Shared model access
- date
- category
- AI Agents
- also in
- AI Engineering · Cloud
- reading
- 2 min / 332 words
The Implementer Agent needs a model.
For a single team the simplest setup looks like this:
Implementer Agent
|
v
Claude / GPT / Gemini
Each team can pick a provider, create credentials and get going.
At a larger scale, though, this means:
Team A -> Claude
Team B -> GPT
Team C -> Gemini
Team D -> yet another provider
and separate management of:
- keys,
- endpoints,
- regions,
- quotas,
- versions,
- costs,
- data policy.
What we introduce
A shared model access platform:
Implementer Agents
|
v
Organization AI Platform
|
v
Approved Models
The agent still uses a specific model.
The difference is that it does not connect to the provider directly.
Where we configure it
Agent repo
The agent can point at a logical deployment:
model:
deployment: coding-reasoning
Organizational platform
Maps coding-reasoning to a specific model and its deployment.
The organization can switch:
GPT X -> GPT Y
without rewriting the agent's whole flow.
What the organization gains
- a shared list of approved models,
- control over region and data processing,
- central identity,
- quotas,
- cost monitoring,
- the ability to retire a model,
- the ability to compare providers.
Tools on the market
| Platform | Cloud | What it gives the organization |
|---|---|---|
| Microsoft Foundry | Azure | model catalog and deployments, identity, networking, observability |
| Amazon Bedrock | AWS | access to many models through IAM, guardrails, billing and quotas |
| Vertex AI | GCP | managed models, IAM, regions, monitoring and quotas |
| Direct provider API | any | full freedom, but every team manages the integration on its own |
What changes for the Implementer Agent
Nothing significant in its workflow.
It still does:
task
|
v
model
|
v
decision
What changes is how the model is delivered.
This is the first example of standardization that does not change the team's logic but gives the organization control over a shared layer.