Databricks GenAI Application Architecture

A strong architecture for the Databricks Generative AI Engineer Associate exam begins with the business task and works outward through data, model, retrieval, tools, serving, governance, and evaluation. The current exam guide expects candidates to choose among prompt chains, RAG, agents, Agent Bricks, structured-data tools, MCP servers, Model Serving, MLflow, and Unity Catalog according to the requirement rather than assembling every feature into one solution.

The vendor-neutral AI agents fundamentals and RAG fundamentals provide useful mental models. This article concentrates on how those patterns become a Databricks application architecture.

Start with inputs, outputs, and consequence

Write down what the user provides, which enterprise data the system may use, what output is expected, and whether the application only advises or can take action.

A simple summarization task may need one model call. A support assistant may need RAG. A workflow that changes business systems may need tools, state, safeguards, and explicit approval.

Choose the model task before choosing the model

Classify the work as summarization, extraction, classification, generation, reasoning, multimodal processing, or tool-using agent behavior.

Then compare models on the attributes that matter: quality, context, latency, cost, modality, tool use, structured output, and deployment constraints.

Use prompt chains when the steps are known

A deterministic sequence of prompt stages can be easier to test than a fully autonomous agent when the task has a stable workflow.

Keep intermediate outputs structured so failures are easy to locate and later stages do not have to interpret loosely formatted prose.

Use RAG when evidence must come from governed sources

Retrieval is appropriate when answers depend on enterprise content that changes, is too large for a static prompt, or must respect source permissions.

Source quality, chunking, indexing, filters, retrieval metrics, reranking, and citation all become part of the architecture.

Use agents when the next action depends on the current state

Agents are useful when the model must choose among tools, retrieve information, observe results, and adapt. They add flexibility but also increase the importance of tool contracts, state, tracing, and stop conditions.

The tool-use guide is useful because Databricks agents still need deterministic execution boundaries even when the model controls orchestration.

Agent Bricks can reduce custom engineering

Knowledge Assistant fits domain question-answering over documents. Supervisor Agent coordinates specialized agents and tools. Information Extraction turns unstructured content into structured data.

Use a managed pattern when the requirement aligns closely with the supported use case and the reduced operating burden matters.

Custom agents are appropriate when behavior is specialized

Custom agents allow more control over orchestration, libraries, tools, state, and business logic.

That flexibility creates more engineering responsibility: testing, permissions, tracing, deployment, and lifecycle must be designed explicitly.

Structured data needs a different access path from documents

Not every question should be answered through vector retrieval. Transactional facts, metrics, or governed business tables may be better accessed through SQL, Genie, Unity Catalog functions, or another structured-data tool.

Keep the source of truth close to the data instead of embedding dynamic facts into a document index.

MCP standardizes access to tool ecosystems

The current exam guide includes managed, external, and custom MCP servers. Use MCP when standardized tool discovery and invocation reduce integration work or centralize governance.

Server type should follow ownership and operational requirements rather than being chosen only because MCP is available.

Unity Catalog belongs in the architecture from the beginning

Data, models, functions, prompts, and other governed assets need ownership and access controls.

The broader data-governance principles apply: know who owns the asset, who may access it, and how changes are audited.

Persistent state should be deliberate

Agents may need conversation state, intermediate memory, or structured information across steps. Store durable state in a suitable datastore rather than relying only on one growing model context.

Separate temporary reasoning context from business records that must remain authoritative.

Model Serving turns the application into an API

Serving endpoints provide a stable interface for models and GenAI applications. The endpoint boundary is where access, latency, scaling, observability, and versioning become production concerns.

Do not treat deployment as the last checkbox; it changes how the application is operated.

Databricks Apps can provide a user-facing layer

An agent or model can be exposed through a web application or integrated into collaboration tools, depending on the use case.

Keep authentication and downstream permissions in the backend rather than exposing long-lived credentials to the browser.

Tracing should span the whole application path

MLflow tracing can capture model calls, retrieval, tools, latency, token use, and errors across a multi-step application.

Architecture is easier to improve when operators can see which stage caused the failure rather than judging only the final answer.

Evaluation belongs before and after deployment

Use representative evaluation datasets during development and monitor production traces after launch.

The AI evaluation guide helps connect component quality, task success, safety, latency, and cost into release decisions.

CI/CD should move GenAI artifacts between environments

Prompt versions, agent code, retrieval assets, configuration, and tests should follow a controlled promotion path.

The CI/CD fundamentals apply because GenAI applications still need review, staged deployment, and rollback.

Separate identity for users, apps, and agents

A human user, application backend, and autonomous agent can have different permission models. Architecture should make clear whose identity reaches each governed resource.

This is especially important when a Databricks App calls an agent or model on behalf of an authenticated user.

Use Agent Bricks when productized behavior matches the requirement

Managed patterns can reduce development and operations, but they work best when the use case fits the supported behavior.

If the application needs unusual state, orchestration, proprietary libraries, or custom control, a custom agent may provide the required flexibility.

Tool contracts should be smaller than the business system

Expose a narrow business action or query rather than an unrestricted backend API where practical.

Smaller tools are easier to authorize, describe, evaluate, and monitor.

Use Unity Catalog as a cross-cutting governance layer

Data, functions, models, and other assets can share a consistent permission and ownership model.

That reduces the number of ad hoc authorization systems surrounding an agent, although external tools still need their own controls.

Design observability before production

Tracing should capture the components that matter to quality and cost: retrieval, models, tools, latency, tokens, and state transitions.

Adding observability after an incident is much harder than defining it while the architecture is still simple.

Use interfaces that preserve backend control

User-facing apps should authenticate the user but keep privileged model or agent credentials in the backend.

This separates the browser trust boundary from the governed Databricks resources.

Architecture decisions should be revisitable

Models, Agent Bricks, serving options, and MCP capabilities can change. Keep the business contract stable and product-specific choices modular.

That makes upgrades and migrations evaluation exercises rather than full application rewrites.

Use explicit contracts between architectural layers

Retrievers should return evidence with metadata, tools should return structured results, agents should preserve task state, and serving endpoints should expose stable request and response contracts.

Clear contracts make individual layers replaceable and easier to evaluate.

Keep business rules outside free-form generation

Approval thresholds, permission checks, required fields, and irreversible side-effect rules should be implemented in deterministic code or policy.

The model can reason about whether a rule applies, but it should not become the only enforcement mechanism.

Choose memory according to what must persist

Conversation history, task checkpoints, user preferences, and business records have different lifetimes and governance needs.

Store each type in an appropriate system rather than keeping every fact in the active model context.

Plan fallback at component boundaries

Search can fail while the model remains healthy; a model can be throttled while structured tools still work; an external MCP server can be unavailable while other agent functions remain usable.

Define which degraded modes are acceptable and which should stop the task.

Architecture review should include cost paths

Agent loops, large contexts, reranking, high-end models, and repeated tools can all multiply cost.

Estimate the expensive path and monitor whether production traffic follows it more often than expected.

Use a data-flow diagram for security review

Mark where user input, enterprise documents, structured data, tool results, traces, and model output travel. Add the identity and governing control at each boundary.

This diagram often reveals unnecessary data movement or a tool that receives broader access than the use case requires.

Use ownership boundaries in the component diagram

Mark which team owns the corpus, agent code, MCP server, model endpoint, prompt, and user interface. Cross-team boundaries often determine how changes are approved and how incidents are routed.

An architecture is easier to operate when every dependency has both a technical interface and an accountable owner.

Architecture should minimize unnecessary components

If a prompt-only solution meets the requirement, adding an agent increases risk and cost. If RAG is required, a document index may be enough without a multi-agent supervisor.

The exam rewards selecting the smallest architecture that meets the stated requirement while remaining governable and testable.