Agentic AI for Life Sciences

Explore core concepts of agentic AI in life sciences, meet your instructors, and set up Vocareum OpenAI API keys for hands-on learning.

Learn to protect sensitive health and genomic data by understanding privacy risks, key regulations (HIPAA, GDPR), and security measures essential for trust in life sciences AI.

Explore how to ensure healthcare AI is safe and effective, covering regulatory compliance, risk analysis, SaMD, lean assurance, bias mitigation, and ongoing post-market vigilance.

Learn essential practices for building trust and accountability in AI within the life sciences: documentation, traceability, good governance, and ethical standards in high-stakes, regulated fields.

Introduces the core concepts of Agentic AI, the course structure, prerequisites, and learning environment.

Explains the theory of using roles or personas to control the tone, style, and expertise of an LLM's output.

Learn to create effective role-based prompts in Python, guiding AI to emulate expert personas like pathologists or genetic counselors for structured, safe, and professional outputs.

Explains the conceptual frameworks for Chain-of-Thought (CoT) for guided reasoning and ReAct (Reason+Act) for enabling agents to plan and take actions.

Learn to implement Chain-of-Thought (CoT) and ReAct prompting in Python to enable structured agent reasoning and tool-using workflows for biomedical tasks.

Explains the theory of systematically refining prompt instructions by modifying components like Role, Task, Context, Examples, and Output Format.

Learn to iteratively refine Python prompts for regulated, auditable, and machine-validated LLM outputs, using role, task, and format adjustments in real-world health data scenarios.

Explains the conceptual framework for building multi-step AI workflows by linking the output of one prompt to the input of the next, and the importance of validation.

Learn to implement robust multi-step prompt pipelines in Python using LangChain, ensuring validated, error-free outputs for pharmacovigilance signal reporting workflows.

Explains the conceptual framework for building self-improving systems where an agent uses feedback from its own actions to iteratively refine its output.

Learn to build self-correcting feedback loops for LLMs in Python, enabling programmatic evaluation and revision for reliable, audit-friendly outputs in life sciences workflows.

Explore the course overview, meet your instructors, and learn how to access and use the Vocareum OpenAI API key for hands-on projects.

Compare deterministic vs agentic workflows using PubMed: rule-based filters vs LLM-powered analysis, highlighting agentic flexibility in context understanding and structured outputs.

Explores what defines a modern AI agent, its core components (Persona, Knowledge, Tools, Interaction), and the different types of agents based on their LLM interaction model.

Explore modern AI agentic workflows: designing agents with goals, tools, memory, and reasoning via hands-on demos and exercises contrasting agentic vs. deterministic approaches.

Design and visualize agentic workflows. Learn common agent types as building blocks for creating visual workflow diagrams.

Compare linear and multi-agent models, and implement a biomedical literature triage using parallel agents, LLM scoring, and evidence extraction.

Covers the practical aspects of translating agentic workflow models into Python code. Students learn to structure agent logic, define agent classes, and orchestrate their interactions.

Learn to design, implement, and test a sequential agentic workflow in Python, passing data through specialized agents to solve complex tasks like cell authentication and drug repurposing.

Introduces the Prompt Chaining pattern for breaking down complex tasks into a sequence of smaller, dependent steps. It covers strategies for task decomposition, validation, and context management.

Learn to build agentic prompt chaining workflows in Python, creating multi-step LLM pipelines with explicit handoffs for complex, structured, and regulatory data processing.

Teaches the Routing pattern, which involves classifying incoming tasks and directing them to the most appropriate specialized agent or processing path.

Learn to implement agentic routing workflows in Python using LLMs for request classification, context extraction, and dispatching to specialist agents for structured, reliable recommendations.

Introduces the Parallelization pattern for executing multiple agent tasks concurrently. It covers strategies for task decomposition (sharding, aspect-based) and result aggregation.

Learn to design agentic parallel workflows in Python: run independent analyses concurrently, aggregate results, and synthesize findings for robust, efficient decision-making.

Focuses on the Evaluator-Optimizer pattern, an iterative process of generation, critique, and refinement to improve output quality. It emphasizes clear evaluation criteria and actionable feedback.

Learn to design agentic evaluator-optimizer workflows in Python, using AI agents to iteratively improve clinical study documents through multi-criteria evaluation and revision loops.

Introduces the advanced Orchestrator-Workers pattern, where a central agent dynamically plans, delegates, and synthesizes the work of multiple specialized worker agents.

Learn to implement the Agentic Orchestrator-Worker pattern in Python, coordinating specialized agents for protocols, equipment, and safety into a unified experimental workflow.

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.

Extend AI agents beyond text with tool integrations, enabling reliable real-time actions and data access.

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.

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.

Learn to extract, validate, and structure genomic variant data from free-text using Pydantic and LLMs, enabling reliable, machine-readable outputs for bioinformatics workflows.

Explore agent state management with state machines. Learn how agents track user input, instructions, and tool use for complex workflows, ensuring adaptability and reliability.

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.

Explore short-term memory in AI agents, enhancing coherence via state, ephemeral, and ephemeral memory strategies for efficient context retention in active sessions.

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.

Explore using external APIs for real-time data, dynamic actions, and authenticating agents. Discover MCP, a protocol standardizing AI’s tool interoperability and safety.

Learn to integrate external biomedical APIs with LangChain, enabling agents to query, analyze, and summarize data for clinical questions using PubMed and ClinVar.

Equip agents to search web for real-time, unstructured info. Ground responses in evidence using APIs, handle noise, and avoid hallucination for credible answers.

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.

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.

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.

Discover Agentic RAG: Enhance RAG by enabling reflection, query reformulation, and intelligent adaption for nuanced answers. Master retrieval, reasoning, and retry loops.

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.

Explore long-term agent memory: understand semantic, episodic, and procedural memories. Learn storage strategies and best practices for personalized, coherent interactions.

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 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.

Learn to evaluate biomedical agents by testing citation integrity, retrieval quality, quantitative and entity accuracy, and source attribution to ensure reliability and safety.

Meet your instructors and get started with building multi-agent AI systems, learning architecture, orchestration, and using Vocareum OpenAI API keys for hands-on projects.

Explain the core components of multi-agent systems and how to design their high-level architecture.

Learn to build a multi-agent AI system with an orchestrator that routes genomics queries to specialist agents using real APIs for frequency, significance, literature, and trials.

Develop a multi-agent system by coding the designed architecture and connecting agents with well-defined interfaces.

Learn to design and implement multi-agent architectures in Python, integrating specialists with orchestrated logic and API tools for real-world life sciences workflows.

Apply orchestration techniques to coordinate multiple agent actions and achieve complex workflows.

Learn to build stateful agent workflows using sequential, parallel, and conditional orchestration for drug-target analysis and service desk automation in life sciences.

Configure routing mechanisms to manage data flow among agents in multi-agent systems.

Learn to design agentic systems that use LLMs and priority queues for scalable content-based and priority-based routing in real-world scenarios.

Evaluate methods for tracking and updating agent state across multi-turn interactions.

Learn how to manage shared state in multi-agent systems through demos and exercises, enabling coordination between agents for collaborative tasks using thread-safe designs.

Develop a coordinated multi-agent system that synchronizes states for coherent task execution.

Learn multi-agent orchestration by coordinating access to shared lab resources, preventing conflicts through atomic state updates and locks, with priority scheduling of concurrent bookings.

Extend RAG to multiple cooperating agents, each specialized in certain retrieval tasks.

Learn how to build Multi-Agent RAG systems that retrieve domain-specific evidence in parallel and synthesize concise, cited reports for clinical or scientific queries.