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Udacity

Building Agents with Core Bioinformatics Tools

via Udacity

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Overview

This course equips learners with essential skills to create AI agents utilizing prominent bioinformatics frameworks. It begins with an introduction to agent development, followed by extending their functionality with Python and LangChain. Students will learn to manage structured outputs and implement state management systems in agents. The course covers short and long-term memory integration, database interactions, and the utilization of external tools and APIs. Additionally, learners will discover how to create web search agents and employ agentic retrieval augmented generation with ChromaDB. Finally, the course emphasizes agent evaluation and introduces UdaciScan, an AI research agent designed for drug-repurposing discoveries.

Syllabus

  • Course Introduction
    • Meet your instructors, review the course focus on practical AI agent building with LLMs, and learn how to set up and use your Vocareum OpenAI API key for hands-on labs.
  • Extending Agents with Tools
    • Extend AI agents beyond text with tool integrations, enabling reliable real-time actions and data access.
  • Building Agents with Tools in Python and LangChain
    • Learn to build Python agents using LangChain and OpenAI function calling, enabling LLMs to use external tools, parse data, and autonomously analyze clinical or drug safety signals.
  • Structured Outputs
    • Discover structured outputs in AI: transform responses into actionable JSON for integration. Utilize schemas, parsers, and function calls to enhance reliability and automation in workflows.
  • Implementing Structured Outputs with Pydantic
    • Learn to extract, validate, and structure genomic variant data from free-text using Pydantic and LLMs, enabling reliable, machine-readable outputs for bioinformatics workflows.
  • Agent State Management
    • Explore agent state management with state machines. Learn how agents track user input, instructions, and tool use for complex workflows, ensuring adaptability and reliability.
  • Implementing Agent State Management in LangGraph
    • Learn to manage agent state with LangGraph by building iterative state machines for life science workflows, using tools, Pydantic models, and conditional logic for controlled loops.
  • Short-Term Agent Memory
    • Explore short-term memory in AI agents, enhancing coherence via state, ephemeral, and ephemeral memory strategies for efficient context retention in active sessions.
  • Adding Agent Memory with LangChain
    • Learn how to implement short-term agent memory in LangChain, enabling AI to recall clinical context and patient details across chat turns using local or cloud LLMs and session state.
  • External Tools and APIs
    • Explore using external APIs for real-time data, dynamic actions, and authenticating agents. Discover MCP, a protocol standardizing AI’s tool interoperability and safety.
  • Integrating External Tools and APIs with LangChain
    • Learn to integrate external biomedical APIs with LangChain, enabling agents to query, analyze, and summarize data for clinical questions using PubMed and ClinVar.
  • Web Search Agents
    • Equip agents to search web for real-time, unstructured info. Ground responses in evidence using APIs, handle noise, and avoid hallucination for credible answers.
  • Creating Web Search Agents with LangChain
    • Learn to build a web-aware agent with LangChain that fetches, clusters, and summarizes life-sciences literature from arXiv and Google Scholar into concise, actionable digests.
  • Interacting with Databases
    • Equip agents to access and modify structured data by using SQL for interaction and vector databases for semantic tasks, ensuring seamless integration with private systems.
  • LangGraph Database Agents
    • Learn to build LangGraph database agents that use AI to convert natural language into SQL queries, enabling users to query databases conversationally without writing SQL.
  • Agentic Retrieval Augmented Generation
    • Discover Agentic RAG: Enhance RAG by enabling reflection, query reformulation, and intelligent adaption for nuanced answers. Master retrieval, reasoning, and retry loops.
  • Agentic RAG with ChromaDB
    • Learn to build agentic RAG systems for biomedical questions using ChromaDB—extracting key terms, retrieving evidence, and refining LLM answers with evidence-based, iterated workflows.
  • Long-Term Agent Memory
    • Explore long-term agent memory: understand semantic, episodic, and procedural memories. Learn storage strategies and best practices for personalized, coherent interactions.
  • Maintaining Long-Term Life Sciences Agent Memory
    • Learn to build a scientific agent that summarizes, stores, and retrieves miRNA–gene literature with long-term memory using vector search and domain filters.
  • Agent Evaluation
    • Agent Evaluation guides assessing an agent’s task completion, quality, tool use, and system metrics using response, step, or trajectory strategies to ensure reliable and efficient operations.
  • Evaluating Agents
    • Learn to evaluate biomedical agents by testing citation integrity, retrieval quality, quantitative and entity accuracy, and source attribution to ensure reliability and safety.
  • Project: UdaciScan - An AI Research Agent for Drug‑Repurposing Insights
    • UdaciScan is a LangChain-powered RAG agent that searches static + live biomedical literature, updates a ChromaDB store over time, and emits a structured, source-cited drug-repurposing brief.

Taught by

Tamas Madl and Henrique Santana

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