Deep Research and Development

 In the previous article , we moved from the idea of a Damodaran-style financial bot toward a practical multi-agent architecture: a system where different agents are responsible for data retrieval, assumptions, valuation, supervision, and report writing. That architecture gave us the direction of travel. The next question is more concrete:  how do we turn that architecture into a project that can be tested, evolved, and eventually connected to real financial data sources? The goal is to build a small, deterministic, testable version of the system that proves the shape of the application before we introduce live data, complex assumptions, model drift, flaky APIs, or LLM variability. A financial agent system can become difficult to debug very quickly. If an answer is wrong, the source of the problem may be the financial data, the normalization logic, the assumption layer, the valuation formula, the scenario model, the report writer, the agent instruction, or the orchestration pa...

In the previous article, we moved from the idea of a Damodaran-style financial bot toward a practical multi-agent architecture: a system where different agents are responsible for data retrieval, assumptions, valuation, supervision, and report writing. That architecture gave us the direction of travel. The next question is more concrete: how do we turn that architecture into a project that can be tested, evolved, and eventually connected to real financial data sources? ( Medium ) This article continues from that point by focusing on the proof-of-concept implementation. The goal is not to build a production-grade valuation platform in one jump. The goal is to build a small, deterministic, testable version of the system that proves the shape of the application before we introduce live data, complex assumptions, model drift, flaky APIs, or LLM variability. That distinction matters. A financial agent system can become difficult to debug very quickly. If an answer is wrong, the source of th...