Source URL: https://blog.scottlogic.com/2025/07/17/visualising-the-trade-lifecycle-phase-1-building-a-react-spa-with-multiple-ais.html
Source: Scott Logic
Title: Visualising the Trade Lifecycle – Phase 1 – Building a React SPA with Multiple AIs
Feedly Summary: A non-React developer built a trade lifecycle simulation using three AI assistants as his coding team, discovering that managing AI agents is rather like conducting an orchestra where each musician excels at different parts of the piece but occasionally abandons the score for a spot of impromptu improvisation. The project demonstrated that whilst AI collaboration can be very useful, someone still needs to wave the baton when your string section decides to have a go at bebop when they should be playing Beethoven.
AI Summary and Description: Yes
**Summary:** The text details the author’s journey in developing a React-based application to simulate a trade processing workflow, leveraging multiple AI tools for coding assistance. It highlights practical insights on orchestrating different AI capabilities, managing context for seamless integration, and the importance of human oversight in ensuring quality output.
**Detailed Description:**
The author’s narrative revolves around building a React Single Page Application (SPA) that visualizes a hybrid trade processing workflow. Several significant factors emerge throughout the project, showcasing the integration of AI tools like ChatGPT, Claude, and Microsoft Copilot, and their distinct utility in different aspects of the development. Here’s an expanded view of the critical insights and practical implications for security and compliance professionals:
– **Project Inspiration and Structure:**
– The application simulates a full trading day at an accelerated pace, incorporating both on-premises and cloud workflows.
– Features include cloud auto-scaling, chaos engineering capabilities, and dynamic trade volume counters.
– **Multiple AI Assistants:**
– The use of various AI tools was driven by necessity and curiosity. Every AI brought unique strengths:
– **ChatGPT:** Excelled in system architecture and high-level simulation logic.
– **Claude:** Focused on design and interface development.
– **Copilot:** Specialized in code implementation and refactoring.
– This orchestration allowed for a strategic division of tasks, maximizing each AI’s strengths.
– **Development Insights:**
– **Human-in-the-Loop Quality Control:** The importance of human judgment is evident as the author regularly intervened to refine AI outputs, ensuring quality and adherence to project goals.
– **Documentation’s Role:** Maintaining clear documentation became essential for context switching between AI tools, which helped preserve coherence and continuity across the project.
– **Technical Architecture and Workflow Management:**
– The final output demonstrated real-time tracking of trade lifecycles, with a strong focus on cloud versus on-premises functionality and the dynamics of their interaction.
– The need for chaos engineering showcased an understanding of market volatility, critical for security and disaster recovery planning in financial sectors.
– **Challenges Encountered:**
– The author faced limitations related to the free-tier usage of the AIs, pushing them to innovate how they utilized available resources.
– Frequent switching of AIs often required contextually reintroducing information, emphasizing the operational challenges of multi-agent workflows.
– **Looking Ahead:**
– Future exploration with new tools like Cursor IDE hints at the ongoing evolution of leveraging AI in development environments, foreshadowing a deeper integration and smarter workflows.
**Key Implications for Security and Compliance Professionals:**
– As organizations increasingly adopt AI for software development, understanding the importance of orchestration, human oversight, and context management becomes crucial.
– Developing infrastructures that can adapt to hybrid environments and support multi-agent collaboration will be vital for security, compliance, and efficient operations.
– Continuous learning about operational challenges and failures in AI outputs can lead to more robust strategies for project management and quality assurance in tech-driven projects.
This insightful case study serves as a guide for software security professionals looking to integrate AI in their workflows while also reflecting on the implications for system resilience and compliance.