I Tried to Secure an AI Trading Bot. I Was Solving the Wrong Problem
Oh, man... Over the last few months I have been working through what started as a relatively straightforward question: what would it actually take to build a useful AI-native financial agent? The first version used LangGraph and LangSmith and was deliberately conservative. The LLM could interpret the user's request, decide which operations were necessary and explain the result, but the financial calculations themselves lived in deterministic code. That separation was important because I did not want a language model improvising portfolio mathematics or inventing numbers. From there I moved to OpenClaw , and the scope expanded considerably. Tools became more explicit, execution could persist beyond a single request, MCP provided a cleaner interface between the agent and deterministic financial services, and eventually I started experimenting with agent-to-agent designs: a research agent collecting information, another agent interpreting it, a portfolio component considering ...