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? ( 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...

 Introduction In the previous article , we explored how to build a financial agent using OpenClaw as a foundation for real-world automation. That implementation demonstrated an important principle: modern AI systems become useful not when they generate text, but when they interact with structured tools and data pipelines . This article extends that idea toward a more ambitious goal: Building a Damodaran-style valuation bot (DBOT)  - a system that performs structured equity valuation using established financial methodologies. The objective is not to invent a new valuation framework. Instead, the goal is to: reuse well-known financial models (DCF, comparables, sensitivity analysis) integrate them with LLM-based reasoning orchestrate everything through a multi-agent system use OpenClaw as the execution and interaction layer This first part focuses on system design and conceptual architecture. Later parts would move into implementation details. DBot Why Build a Damodaran Bot? Equi...

  In the previous article , we built a FinChat-style financial research agent using LangGraph and LangSmith. That system established a clean baseline: structured data, explicit workflows, deterministic operations, and observable traces. It deliberately avoided embeddings and retrieval in order to keep reasoning and execution transparent. That baseline is useful - but it is also fragile. This article examines where the initial design breaks down as the system grows, and how introducing OpenClaw changes the role of execution from “some code ran” into a formal, auditable system . The goal is not to add new capabilities, but to make correctness and failure explicit properties of the system. Recap The first version of the agent had several strong properties: Natural language queries were mapped to a structured QueryPlan All financial logic was deterministic and testable Agent behavior was modeled as an explicit graph Traces and lightweight evaluation were available via La...