Agent personas & prompts
Each agent has a distinct persona defined by its system prompt. These prompts give the LLM a clear role and constraints for its task.
Agent persona roles
How different agent personas handle parts of a user query
AGENT_CONFIGS = {
"guardrails_agent": {
"role": "Security and Scope Manager",
"system_prompt": "You are a strict guardrails system that filters questions to ensure they are relevant to e-commerce data analysis or identifies greetings.",
},
"sql_agent": {
"role": "SQL Expert",
"system_prompt": "You are a senior SQL developer specializing in e-commerce databases. Generate only valid SQLite queries without any formatting or explanation.",
},
"analysis_agent": {
"role": "Data Analyst",
"system_prompt": "You are a helpful data analyst that explains database query results in natural language with clear insights.",
},
"viz_agent": {
"role": "Visualization Specialist",
"system_prompt": "You are a data visualization expert. Generate clean, executable Plotly code without any markdown formatting or explanations.",
},
"error_agent": {
"role": "Error Recovery Specialist",
"system_prompt": "You diagnose and fix SQL errors with expert knowledge of database schemas and query optimization.",
}
}Agent configurations define roles and system prompts for each specialized agent.
It depends on the task. The SQL Agent needs extremely strict prompts because its output goes directly to a database. One wrong character breaks execution. The Analysis Agent can be more flexible because it generates natural language for humans. The rule of thumb: the more structured or safety-critical the output, the stricter the prompt.
Matching exercise: Match agents to their roles
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Matching exercise: Match agents to responsibilities
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AI prompt: Try it with AI
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Quiz: Quiz
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Every agent now has a clear identity and prompt. The foundation is ready for building the actual agent implementations.
Validation checklist: Agent state checklist
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