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...

OpenClaw is best understood as a self-hosted gateway plus agent runtime system , not as a framework where every user-facing agent is a Python class or long-running daemon. The Gateway receives messages from channels, resolves them to an agent and session, runs one serialized agent turn, records transcript state, executes tools, and delivers the answer back through the originating channel. The official agent-loop docs summarize the core path as “intake → context assembly → model inference → tool execution → streaming replies → persistence,” with one serialized run per session.  That matters when building something like a deterministic financial, like our DBOT - analyst that must compute DCFs, query financial facts, rank companies, and never hallucinate numbers. In OpenClaw, the right design is usually: Agent = prompt/config/session/runtime scope Skill = markdown guidance for when/how to act Tool = typed callable capability MCP server = cross-language tool server, ideal for Python/p...