Building AI Agents with the Mosaic AI Agent Framework
Who this is for:
Architecture / Concept Overview: Building AI Agents with the Mosaic AI Agent Framework
An AI agent orchestrates an LLM, retrieval sources, and tool calls in a reasoning loop to accomplish user tasks.
%%{init: {"theme":"base","themeVariables":{"background":"#0B0E14","primaryTextColor":"#E0E6ED","lineColor":"#5D6470","darkMode":true,"primaryColor":"#2E4A4A","secondaryColor":"#374151","secondaryTextColor":"#E0E6ED","tertiaryColor":"#111827","tertiaryTextColor":"#E0E6ED","edgeLabelBackground":"#1f2937"}}}%%
flowchart LR
classDef source fill:#3F4B59,stroke:#9CA3AF,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef ingestion fill:#5A4B36,stroke:#C9A86B,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef processing fill:#535072,stroke:#8E82B4,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef storage fill:#2E4A4A,stroke:#5FAFA8,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef serving fill:#3D5550,stroke:#6BB7AA,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef governance fill:#5A3F52,stroke:#C28BB0,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
USER[User Query] -->|Input| AGENT[Agent Orchestrator]
AGENT -->|Reason| LLM[Foundation Model]
AGENT -->|Retrieve| VS[Vector Search - RAG]
AGENT -->|Call| TOOLS[Unity Catalog Functions]
AGENT -->|Guard| GUARD[Guardrails]
LLM -->|Plan| AGENT
VS -->|Context| AGENT
TOOLS -->|Results| AGENT
AGENT -->|Response| USER_OUT[User Response]
USER:::source
AGENT:::processing
LLM:::serving
VS:::storage
TOOLS:::ingestion
GUARD:::governance
USER_OUT:::source
*Agent architecture: the orchestrator loops through reasoning, retrieval, and tool execution to produce a grounded response.*
%%{init: {"theme":"base","themeVariables":{"background":"#0B0E14","primaryTextColor":"#E0E6ED","lineColor":"#5D6470","darkMode":true,"primaryColor":"#2E4A4A","secondaryColor":"#374151","secondaryTextColor":"#E0E6ED","tertiaryColor":"#111827","tertiaryTextColor":"#E0E6ED","edgeLabelBackground":"#1f2937"}}}%%
graph TD
classDef source fill:#3F4B59,stroke:#9CA3AF,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef ingestion fill:#5A4B36,stroke:#C9A86B,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef processing fill:#535072,stroke:#8E82B4,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef storage fill:#2E4A4A,stroke:#5FAFA8,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef serving fill:#3D5550,stroke:#6BB7AA,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
classDef governance fill:#5A3F52,stroke:#C28BB0,stroke-width:2px,rx:8,ry:8,color:#E0E6ED
LIFECYCLE[Agent Lifecycle] --> BUILD[Build]
LIFECYCLE --> EVAL[Evaluate]
LIFECYCLE --> DEPLOY[Deploy]
LIFECYCLE --> MONITOR[Monitor]
BUILD --> DEFINE[Define Tools & Retrieval]
BUILD --> PROMPT_ENG[Prompt Engineering]
BUILD --> TEST_LOCAL[Local Testing]
EVAL --> EVAL_SET[Evaluation Dataset]
EVAL --> METRICS[Quality Metrics]
EVAL --> REVIEW[Human Review App]
DEPLOY --> LOG_MODEL[Log to MLflow]
DEPLOY --> SERVE_EP[Serving Endpoint]
MONITOR --> TRACES[Inference Traces]
MONITOR --> FEEDBACK[User Feedback]
LIFECYCLE:::governance
BUILD:::processing
EVAL:::storage
DEPLOY:::serving
MONITOR:::ingestion
DEFINE:::source
PROMPT_ENG:::source
TEST_LOCAL:::source
EVAL_SET:::source
METRICS:::source
REVIEW:::source
LOG_MODEL:::source
SERVE_EP:::source
TRACES:::source
FEEDBACK:::source
*Agent lifecycle: build, evaluate, deploy, and monitor.*
Key Terms
Prerequisites and Setup
- Databricks workspace with Unity Catalog and Model Serving.
databricks-agentsPython package.- Foundation Model APIs enabled.
- Vector Search endpoint for RAG tools.
Step-by-Step Implementation
Configuration Reference
| Parameter | Default | Description |
|---|---|---|
model_endpoint | — | Foundation model serving endpoint name |
max_tokens | 500 | Maximum response tokens |
temperature | 0.1 | Low temperature for consistent agent responses |
num_results (retriever) | 3 | Documents retrieved per query |
max_iterations | 5 | Maximum reasoning loop iterations |