Agents that reason, not just respond.
A single LLM call can't handle multi-step decisions — it needs to gather data, weigh trade-offs, and hand off between specialized roles. Most "AI agent" products are a thin wrapper around one prompt.
A multi-agent system where specialized agents (research, analysis, risk-checking, decision) work together, with a human approval gate before anything ships — orchestrated with LangGraph, not glued together with prompts.
View the source — Finance AI Agent on GitHub
View Source ↗Ticker Input -> Data Pipeline (yfinance/SEC/Tavily) -> Bull vs Bear Debate -> Risk Audit -> CIO Verdict -> Human Approval -> PDF Report
For simple business AI implementations — chatbot, FAQ assistant, basic knowledge base.
Workflow automation, AI agent, API/Telegram/email integrations.
RAG, NL2SQL, business analytics, document intelligence, advanced agents.
For larger or more complex requirements — scoped on a call.
Directional, not a fixed quote — real scope depends on your data and integrations.
An agent plans across multiple steps, can call different tools depending on what it finds, and — in systems like the Finance AI Agent case study — has specialized roles that debate before a decision is made. A chatbot with tools typically just calls one function per turn.
Yes, that's a deliberate design choice — a human-in-the-loop approval gate is built in before any consequential output ships, not fully autonomous by default.
Primarily LangGraph for stateful, multi-agent orchestration — chosen because it makes the decision flow explicit and debuggable, not a black box.
Tell me what you're trying to solve — I'll tell you honestly whether this is the right fit.