Build methodology for agentic AI systems in regulated verticals — Audit → Gauge → Engineer → Navigate → Track.
The AGENT framework adapted from the Harvard Agentic AI summer program, applied to building agentic systems for regulated verticals. Each step has both an analytical component (what to do) and a compliance component (how to do it safely).
Scan the existing workflow for choke points. Map where humans are doing routine work AI is excellent at vs work humans should keep doing. Document the regulatory environment + compliance constraints + audit requirements that apply.
Score the candidate interventions by impact + feasibility + risk. Critical-path choke points with low compliance risk get prioritized; nice-to-have automation in high-compliance-risk areas defers.
Build the agentic intervention. Python-native architecture; shared body-of-knowledge; structured handoffs; compliance disclosure baked in at the data layer. Pilot scope first (single market or asset class or workflow slice).
Route the agentic outputs to the right human at the right decision point. Severity-ranked: critical findings auto-applied with notification; high findings auto-applied with notification; medium findings flagged for human approval; low findings logged silent. First 3 production deploys: ALL findings flag-only regardless of severity (build operator trust + tune false-positive rate before granting autonomy).
Maintain an audit log per deployment + per engagement + measure regression rate over time. Quarterly review at minimum; usually more frequent in production.
Why this methodology beats common alternatives:
Compliance discipline is built into the build methodology at the architecture level. Every agentic action that touches a regulated decision is logged with the human decision-maker, the AI's role, and the disclosure language used. Series 82 is targeted approximately 2027 and is NOT currently held — agentic AI consulting engagements are analytical services, not investment-advisory or broker-dealer services.
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