Post by Christian Lira
I help organizations deliver ROAI results through AI-Native Program & Product TPM Leadership | Bridging Decision Science Data Science & Strategy | High-Impact ROAI | Enterprise Digital Transformation
I built a production-ready, multi-agent AI orchestration system built natively on Anthropic's Claude API. I am professionally happy to share that outside of my role at AWS, I am actively building a portfolio of production-grade multi-agent AI systems on Anthropic's Claude platform which is the work I believe defines the next generation of enterprise AI leadership. My first project is a fully orchestrated, multi-agent decision intelligence system that coordinates six specialist Claude agents to deliver boardroom-quality business analysis from a single natural language question. Built from scratch using the Anthropic Claude API, Python, and Anthropic's tool use protocol. It is designed to receive a raw business question and return a structured, boardroom-quality decision intelligence report without human intervention between steps. The system coordinates six specialist Claude agents — a business intake analyst, market researcher, financial risk analyst, financial analyst, technology build expert, and a CEO-level executive synthesizer — under a central orchestrator that manages sequencing, context passing, and final synthesis. Each agent is defined by a purpose-built system prompt that establishes a distinct expert identity using a single Claude model, with no fine-tuning required. The financial analyst agent executes live Python calculations through Anthropic's two-turn agentic loop — Claude signals tool use, Python runs the math, and results are returned with full conversation history for Claude to interpret and deliver as financial analysis. Built from scratch across ten immersive sessions covering the complete Anthropic agentic stack: authentication, the Messages API, system prompt engineering, multi-agent pipeline design, tool use protocol, stop_reason handling, tool_use_id matching, and timestamped file output. Directly applicable to: investment analysis, go-to-market assessment, and operational risk review at the enterprise level. Built with: Python, Anthropic Claude API, claude-haiku-4-5-20251001 I am thankful to thousands of people that help us to learn, innovate and build real technology based solutions for customers, and thank you #Anthropic #Claude #Haiku #Python for all you do for the World. Read me on my read me file here. https://lnkd.in/eewX95TS #AI #ML #Amazon #AWS #DecisionScience #DataScience #MIT #CWU #VT #JMU #CUNY #HBR Ari Chanen Vikas Kumar Muhammad Aurangzeb Ahmad Jeff Lumpkin Sal Uslugil