When financial workflows become too complex for simple parallel processing or chaining, the orchestrator-worker pattern provides dynamic coordination. Unlike fixed workflows, an orchestrator analyzes problems at runtime, breaks them into subtasks, and delegates work to specialized agents. This is the pattern that ties together everything we’ve learned - bringing intelligent coordination to financial operations.
Parallel Processing and Quality Control in Finance
Financial analysis often requires examining multiple dimensions simultaneously - market data, sentiment, risk factors, regulatory compliance. Rather than processing these sequentially, parallel workflows distribute work across multiple agents for speed and diverse perspectives. Combined with evaluator-optimizer patterns for quality assurance, these techniques form the backbone of production-grade financial AI systems.
Prompt Chaining and Routing in Trading Systems
Trading systems process diverse request types - market orders, limit orders, options, compliance checks - each requiring specialized handling. Two fundamental patterns enable this: prompt chaining for sequential multi-step processing, and routing for intelligent task distribution. Mastering these patterns is essential for building robust financial workflows.
Modeling Agentic Workflows for Finance
Moving from individual prompts to production systems requires thinking architecturally. Financial workflows aren’t just chains of prompts - they’re coordinated systems where multiple specialized agents work together. Understanding how to model these workflows is essential for building reliable, maintainable financial AI systems.
Building Financial Prompt Pipelines
Once you have effective prompts for individual tasks, the next challenge is connecting them into reliable workflows. Financial services demand more than single-shot responses - they require multi-stage pipelines with validation at every step. Prompt chaining and feedback loops are the mechanisms that transform individual prompts into robust, production-ready systems.
Reasoning Chains for Financial Decisions
Financial decisions require more than quick answers - they demand transparent, auditable reasoning. When an AI flags a transaction as fraudulent or recommends a portfolio rebalancing, stakeholders need to understand why. Chain-of-Thought and ReACT prompting techniques transform LLMs from black-box responders into systematic reasoners whose logic can be traced, verified, and trusted.
Role-Based Prompting for Financial Analysts
When you ask an LLM to “help with financial planning,” you might get a generic response that misses the nuance your situation requires. But what if you could transform that same LLM into a specialized Certified Financial Planner with 10 years of experience in debt management and retirement planning? That’s the power of role-based prompting, and it’s particularly valuable in financial services where precision and expertise matter.
Building Intelligent AI Systems - A Complete Guide to Agentic AI
Over the past few months, I’ve been exploring the world of agentic AI - systems where language models don’t just generate text, but reason, plan, and take action. This post serves as both an introduction and a roadmap to the complete series, sharing my thoughts on the key concepts, practical patterns, and how to get started building your own intelligent agents.
Multi-Agent RAG and Building Complete Systems
Standard RAG retrieves from a single source, but real problems often require information from multiple specialized domains. Multi-Agent RAG coordinates multiple retrieval specialists, each expert in querying specific data sources, then synthesizes their findings into coherent answers. In this final post of the series, I’ll explore Multi-Agent RAG patterns and bring together everything we’ve learned into complete, production-ready systems.
Multi-Agent Routing, State, and Coordination
When multiple agents work together, three challenges emerge: how do requests reach the right agent (routing), how does information flow between agents (data flow), and how do agents maintain a consistent view of the world (state coordination). In this post, I’ll explore patterns for managing these critical aspects of multi-agent systems.