Regulatory Radar — Daksh Randeria

Background

Compliance teams at wealth management firms face a constant stream of regulatory output from the SEC, DOL, and CFPB. Most of it doesn't matter. Some of it does. A small portion represents coordinated enforcement pushes across agencies which can take weeks to surface.

The problem is triage: knowing which regulations need detailed attention, and getting ahead of broader enforcement patterns before they become headlines.

How the agent works

Regulatory Radar is built as a four-node state machine using LangGraph. Each node handles a distinct reasoning step, and the agent carries a structured "state" — a running digital clipboard — as it moves through the graph.

Fetcher — pulls the regulation title, summary, and associated agencies from Supabase, where regulations are ingested daily via the Federal Register API.
Triage — an LLM (Llama-3.3-70b via Groq) scores the regulation on relevance to wealth management (0–10), assigns an impact level, extracts primary keywords, and runs regime shift detection.
Investigator (conditional) — if a regime shift is detected, the agent autonomously triggers a targeted Tavily web search for law firm client alerts and recent enforcement actions, adding context a keyword search can't provide.
Writer — synthesizes everything into a structured executive brief with a Red/Amber/Green risk rating, a two-sentence summary, and three to four priority action items written for a compliance officer audience.

The conditional branch is the important design choice: the agent doesn't deep-dive everything. It reserves that compute, and the associated cost, for situations where it genuinely matters.

The "Regime Shift" signal

A regime shift is detected when any of three conditions are met: three or more agencies act in coordination on a single rule, a single agency exceeds a velocity threshold (five or more regulations in a seven-day rolling window), or a regulation scores 8 or higher on relevance. The velocity check is a PostgreSQL query against the Supabase database, not an LLM call, which keeps it fast and consistent.

No single signal works well alone — the multi-agency check catches coordinated pushes, the velocity count catches volume, and the LLM catches relevance. You need all three to avoid drowning in false positives.

Product thinking

The Streamlit interface is built around a single interaction: select a regulation, click Analyze, watch the agent reason. As it runs, the app surfaces each internal log step in real time — "Calculating SEC velocity...", "Joint action detected: 3 agencies...", "Fetching enforcement context..." — so a compliance officer or a recruiter can see exactly how the decision was made.

A black-box output from a compliance tool is useless; the reasoning trace is what builds trust. The design also includes a "Demo Mode" that feeds a simulated high-velocity dataset, forcing the investigator branch to fire so the full logic tree is visible even when real regime shifts are rare.