Agentic AI For Financial Services

Explore the basics of LLM reasoning and planning, with a focus on applications in financial services, guided by industry expert.

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

Transform generic AI into expert financial advisors by iteratively refining prompts to define role, expertise, and communication style for tailored, actionable client advice.

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 enhance financial fraud detection with Python by applying Chain-of-Thought and ReACT prompting for systematic, transparent, and tool-integrated LLM reasoning.

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

Learn to refine AI prompts with Role, Task, Output Format, Examples, and Context using Python, generating expert, actionable financial analysis for client-ready reports.

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 build robust, auditable financial AI workflows by chaining specialized prompts with Python and OpenAI, using Pydantic models as validation gates for data integrity at each stage.

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 implement Python-based LLM feedback loops in finance, enabling multi-agent collaboration for quality-controlled, compliant investment recommendations through iterative refinement.

Learn how AI agents replace rigid rules to detect fraud in SWIFT transactions. Start your journey into adaptive financial workflows.

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.

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

Learn to implement agentic workflows for SWIFT transaction processing using specialized AI agents for validation, fraud detection, risk assessment, and orchestration.

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 multi-agent prompt chaining workflows in Python, with each agent sequentially analyzing SWIFT transactions to produce a comprehensive, consensus-based decision.

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 for SWIFT messages in Python using LLMs to intelligently triage transactions through specialized processing workflows.

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

Learn to build scalable multi-agent fraud detection workflows in Python using parallel processing, agent design, and modular aggregation for fast, adaptable SWIFT message analysis.

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 build Python workflows that validate, optimize, and auto-correct SWIFT messages using LLMs for efficient message processing and auditability in financial systems.

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

Learn how to implement the agentic Orchestrator-Workers pattern in Python to automate SWIFT post-processing, generating dynamic compliance and audit reports with modular, extensible agents.

Learn to build AI agents for financial services that retrieve, analyze, and synthesize data, mimicking expert analyst reasoning using Python, APIs, and agentic workflows.

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

Learn to build AI financial agents in Python by defining functions as tools, using JSON schemas, and orchestrating multi-step conversations for accurate, explainable analysis.

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 enforce strict, validated JSON schemas on AI outputs for finance using Pydantic, ensuring reliable, type-safe, and fail-safe structured data ready for real applications.

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 build reliable, auditable AI agents for financial workflows in Python using state machines, dataclasses, and processor classes to manage multi-step processes with data integrity.

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

Learn to build AI assistants for financial services with Python by implementing short-term memory: sliding window, running summary, and structured data extraction for better context retention.

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 build AI agents in Python that fetch live financial data using APIs, extract info with LLMs, calculate costs, and interactively handle incomplete user queries.

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 Python AI agents for real-time financial research by integrating web search APIs, filtering credible sources, using LLMs for data extraction, and delivering concise summaries.

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 AI agents in Python that convert plain English into safe SQL for finance, with validation, safety guardrails, retry logic, and clear natural language summaries.

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 an autonomous RAG agent with Python and ChromaDB for finance, featuring self-evaluation, improved search queries, reliable answers, and confidence assessment.

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

Learn to implement persistent, intelligent long-term memory for AI agents in Python using PostgreSQL, SQLAlchemy, and LLMs for consistent, context-aware financial advice.

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.

Discover how to evaluate agentic RAG systems in financial services with Python, using accuracy, citation, and retrieval metrics for objective, actionable AI performance insights.

Learn about the course, prerequisites, and technical environment.

Explore how specialized agents collaborate in a multi-agent AI architecture for hedge funds, focusing on data, strategy, risk, compliance, and trade execution flows.

Learn to implement multi-agent architectures in Python for financial services using Pydantic AI, shared context, clear agent roles, and strict output structures for robust communication.

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

Learn how to implement agent orchestration in financial services by combining deterministic flows with AI-powered decision making, using orchestration patterns and structured output models.

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

Learn to implement routing strategies, agent pooling, and data flow orchestration for multi-agent financial systems using LLM and classic methods to balance loads and optimize message handling.

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

Learn to implement persistent, auditable state management in multi-agent trading systems using CSV files, ensuring consistent, reliable execution and separation of decision and action.

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

Learn to coordinate multiple specialist AI agents for financial trade processing using state management, conflict detection, and atomic transactions for robust multi-agent orchestration.

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

Learn how to build a Multi-Agent Retrieval-Augmented Generation system for finance, coordinating specialized agents for comprehensive trading analysis and recommendations.