Key takeaway: You can build a multi-agent investment research team in under 30 minutes using a visual builder, YFinance data, HackerNews sentiment, and web search. The agents handle data gathering so your analysts can focus on judgment. But agents don't replace financial expertise. They amplify it.
A friend who runs a small fund asked me if AI could help his analyst team screen 50 companies a week instead of 20. I said yes, then spent a weekend proving it with three agents and a visual builder.
His problem was simple and common. His team of four analysts was spending roughly 60% of their time on data gathering: pulling financial statements, reading earnings call transcripts, scanning news, checking sentiment on forums. By the time they had all the inputs, they had maybe two hours left to actually think about whether a company was worth investigating further.
That's not an analysis problem. That's a plumbing problem.
And plumbing problems are exactly what AI agents are good at solving.
What a three-agent research team looks like
The setup I built uses Agno Builder's Investment Research Team template as a starting point. It consists of three agents connected to a coordinator team node. Here's how each agent works.
Agent 1: The Data Collector. This agent uses the YFinance tool to pull real-time stock data, historical prices, key financial ratios, and company profiles. You give it a ticker symbol and a time range, and it returns structured financial data. No API key required for YFinance, which keeps the setup simple.
Agent 2: The Sentiment Scanner. This agent combines HackerNews and DuckDuckGo web search tools. It scans recent news articles, forum discussions, and tech community sentiment around a company. For tech stocks especially, HackerNews discussions often surface concerns (or enthusiasm) that don't show up in analyst reports for weeks.
Agent 3: The Brief Writer. This agent takes the outputs from the other two and synthesizes a one-page investment brief. It includes a summary of financial health, recent sentiment trends, key risks, and a preliminary assessment. The instructions tell it to be conservative, flag uncertainties explicitly, and never make buy/sell recommendations.
The coordinator pattern ties them together. When a user sends a query like "Screen NVDA for the last quarter," the coordinator routes the request to the Data Collector and Sentiment Scanner in parallel, waits for their results, then passes everything to the Brief Writer for synthesis.
Building this in Agno Builder took me about 25 minutes. I dragged three agent nodes onto the canvas, connected them to a team node, configured the tools and instructions for each, and tested it in the chat panel.
Building the equivalent in raw Python took me about two hours. Not because the code is hard, but because of all the small decisions: import paths, tool configurations, team wiring, error handling, streaming setup. The visual builder compresses all of that into form fields and dropdown menus.
Walking through the build, step by step
Let me be specific about how this works in practice.
Step 1: Open the Investment Research Team template. Agno Builder ships with 11 templates. The Investment Research Team is one of them. Loading it gives you a pre-configured canvas with agent nodes already connected. You can use it as-is or customize everything.
Step 2: Configure the Data Collector agent. Click the agent node, and the config panel opens on the right. I set the model to GPT-4o (good balance of speed and reasoning for financial data interpretation), enabled the YFinance tool, and wrote instructions like: "You are a financial data specialist. When given a company ticker, retrieve the current stock price, 52-week range, P/E ratio, market cap, revenue growth, and debt-to-equity ratio. Present data in a structured format. Flag any metrics that deviate significantly from sector averages."
Step 3: Configure the Sentiment Scanner. Same process. I enabled DuckDuckGo search and HackerNews tools, set the model to Claude Sonnet (strong at synthesizing qualitative information), and wrote instructions focusing on recent news, regulatory risks, management changes, and community sentiment. The key instruction: "Distinguish between verified news and speculation. Label your confidence level for each finding."
Step 4: Configure the Brief Writer. This agent gets no tools. It only receives the outputs from the other two agents. I used GPT-4o again and wrote instructions that specify the output format: executive summary, financial snapshot, sentiment overview, risk factors, and a "further investigation recommended" flag. The critical instruction here: "Never recommend buying or selling. Your job is to organize information, not make investment decisions."
Step 5: Set the team to coordinator mode. Click the team node, select "coordinator" from the dropdown. The coordinator automatically manages the workflow: it sends the initial query to the right agents, collects their outputs, and routes the combined context to the Brief Writer.
Step 6: Test in the chat panel. Type "Screen AAPL for Q1 2026" and watch the agents work. The chat panel shows each agent's reasoning steps and tool calls in real time. You can see exactly what data YFinance returned, what searches the Sentiment Scanner ran, and how the Brief Writer structured the final output.
Step 7: Export as Python. Click the export button. You get a standalone Python file with all three agents, the team configuration, tool imports, and streaming setup. Your engineering team can deploy this as an API endpoint, a CLI tool, or integrate it into an existing research platform.
What the research says about AI in finance
I want to be careful here because the hype around AI in finance is intense, and not all of it is warranted.
A Stanford study that backtested an AI analyst across 30 years of market data found that it outperformed 93% of mutual fund managers (Stanford Report, June 2025). That sounds incredible, but context matters: the AI was given 170 variables from public data sources and optimized purely for stock selection. Real investment management involves portfolio construction, risk management, liquidity constraints, and client relationships. Stock picking is only one piece.
A Harvard study published in early 2026 found that AI can predict 71% of mutual fund managers' trade directions (National Bureau of Economic Research, 2026). That's interesting for a different reason: it suggests that a significant portion of active management decisions are pattern-based and therefore automatable.
Gartner's 2026 Finance AI Outlook projects that by 2027, 40% of finance departments will deploy autonomous agents that execute judgment-based decisions under human oversight.
These numbers tell a consistent story. AI is very good at the data gathering and pattern recognition parts of investment research. It's less proven at the judgment, conviction, and risk management parts.
That's exactly why I designed the three-agent system the way I did. The agents gather and organize. The humans decide.
The Polystrat example (and its limits)
If you want to see what fully autonomous AI trading looks like, Polystrat is worth studying. Launched in February 2026 on Polymarket, Polystrat is an autonomous AI agent that trades prediction markets 24/7. Within its first month, Polystrat agents executed over 4,200 trades, with some individual trades returning over 300% (Olas Network, 2026).
Impressive numbers. But prediction markets are a specific domain with binary outcomes, clear resolution criteria, and relatively simple risk profiles. Public equity investing is a different animal. The number of variables, the uncertainty in outcomes, the impact of macro factors, regulatory changes, geopolitical events; these make fully autonomous equity trading a much harder problem.
I mention Polystrat not as a model to copy, but as a reference point. It shows what AI agents can do when the problem is well-defined. Investment research screening is well-defined enough for agents. Investment decision-making is not. At least not yet.
Being honest about the limitations
I would not be doing you any favors if I pretended this system is a replacement for experienced analysts. It isn't. Here's what it does poorly.
Qualitative judgment. An agent can tell you that a CEO made unusual comments about "strategic alternatives" in an earnings call. It cannot tell you whether that means the company is about to be acquired or the CEO is about to be fired. That requires industry context, relationship knowledge, and pattern recognition that comes from years of experience.
Novel situations. AI agents are trained on historical data. When something genuinely new happens (a pandemic, a sudden regulatory shift, a breakthrough technology), the agents' prior training may be misleading rather than helpful. The 2020 market crash and recovery would have confounded most AI systems trained on pre-pandemic data.
Interconnected risks. The Sentiment Scanner might flag negative news about a supplier, but it won't automatically connect that to the impact on your target company's supply chain unless you've specifically instructed it to check for those relationships. You need to be thoughtful about your agent instructions.
Compliance and audit trails. If you work in a regulated environment, you need to document how investment decisions were made. "An AI agent told me" is not sufficient documentation. Any agent-assisted research process needs human review and sign-off.
Data freshness. YFinance data has a slight delay. For high-frequency decisions, that delay matters. For weekly screening of fundamentals, it's fine.
These aren't reasons to avoid using agents for research. They're reasons to design your system with clear boundaries between what agents do (gather, organize, summarize) and what humans do (judge, decide, commit capital).
What my friend's fund actually did
After I showed him the three-agent prototype, my friend's team spent a week testing it against their manual process. They ran the same 20 companies through both approaches and compared the outputs.
The agents caught two pieces of news that the analysts had missed during their manual scan. One was a regulatory filing buried in an SEC comment letter. The other was a HackerNews discussion about quality issues with a hardware product that hadn't made it into mainstream financial news yet.
The analysts, on the other hand, correctly identified a management credibility issue that the agents missed entirely. The CEO had a history of overpromising on timelines, and the analysts recognized the pattern from previous quarters. The agents flagged the statements as "optimistic" but didn't have the longitudinal context to call it a credibility issue.
That's the right division of labor. Agents have broader coverage and faster processing. Humans have deeper context and better judgment.
His team now uses the agent system for initial screening and spends their freed-up time on deeper analysis of the companies that pass the screen. They went from screening 20 companies a week to 50, without adding headcount. The quality of their final analysis improved because they had more time to think.
Getting started
If your analysts spend more time gathering data than analyzing it, that's a three-agent problem. You can build the system I described in about 30 minutes with Agno Builder's Investment Research Team template. No Python required for the prototype. Export clean code when you're ready to integrate it into your workflow.
Start with a small basket of companies you already know well. Run the agents and compare their output against your existing research. See where they add value and where they fall short. Then adjust the instructions, add or remove tools, and iterate.
The goal isn't to automate investment decisions. The goal is to automate the tedious parts of investment research so your best people can spend their time on the work that actually requires expertise.
What would your research process look like if data gathering took 10 minutes instead of 10 hours?