Improve Agent Scalability with Dependency Injection in PydanticAI
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💡Tutorial Examples: A diverse scenarios of agent use cases:
1️⃣ Career advisor (single agent) - an example to demonstrate how to use dependency injection in Pydantic AI to pass additional context to the system prompt. We define a system prompt that uses dependency injection to pass the user's industry specialization and ask for career advice. The system prompt uses the context to provide a more personalized response to the user.
2️⃣ Insurance company CSR (single agent) - the use case is a customer service representative for an insurance company. The agent is expected to take the customer's question, classify it appropriately (e.g., billing, technical support, general inquiry), determine if the issue needs to be escalated, and provide a clear and helpful response.
3️⃣ Loan qualifier (single agent) - the use case is a loan officer agent evaluating the credit-worthiness of an applicant and approving or denying a loan application. The agent is expected to take the applicant's information, evaluate the credit score, determine the loan amount, and provide a clear and helpful response.
4️⃣ Medical team of specialists (multi-agent with a reasoning DeepSeek R1) - the use case is a team of medical agents providing a diagnosis for a patient case. The agents are expected to take the patient's symptoms, evaluate the patient's condition, and provide a clear and helpful response.
5️⃣ Stock broker (single agent) - the use case is a stock market researcher agent that takes a stock name, retrieves the stock symbol, and fetches the latest stock price. The agent is expected to provide the stock symbol and the latest stock price for the given stock name.
Masterclass Series:
▶️ Part 1: • The Best Agent Framework Has Arrived ...
▶️ Part 2: • From Chaos to Clarity: LLM Tracing wi...
▶️ Part 3: • 100% Reliable LLM Outputs with Struct...
▶️ Part 4: • Dramatic Improvement! Design Better A...
▶️ Part 5: • Design Better AI Agents With Function...
▶️ Part 6: • Transform You Agents with Result Vali...
▶️ Part 7: • Improve Agent Scalability with Depend...
▶️ Part 8: • Build More Reliable Agents with Retri...
▶️ Part 9: Better Context Retention with Agent Memory in PydanticAI
▶️ Part 10: Building Resilient Agents: Best Practices for Handling Model Errors in PydanticAI
▶️ Part 11: Better User Experience with Streaming Outputs in PydanticAI
▶️ Part 12: Achieving Precision and Efficiency with Advanced Model Settings
▶️ Part 13: Multi-Model Agents in PydanticAI: Unlocking Next-Gen AI Capabilities
▶️ Part 14: Mastering RAG in PydanticAI: Better AI Agents with Real-Time Data
▶️ Part 15: Masterclass Final Project: AI Resume Writing with Multiple Agents
Today we’re taking a detailed look at Dependency Injection: a feature that’s unique to PydanticAI. Dependency Injection (DI) is a software design pattern that promotes loose coupling by injecting dependencies (such as services, objects, or configurations) into a component rather than having the component create them itself. This improves testability, maintainability, and flexibility, making code easier to manage and scale.
PydanticAI uses a dependency injection system to provide data and services to your agent's system prompts, tools and result validators.
DI in PydanticAI uses existing best practice in software development in order to make dependencies type-safe, understandable, easier to test and ultimately easier to deploy in production. We will see examples of how to use DI in all three cases, so stick around.
🎯 Whether you're building a chatbot, an AI agent, or any other LLM-powered system, this tutorial provides practical examples to elevate your application’s ability to get better outputs from AI.
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