Agentic AI architecture on Azure
Teams ship agents without rebuilding identity, evaluation and observability each time, on a single gateway where every agent, model and tool is registered. Scope is agreed after the architecture review.
Every team builds agents. No one can see them.
Prototypes, copilots and user-built automations multiply faster than any inventory can track them. Each area rebuilds the same identity, evaluation and logging components in isolation, so no single place records which agents run in production, which tools they can reach or what they cost.
An agent platform makes the governed path the fastest path: a central gateway where every agent, MCP server and model is registered, reference blueprints your teams copy instead of rebuilding, and evaluation and tracing wired in by default. The question is not whether to govern agents, but whether you set the standard now or inherit whatever each team already shipped.
A platform built in layers, each one holding the next
AI does not fail at the model. It fails underneath, where the data is fragmented, ungoverned or impossible to trust. This is the stack we build, bottom up.
Sources
ERP, CRM, IoT, files and third-party APIs, structured and unstructured
Ingestion & engineering
Reusable pipelines, batch and streaming, with tested transformations
Lakehouse foundation
Fabric, Databricks or Snowflake, chosen on your context, not on ours
Governance & quality
Ownership, lineage, access models and validation gates
AI-ready consumption
Data shaped for analytics, ML and agents, and trusted by both
One governed foundation instead of a platform per department. The same data serves analytics, machine learning and agents, with the same ownership and the same quality gates.
Vendor-agnostic: Fabric, Databricks or Snowflake, decided by your context and not by our shelf.
What the engagement includes
The gateway, the standards and the reference implementations that make the platform usable by every team. Not for organisations running a single pilot agent, or without an Azure tenant and an owner for identity decisions.
AI Gateway
Central registration of agents, MCPs and models, with access control and centralized traceability.
Industrialization blueprints
Reference projects covering traceability, evaluation, identity, deployment and stack, ready to clone for new use cases.
Corporate standards
Monitoring, quality, security, memory, deployment and scalability standards documented and applied.
What working together looks like
- Duration. Architecture review first; the platform build is scoped from its findings
- Team. Plain platform architects and AI engineers, building on your Azure tenant with your teams
- What we need from you. Access to the Azure tenant, the list of agents already running, and an owner for identity and access decisions
- What you take away. An agent gateway with every agent, MCP server and model registered, plus reference blueprints and evaluation and tracing standards
- Not included. We do not build your line-of-business agents, fine-tune models, or cover Azure consumption and licence costs
Start with the architecture review
One session with your architects; you leave with a map of the agents already running, what they can reach, and what a platform would replace.
Book the architecture review