Update AI Gateway Blueprint

As per https://gitlab.slack.com/archives/C051K31F30R/p1710972249450739?thread_ts=1710431031.385889&cid=C051K31F30R, we want to outline our work/goals for the AI Gateway as part of the current blueprint, this will help consolidate understanding and drive our work in AI Framework HQ (&11403) • Unassigned

doc/architecture/blueprints/ai_gateway/index.md

@@ -526,12 +526,11 @@ Alternative solutions were discussed in
 
 ## Future work
 
-Our aim is for the AI Gateway to become the primary method for the monolith to access models. Here are some of the key features we need to implement:
+AI Gateway aim is to become the primary method for the monolith to **access** machine learning models across all usages of GitLab and create a consistent user journey when developing AI-backed features. To do so, these goal is split down into three categories:
 
-- Expose a list of available models.
-- Allow self-managed installations to have their own AI Gateway, with Runway likely being the deployment method.
-
-These goals are broken down into 4 distinct categories below.
+- Centralized Access Through AI Gateway
+- Self Managed AI Gateway
+- Unit Primitives
 
 ### Centralized Access Through AI Gateway
 
@@ -539,7 +538,8 @@ The AI Gateway, a standalone service, is the sole access point for all communica
 
 This strategy significantly simplifies enterprise management and abstracts machine learning away from the monolith. With future expansions including telemetry, embeddings API, and multi-region/customer-specific deployments, our goal is to provide a scalable, comprehensive AI solution for all GitLab users, regardless of their installation type.
 
-When it comes to user-deployed models, the model registry is not solely for large language models; it's currently more targeted at smaller model applications. These smaller models could be deployed in various ways: as a standalone library, a service, a pod, a cloud deployment, and so forth. However, even for these user-deployed models, the ability to auto-configure an API that's accessible through the AI Gateway could be a significant feature.
+
+[Model registry](https://docs.gitlab.com/ee/user/project/ml/model_registry/) is a feature that allows users to use GitLab to manage the machine learning models. While not solely focused on large language models, and currently more targeted at smaller model applications, which could be deployed in various ways: as a standalone library, a service, a pod, a cloud deployment, and so forth. For these user-deployed models, the ability to auto-configure an API that's accessible through the AI Gateway could be a significant feature.
 
 - [AI Gateway as the Sole Access Point for Monolith to Access Models](https://gitlab.com/groups/gitlab-org/-/epics/13024)
 
@@ -568,4 +568,9 @@ Self-managed instances should have their own AI Gateway, with Runway likely bein
 As AI Agents evolve, we plan to reimplement our AI features as agents. To achieve this, we need to:
 - Implement prompt templating.
 - Find a way to replicate these into a self-managed installation (e.g., organization-level agents where we prepopulate with a few agents).
-- To avoid hitting the monolith on every call to AI agents, we will need a method to propagate AI Agents prompts into the AI Gateway.
+
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+[AI Agents](https://gitlab.com/groups/gitlab-org/-/epics/12330) is a feature that allows users to implement and manage their own chats and AI features, managing prompts, models and tools. Development is currently in its early stages. Once mature, we intend to move GitLab feature to agents, but there are blockers that currently prevent us from doing so: 
+
+- [Lack of prompt templating](https://gitlab.com/gitlab-org/gitlab/-/issues/441081).
+- Implement replication of user-defined prompts into ai-gateway.
+- Implement replication of GitLab-defined prompts into self-managed installations (e.g., organization-level agents where we prepopulate with a few agents).
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