Foundations of Agentic AI Systems

Deconstruct the core components of an AI agent: LLM, memory, planning, and tools.

Build a simple function-calling agent using an LLM API to interact with external tools.

Implement advanced prompt engineering techniques for financial data extraction and analysis.

Design and implement an agent workflow using the ReAct (Reason-Act) framework.

Implement short-term memory for an agent to maintain context across multiple interactions.

Implement a long-term memory system using a vector database for Retrieval-Augmented Generation (RAG).

Evaluate the trade-offs between different LLM providers (e.g., cost, latency, accuracy) for project finance tasks.

Build a simple router agent that selects the appropriate tool based on user queries.