---

name: retail-agent-learner description: Learn ADK concepts and understand the retail agent pipeline. Use when asking how the agent works, what ADK is, wanting to understand the architecture, state flow, or individual agent purposes.

Retail Agent Learner

Pipeline Overview

This is an 8-agent pipeline for retail site selection analysis:

CopyUser Query
    ↓
IntakeAgent ────────────→ Extract location + business type
    ↓
MarketResearchAgent ────→ Google Search for demographics
    ↓
CompetitorMappingAgent ─→ Google Maps Places API
    ↓
GapAnalysisAgent ───────→ Python code execution (pandas)
    ↓
StrategyAdvisorAgent ───→ Extended thinking synthesis
    ↓
ParallelAgent ──────────→ Concurrent artifact generation
    ├── ReportGenerator ─→ HTML report
    ├── InfographicAgent → Image generation
    └── AudioOverview ───→ TTS podcast audio

What Each Agent Does

Key ADK Concepts

Agents

• LlmAgent: Single LLM call with tools and instructions
• SequentialAgent: Runs sub-agents one after another
• ParallelAgent: Runs sub-agents concurrently

State Flow

Data passes between agents via session state:

CopyIntakeAgent → state["target_location"], state["business_type"]
MarketResearchAgent → state["market_research_findings"]
CompetitorMappingAgent → state["competitor_analysis"]
GapAnalysisAgent → state["gap_analysis"]
StrategyAdvisorAgent → state["strategic_report"]

Tools

Functions that agents can call to perform actions. Access state via ToolContext.

Callbacks

Lifecycle hooks: before_agent_callback and after_agent_callback.

Learning Path

Start with the 9-part tutorial series:

1. Part 1: Setup + First Agent
2. Part 2: IntakeAgent
3. Part 3: MarketResearchAgent
4. Part 4: CompetitorMappingAgent
5. Part 5: GapAnalysisAgent (code execution)
6. Part 6: StrategyAdvisorAgent (extended thinking)
7. Part 7: ArtifactGeneration (parallel outputs)
8. Part 8: Testing
9. Part 9: Production Deployment

[See references/architecture.md for detailed data flow] [See references/adk-concepts.md for ADK deep dive]