini Enterprise Agent Platform (Vertex AI)AI platform
Current name
Gemini Enterprise Agent Platform (from Vertex AI)
Gemini 2.5 retirement date
20 October 2026 (passed)
Next Gemini retirement
gemini-3.6-flash, 19 November 2026
Embedding models retire
text-embedding-004/005 and others, 1 April 2027
release watch
  • Older text and multimodal embedding models retire1 Apr 2027
  • Gemini 2.5 Flash Image retirement pushed back15 Mar 2027
connects with
in short·15-second read

Cohash works on Gemini Enterprise Agent Platform, which is what Vertex AI has been called since April 2026.

We move apps off retiring models. Gemini 2.5 Pro, Flash and Flash-Lite reached their date on 20 October 2026, gemini-3.6-flash goes on 19 November 2026 and the older embedding models on 1 April 2027. We build ADK agents on Agent Runtime, each with an evaluation set.

you probably need this if
  • Something in production still calls a Gemini 2.5 model. Its 20 October retirement date has passed, and nobody's sure who owns it.
  • Agents on Agent Runtime still run as the default service account, which can read far more of the project than they need.
  • Ask who can see what through the search index and nobody knows. Nobody has checked which documents it returns to which users.
terms on this page RulesdefinitionThe JavaScript snippets Auth0 used to run at login. Someone would write one to add a role to the token or block a sign-in, and each one ran after the last.explained in a field note →RAGdefinitionRetrieval-augmented generation. The assistant runs a search over your company's files first, then a language model writes the answer from the passages it got back. Feed it the wrong files and you get a wrong answer, said with confidence.explained in a field note →
Ask an engineer facts checked 2026-11-07
how it runs
  1. 01Model and agent inventory
  2. 02Migration and build plan
  3. 03Evaluate and switch
  4. 04Handover
you keep
  • A register showing which model each app calls, when that model retires and who owns the app
  • For every app moving off an old Gemini version, a migration plan and the evaluation results to sign it off
  • ADK agent code deployed to Agent Runtime with CI
+ 3 more documents
who does it
Forward deployed engineer, Google Cloud and GeminiADK agent developer (Python, TypeScript)Google Cloud AI platform engineer (IAM, VPC-SC) Professional Machine Learning Engineer Generative AI Leader
google-vertex-ai · release timeline
  1. 22 Apr 2026 Vertex AI becomes Gemini Enterprise Agent Platform Google calls it the evolution of Vertex AI. What you'll actually notice is the renames: Agent Engine is Agent Runtime now and Vertex AI Search is Agent Search. Anything new for Vertex AI comes out only through the Agent Platform.
  2. 13 Oct 2026 Gemini 4 Argon is GA Before you point production at Gemini 4 Argon, check your API version. Long decodes only work on v1beta1, and the Gen AI SDK uses that unless someone set v1. Otherwise it's a 1,048,576-token limit for input and output, with LOW, MEDIUM or HIGH thinking. Google won't retire it before 13 October 2027.
  3. 20 Oct 2026 Gemini 2.5 Pro, Flash and Flash-Lite retire The 20 October 2026 date for Gemini 2.5 Pro, Flash and Flash-Lite has come and gone. Google lists gemini-3.8-flash for all three, so that's the simplest single target. Pro can also move to gemini-3.5-flash, and the other two to gemini-3.5-flash-lite. Gemini 2.0 Flash had already retired on 1 June 2026.
  4. 21 Oct 2026 CodeMender v0.16.0 cm find now runs a deep, file-by-file scan by default. If a CI job relied on the quicker single-session scan, it needs the --quick flag now. There's also a new cm health command that checks credentials and endpoint reachability before a scan starts.
  5. 21 Oct 2026 16 open model endpoints in Model Garden retire Sixteen open model MaaS endpoints had a retirement date of 21 October 2026, including deepseek-r1-0528-maas, gpt-oss-20b-maas, llama-3.3-70b-instruct-maas and four qwen3 endpoints. If a script or agent still points at one of them, it needs a new model and a fresh evaluation run before anyone relies on it again.
  6. 26 Oct 2026 Semantic governance gets an off switch for user intent matching Semantic governance policies check user intent by default, and there's now a key, sgpUserIntentMatching, to switch that off. Google warns that switching it off leaves you more open to indirect prompt injection. We'd leave it on. If someone does turn it off, the sgpUserIntentMatchingMode field in the evaluation logs will show it.
  7. today · Nov 09, 2026
  8. 19 Nov 2026ahead Gemini 3.6 Flash retires gemini-3.6-flash retires on 19 November 2026. That's quick, but it's one of Google's short-term models, and the replacement is gemini-3.8-flash. Check nobody moved a Gemini 2.5 app onto it. gemini-3.7-flash goes on 28 January 2027.
  9. 15 Mar 2027ahead Gemini 2.5 Flash Image retirement pushed back Google moved the retirement of gemini-2.5-flash-image from 2 October 2026 out to 15 March 2027. That buys image workloads a few more months, and the model to test against is gemini-3.1-flash-lite-image.
  10. 1 Apr 2027ahead Older text and multimodal embedding models retire Google retires text-embedding-005 and text-embedding-004 on 1 April 2027, and text-multilingual-embedding-002 and multimodalembedding@001 go the same day. The table doesn't list a replacement. gemini-embedding-001 isn't going anywhere before 20 May 2028. On RAG Engine a corpus keeps its embedding model for life, so moving means building a new corpus.
  11. no date yetHow long models last and how much notice Google givesTwo rules, depending on the table. Models in Google's main table get at least 12 months after release. Short-term ones like gemini-3.6-flash only get a fixed date with at least 45 days to migrate. Dates can be pushed back but, Google says, never brought forward.
the story · gemini enterprise agent platform (vertex ai) estate

From the first call to a setup your own team can run.

Here's what usually prompts a call about your Gemini Enterprise Agent Platform (Vertex AI) estate, what we do about it and in which order, and where you end up.

  1. chapter 01 · where it hurts

    Why teams reach out

    • Something in production still calls a Gemini 2.5 model. Its 20 October retirement date has passed, and nobody's sure who owns it.
    • Agents on Agent Runtime still run as the default service account, which can read far more of the project than they need.
    • Ask who can see what through the search index and nobody knows. Nobody has checked which documents it returns to which users.
  2. chapter 02 · what we change

    How we solve it

    • Gemini model migrations
    • Agents with ADK and Agent Runtime
    • Retrieval and Agent Search
    • Identity, networking and data access
    • Evaluations and monitoring
    • Gemini Enterprise rollout
  3. chapter 03 · how it runs

    Our approach

    • Model and agent inventory
    • Migration and build plan
    • Evaluate and switch
    • Handover
  4. chapter 04 · where you end up

    What changes

    • The apps move across one at a time, in the order the business agreed.
    • The agent is approved by security on the evidence.
    • A register showing which model each app calls, when that model retires and who owns the app
    • For every app moving off an old Gemini version, a migration plan and the evaluation results to sign it off
what we do

Our Gemini Enterprise Agent Platform (Vertex AI) work

6 kinds of Gemini Enterprise Agent Platform (Vertex AI) work, each run by engineers who have done it on live tenants.

  1. Applications moved off retiring Gemini versions to current ones, with prompts retested on an evaluation set and the switch made behind a configuration flag.

  2. Agents written with the open-source Agent Development Kit in Python, TypeScript, Go or Java, deployed to Agent Runtime, Cloud Run or GKE depending on your platform.

  3. Grounding on your documents and data with Agent Search, formerly Vertex AI Search, with sources, access rules and metadata reviewed before indexing.

  4. Service accounts, Workload Identity Federation, VPC Service Controls and IAM roles designed so agents reach only the data and tools they need.

  5. Evaluation sets from real cases, run on every prompt or model change, with logging and cost alerts in Cloud Monitoring.

  6. Agents published to employees through the Gemini Enterprise app, with groups and data connectors configured per team.

capability 01 / 06in production

Gemini model migrations

Applications moved off retiring Gemini versions to current ones, with prompts retested on an evaluation set and the switch made behind a configuration flag.

capability 02 / 06in production

Agents with ADK and Agent Runtime

Agents written with the open-source Agent Development Kit in Python, TypeScript, Go or Java, deployed to Agent Runtime, Cloud Run or GKE depending on your platform.

capability 03 / 06in production

Retrieval and Agent Search

Grounding on your documents and data with Agent Search, formerly Vertex AI Search, with sources, access rules and metadata reviewed before indexing.

capability 04 / 06in production

Identity, networking and data access

Service accounts, Workload Identity Federation, VPC Service Controls and IAM roles designed so agents reach only the data and tools they need.

capability 05 / 06in production

Evaluations and monitoring

Evaluation sets from real cases, run on every prompt or model change, with logging and cost alerts in Cloud Monitoring.

capability 06 / 06in production

Gemini Enterprise rollout

Agents published to employees through the Gemini Enterprise app, with groups and data connectors configured per team.

typical situations

Gemini Enterprise Agent Platform (Vertex AI) situations we walk into

We pieced these together from Gemini Enterprise Agent Platform (Vertex AI) situations that come up a lot. They're patterns. You won't find a specific client in them.

case file 01

Retirement day for Gemini 2.5 Flash, and eleven apps still call it

composite · not a client story
  1. Beforethe situation

    Eleven internal apps call Gemini 2.5 Flash through Vertex AI endpoints, and that model retires on 20 October 2026. Just two have any kind of test set.

  2. What we dothe work

    We pull every caller out of the logs and rank them by business impact. The top apps get small evaluation sets written from real requests. Then, highest impact first, each one moves to a current Gemini Flash model behind a configuration flag.

  3. Afterwhere you end up

    The apps move across one at a time, in the order the business agreed. Each keeps its test set, so the next retirement is a smaller job.

case file 02

Security won't approve an Agent Engine prototype

composite · not a client story
  1. Beforethe situation

    Someone built a procurement agent with ADK and it runs on Agent Runtime, formerly Agent Engine, under the default compute service account. So it can read every BigQuery dataset in the project.

  2. What we dothe work

    The agent gets its own service account, and that account can read two views. Nothing else. We put the project inside VPC Service Controls and log every tool call, and the evaluation set has to pass before the staged release.

  3. Afterwhere you end up

    The agent is approved by security on the evidence. Its access is whatever the procurement process actually needs.

approach

How a Gemini Enterprise Agent Platform (Vertex AI) engagement runs

We don't start a step until the last one has produced something you can check.

  1. step 01

    Model and agent inventory

    We go through every application, agent and pipeline that calls Gemini or another model on the platform. Each one gets a row with its model version, retirement date, owner and service account.

  2. step 02

    Migration and build plan

    Retiring models get replaced first, since those have a date on them. Then agents are built or hardened with ADK, Agent Runtime and scoped IAM, in whatever order the retirement dates and business risk put them.

  3. step 03

    Evaluate and switch

    We score every change on an evaluation set made from real cases. It's switched over behind a flag, and logging and cost alerts stay on.

  4. step 04

    Handover

    Your team keeps the model version register and the evaluation pipelines. There's a runbook as well, so when the next retirement notice turns up somebody already knows the first steps.

architecture

Where your agents run, which models they call and what data they're allowed to touch

Employees open an agent from the Gemini Enterprise app or from a business app they already use. We write the agent with ADK and run it on Agent Runtime, and it calls Gemini through the Agent Platform. When it needs facts, it checks Agent Search or queries BigQuery or Snowflake as a scoped service account, so it only sees what that account can.

Where your agents run, which models they call and what data they're allowed to touch Employees open an agent from the Gemini Enterprise app or from a business app they already use. We write the agent with ADK and run it on Agent Runtime, and it calls Gemini through the Agent Platform. When it needs facts, it checks Agent Search or queries BigQuery or Snowflake as a scoped service account, so it only sees what that account can. 01 · USERS 02 · AGENT PLATFORM 03 · GROUNDING AND DATA 04 · BUSINESS SYSTEMS SAML sign-in agent request model call model call grounding service account scoped query API tool call Okta workforce SSO Gemini Enterprise app employee agent access GO Agent Runtime ADK agents ·sessions Gemini models current Flash and Proversions Model Garden partner models Agent Search approved document stores BigQuery read-only views Snowflake scoped role Sf Salesforce cases ·opportunities © Cohash · cohash.com
01 · USERS02 · AGENT PLATFORM03 · GROUNDING AND DATA04 · BUSINESS SYSTEMS Okta: workforce SSO Okta workforce SSO Gemini Enterprise app: employee agent access GeminiEnterprise app employee agentaccess Agent Runtime: ADK agents · sessions AgentRuntime ADK agents ·sessions Gemini models: current Flash and Pro versions Geminimodels current Flashand Proversions Model Garden: partner models Model Garden partner models Agent Search: approved document stores Agent Search approveddocument stores BigQuery: read-only views BigQuery read-only views Snowflake: scoped role Snowflake scoped role Salesforce: cases · opportunities Salesforce cases · opportunities agent request grounding service account scoped query API tool call © Cohash · cohash.com
use cases

Typical Gemini Enterprise Agent Platform (Vertex AI) projects

The work teams usually bring us, and the platforms Gemini Enterprise Agent Platform (Vertex AI) has to talk to while we do it.

  1. GOO-01 A production app still points at a Gemini 2.5 model, even though its retirement date was 20 October 2026 common request
  2. GOO-02 An agent someone prototyped on Agent Engine is heading for production, and it still has no identity of its own or any monitoring common request
  3. GOO-03 Your RAG corpora sit on embedding models that stop working on 1 April 2027, and nobody has a plan for rebuilding them common request
  4. GOO-04 Old Vertex AI names are still in contracts and IAM roles, and nobody is sure what each one is called now common request
  5. GOO-05 Retrieval went live on Vertex AI Search, and nobody set access rules on the sources first common request
  6. GOO-06 Leadership bought Gemini Enterprise licences and is now asking where the custom agents are common request
Gemini Enterprise Agent Platform (Vertex AI) · connected platforms
you keep

What a Gemini Enterprise Agent Platform (Vertex AI) engagement leaves behind

These are working documents, written against your configuration, so your team can run them without us.

  • inventory01

    A register showing which model each app calls, when that model retires and who owns the app

    yours to keep
  • plan02

    For every app moving off an old Gemini version, a migration plan and the evaluation results to sign it off

    yours to keep
  • code03

    ADK agent code deployed to Agent Runtime with CI

    yours to keep
  • design04

    Service account and IAM design per agent

    yours to keep
  • inventory05

    Agent Search source inventory with access rules

    yours to keep
  • runbook06

    Runbooks for model switches and incident response

    yours to keep
talent and delivery · gemini enterprise agent platform (vertex ai)

Certified engineers, wherever you need them.

Some clients want a project team, some want a couple of engineers on theirs. Either works. Onsite, nearshore or offshore, with handovers across time zones that keep things moving.

Onsite

Engineers at your sites in the US

Nearshore

Overlapping hours with US teams

Offshore

Overnight progress on your backlog

All time zones

Coverage handed over across time zones

24-hour coverage · US Central time
OnsiteUS business hours
Nearshoreoverlapping US hours
Offshoreovernight progress
Combinedhandover, not hand-off
certifications our engineers hold
  • Professional Machine Learning EngineerGoogle Cloud
  • Generative AI LeaderGoogle Cloud
roles we staff
  • Forward deployed engineer, Google Cloud and Gemini
  • ADK agent developer (Python, TypeScript)
  • Google Cloud AI platform engineer (IAM, VPC-SC)
  • ML engineer for Gemini model migration and tuning
  • LLM evaluation engineer
01Was Vertex AI renamed? What is it called now?

Yes, more or less. On 22 April 2026 Google launched Gemini Enterprise Agent Platform and described it as the evolution of Vertex AI, and new features now come out there. A couple of components changed names too. Agent Engine is Agent Runtime, and Vertex AI Search is Agent Search. If you bookmarked console pages, they still redirect.

02Our app still calls Gemini 2.5 Pro, Flash or Flash-Lite, which have retired. What should we move it to?

Move it now, because the retirement date was 20 October 2026. Google lists gemini-3.8-flash for all three 2.5 models, or gemini-3.5-flash for Pro and gemini-3.5-flash-lite for Flash and Flash-Lite. Watch your request settings when you test. On 3.6 Flash and 3.5 Flash-Lite, Google says custom temperature, top-K and top-P are ignored and penalty parameters throw an error, so run the evaluation set before traffic moves.

03Our RAG setup uses text-embedding-005. When do the Vertex AI embedding models retire?

On 1 April 2027 for text-embedding-005, text-embedding-004, text-multilingual-embedding-002 and multimodalembedding@001. Google's lifecycle table doesn't name a replacement. gemini-embedding-001 runs until at least 20 May 2028. RAG Engine uses text-embedding-005 by default and keeps a corpus on one model for life, so plenty of teams will be building new corpora.

04We just moved off Gemini 2.5. How much notice will we get before the next model retires?

It depends which table the model is in. Most stay available for at least 12 months after release, but short-term models like gemini-3.6-flash can get as little as 45 days. Google says it may push a date out and won't bring one forward. We keep a register of model versions per application, so the next date doesn't catch anyone out.

05Our team already uses another agent framework. Should we switch to ADK?

Use whatever your team will actually maintain. ADK is Google's own open-source framework and deploys straight to Agent Runtime, Cloud Run or GKE. If you already write agents in another framework, keep it. We usually decide on languages, hosting and which tools the agent has to call.

06Do you embed engineers with our business team?

Yes. Our forward deployed engineers sit with your team and build against your actual systems and data. You keep their evaluations, access controls and runbooks after they hand over. Staffing can be onsite in the US, nearshore or offshore, and covers all time zones.

services

Related services

Projects on Gemini Enterprise Agent Platform (Vertex AI) usually end up touching one of these, so they're worth a look.

fact-checked Checked against vendor and official documentation last checked 2026-11-07 content fingerprint 2513bdd4
  1. 01Book a 30-minute working sessionsenior engineer
  2. 02Talk to an engineer about your setupreply within 24 hours
  3. 03See how similar projects wentcase studies
Reviewed by the Cohash team Gemini Enterprise Agent Platform (Vertex AI) is a trademark of its owner, used here to identify the platform. how we handle facts and data →
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