Work / AI & Machine Learning
Financial Risk AI

MCP Intelligence Agent

An AI-powered financial risk intelligence agent built with LangGraph and Claude Sonnet 4, connected to 163 tools via the SAJHA MCP Server with SSE streaming.

163
Tools Available
LangGraph
Architecture
Claude Sonnet 4
Model
SSE
Streaming

Overview

An AI-powered financial risk intelligence agent built with LangGraph and Claude Sonnet 4, connected to 163 tools via the SAJHA MCP Server. Uses SSE streaming for real-time analysis.

The Challenge

Financial risk analysis requires synthesizing data across dozens of sources — market data, credit metrics, regulatory filings, internal models. Traditional dashboards show data; they don’t reason about it. Risk teams needed an AI agent that could orchestrate across multiple financial data tools, reason through complex risk scenarios, and stream findings in real time.

What We Built

Built a stateful AI agent using LangGraph’s graph-based orchestration framework with Claude Sonnet 4 as the reasoning engine. Integrated the SAJHA MCP (Model Context Protocol) Server giving the agent access to 163 financial tools. SSE (Server-Sent Events) streaming delivers analysis token-by-token. The agent can traverse multi-step workflows: retrieve data, compute risk metrics, cross-reference regulatory requirements, and synthesize a coherent risk assessment.

Results

  • 163 — Tools Available. Financial data and analysis tools via SAJHA MCP Server
  • LangGraph — Architecture. Stateful multi-step agent orchestration
  • Claude Sonnet 4 — Model. Anthropic's production reasoning model
  • SSE — Streaming. Real-time token streaming for live analysis
More Work

Related case studies.

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