In this episode of Zero to Agent in 30 Minutes, Jayeeta Putatunda, forward deployed AI engineering lead at Turing, builds a multi-agent workflow that turns a daily flood of financial headlines into a structured analyst briefing. Financial analysts already have deep internal research, coverage assignments, and market views, but keeping that context current as new information arrives every day and surfacing which of it actually deserves an analyst’s attention is a harder problem.
How to build a source-backed briefing agent, step by step
- Define the scope. Start by setting the analyst’s focus area, research questions, and time window, whether that’s the last one day, seven days, or 30 days. Narrowing the scope up front, including a list of preferred sources, keeps the agent’s web searches directional instead of generic.
- Plan the coverage. A coverage planner agent breaks the research question into discrete sections, such as market backdrop or company catalysts, so the search that follows can run in parallel rather than one long sequential query.
- Search and gather. A news researcher agent runs multiple queries against the preferred sources first, then falls back to a general search if the preferred sources don’t return enough results.
- Validate the sources. A validation agent checks each link for a working, clickable URL and a correct publication date, removes duplicate stories covering the same news, and filters out paywalled pages that won’t return usable content.
- Generate the briefing. A briefing writer agent assembles the validated developments into a set structure, including an executive summary, key bullet points, and a section-by-section breakdown of what each development means for the analyst and what to watch next.
- Capture feedback into memory. A feedback agent logs corrections such as formatting or terminology preferences and saves them to a memory database, so the next briefing run applies those preferences automatically instead of requiring the analyst to re-prompt.
Jayeeta built the entire stack on open source models so newcomers can run it without an API key, and she recommends starting with a smaller model before scaling up. The takeaway extends well beyond finance. Building single-purpose agents rather than one large agent that handles every task means a failure at one stage doesn’t force a restart of the whole pipeline, and each agent’s output stays easier to trace and debug.
The full code base, including the sample data and the UI shown in the demo, is available in Jayeeta’s GitHub repo, so readers can clone it and run the briefing agent on their own systems.
Coming this week
This week, Maxim Salnikov joins Zero to Agent in 30 Minutes to build a supply chain for agent context. He’ll show how to source approved packages from a trusted registry, pin and hash-verify them on any harness, and enforce org policy with a CI gate that can’t be bypassed.