> ## Documentation Index > Fetch the complete documentation index at: https://docs.driven.ai/llms.txt > Use this file to discover all available pages before exploring further. # What Is an AI Investment Agent > An AI investment Agent combines a reasoning model with live financial data, specialized workflows, persistent memory, and automation. Learn how the pieces fit together and how it differs from a chatbot. An AI investment Agent is software that can research markets, analyze investments, remember your strategy, and act on a schedule, on your behalf. It is a step beyond an investing chatbot: where a chatbot answers a question and forgets you, an Agent has live data, structured workflows, persistent memory, and the ability to work while you are away. This article explains what an AI investment Agent is, how the pieces fit together, and why the assembly matters more than the model alone. For how Driven implements it, see [What is Driven](/get-started/what-is-driven). ## From chatbot to Agent A general AI chatbot is a smart reasoning core with real limits for investing. It can read financials, weigh arguments, and explain its thinking, but on its own it cannot see live market data, it does not remember your strategy between conversations, and it cannot do anything once you close the tab. It is a brain with no senses, no memory, and no clock. An AI investment Agent adds the missing pieces: * **Live data** — it pulls real financial figures from data sources instead of recalling them * **Specialized workflows** — it follows defined analytical frameworks rather than improvising each answer * **Persistent memory** — it remembers your universe, risk rules, and style across sessions * **Automation** — it can run tasks on a schedule, monitoring, briefing, and alerting on its own ## The Model + Harness framing A useful way to understand the category: an Agent is a **model plus a harness**. The model is the reasoning core. The harness is everything around it that makes the reasoning useful in the real world, the data connections, the workflows, the memory, and the runtime that lets it act. Most AI models are broadly comparable at the core reasoning layer. What separates a capable investing Agent from a chatbot is the harness: how well it is wired into live data, how well its workflows are designed, how durable its memory is, and what it can do autonomously. ## The four parts of the harness ### 1. Live data The Agent connects to financial data sources, quotes, statements, filings, holdings, and flows, and pulls figures from them rather than recalling from training. This is the difference between an answer you can rely on and one that might be confidently wrong. The breadth and quality of these connections is the first thing that separates Agents. ### 2. Specialized workflows Instead of writing a fresh, unstructured answer every time, the Agent runs defined workflows, frameworks with set data sources, steps, and quality checks. The same question runs the same way each time, which makes the analysis consistent and auditable. In Driven these are called [Skills](/concepts/skills). ### 3. Persistent memory The Agent remembers who you are: your investment universe, risk rules, watchlists, and research style, and applies them automatically to every analysis. Without this, you restate your strategy every session. With it, the Agent feels like it already knows you. In Driven this is the [Playbook](/concepts/playbook). ### 4. Automation The Agent can work without you actively asking. It runs briefings, monitors watchlists, and sends alerts on a schedule, and delivers them where you are. This is what turns a chat window into a workspace that keeps working while you are away. In Driven these are [scheduled tasks](/concepts/scheduled-tasks). ## What an investment Agent can do A capable Agent supports the full arc of a research process: * **Research** individual stocks, sectors, and macro themes with cited evidence * **Screen** for opportunities across fundamental, technical, and flow factors * **Remember** your strategy and apply it automatically to every analysis * **Monitor** your watchlist and portfolio and alert you to what matters * **Automate** recurring work like pre-market briefings and earnings tracking * **Simulate** trades and track ideas with paper trading ## How it comes together in practice Consider a Monday morning. With a chatbot, you would open it, restate your watchlist, and ask what happened over the weekend. With an Agent, the work is already done: it ran a pre-market scan against your watchlist (memory + data + automation), flagged the earnings relevant to your holdings (portfolio context), and summarized Friday's unusual volume (workflows). You open it and the briefing is waiting. That experience is not the model being smarter. It is the harness, data, workflows, memory, and automation, working together around a capable model. ## What it does not do An AI investment Agent is a research and analysis tool, not a decision-maker and not a licensed advisor. It gets you to a well-evidenced view faster; it does not make the call for you, and you remain responsible for your decisions. The best Agents are explicit about their evidence, their assumptions, and what they could not determine, so you can audit the reasoning rather than trust it blindly. ## How to evaluate one If you are comparing AI investment Agents, the questions that matter are the harness questions: * **Data** — does it pull live data from real sources, or recall from training? How broad is the coverage? * **Workflows** — does it follow consistent, auditable frameworks, or improvise each answer? * **Memory** — does it remember your strategy across sessions in a structured way? * **Automation** — can it work on a schedule, or only when you are actively asking? * **Coverage** — does it cover the markets and asset types you actually invest in? ## Where Driven fits Driven is a complete investing harness, live data from 245 data endpoints, purpose-built [Skills](/concepts/skills), a persistent [Playbook](/concepts/playbook), [scheduled tasks](/concepts/scheduled-tasks), and paper trading, around a capable model. See [What is Driven](/get-started/what-is-driven), or read how it compares to a [general chatbot](/why-driven/driven-vs-chatgpt-claude-gemini). ## Related * [AI vs traditional research](/learn/ai-vs-traditional-research) * [Building an investment workflow](/learn/building-an-investment-workflow)