Three Steps to Build Production-Ready AI Agents for Financial Services

As a Forward Deployed Engineer, I keep hearing some version of the same question: "This works in a demo, but what does it take to run it for real?"

Recently, my Tavily colleagues and I hosted the webinar Tavily for Financial Services: Building Production-Ready AI Agents with Real-Time Web Search to show financial services teams how to put AI agents to work in production.

Here is a breakdown of the three most important steps for enabling your agents to deliver current, traceable answers reliably and cost-effectively within the workflows your teams already use. These reflect patterns we’ve seen while building production-ready agents with customers across KYC, investment research, risk, and underwriting.

Step 1: Start with the decision and work backward

Before you start prompting, answer these questions: What decision does the reviewer have to make? And what evidence would they need to make it?

It's straightforward for KYC. The reviewer needs to know whether there's adverse media, sanctions exposure, or ownership mismatch. So the agent's job has evolved from researching a company broadly to running focused searches for those specific issues, attaching a source to every finding, and flagging when the evidence conflicts or isn't strong enough to support a conclusion. Anything in that last bucket goes to a human.

The live demo in our session showed this pattern in action. The agent researched multiple companies in parallel, generated focused queries, reviewed complete articles, and returned the passages, dates, and sources reviewers needed to assess the evidence and make the final decision.

Step 2: Evaluate retrieval separately from generation

You don't want to change models or rewrite prompts at the first instance of your model giving bad responses. Check the right place by investigating what information was retrieved before a report was generated. Stale pages with nothing but cookie banners, irrelevant content cluttered with ads, and missing source links can lead to poor outputs, higher token costs, and conclusions your human reviewers can't verify.

To ensure your agent is working with information-dense context, you should evaluate retrieval separately from generation. That way you can see:

• Which queries it ran
• What information it received and how much of it was relevant
• When it was retrieved
• Which sources support its conclusions

Most of this you can control at the retrieval layer rather than hoping the model sorts it out. Restrict by date range so you're not surfacing a press release from 2009. Restrict by domain to prioritize trusted sources, and by topic to improve relevance. Get full-page content instead of clickbait headlines or misleading snippets. That level of visibility helps you diagnose failures faster and gives reviewers the evidence they need to use a result confidently.

Step 3: Reuse one foundation across workflows

Once you have done steps 1 and 2 successfully, you have learned the pattern and are ready for your own workflow that can be reused across KYC, investment research, risk, and underwriting. Each workflow follows a similar architecture: define the question, search for evidence, filter and structure the results, and give your reviewer what they need to act.

You don't have to rebuild how an agent gets to the web and evaluates what it finds. You may still need to define the output format, but you will primarily be writing new questions on a foundation that already works.

In the webinar, we discussed how this method was used by one of our global payments customers to reduce KYC investigations from hours to minutes, and how a Fortune 500 financial services customer reduced market research across hundreds of thousands of companies from about seven days to 15 minutes. For underwriting, we covered how agents replaced a customer's research spread across 10–30 browser tabs with a consolidated, source-backed report.

Build your financial services agents

Start with a clear decision, evaluate the retrieval behind every answer, and reuse that foundation as you expand. Together, these steps can help you reduce research time and costs while giving reviewers evidence they can verify.

The full session is now available on demand. Christie Green (VP of Customer Success) walks through a customer case study, Yuval Perry (Director of Commercial Sales) covers how teams are rolling this out, and I (Harsh Kataria, FDE) conduct the KYC demo end to end.

Watch the replay →