Vertical AI Agent
A ReAct loop that quotes, samples, and hands off to a human.
The agent runs a reasoning loop: it thinks, picks a grounded tool (catalog lookup, quote, sample request), and acts, repeating until it can help. When a visitor is ready, it proposes a quote or sample and hands off to the owner over Slack and email rather than transacting on its own.
Data flow
How the pieces connect and where data moves.
Components: Visitor (states a need), Agent loop (ReAct reasoning), Gemini (plans next step), Tools (quote, sample, catalog), Catalog + docs (grounded facts), Owner handoff (Slack + Resend). Connections: Visitor to Agent loop; Agent loop to Gemini for reason; Gemini to Agent loop for next action; Agent loop to Tools for call tool; Tools to Catalog + docs for look up; Catalog + docs to Tools for facts; Tools to Agent loop for result; Agent loop to Owner handoff for quote / sample; Agent loop to Visitor for reply.
Behavior
How it behaves step by step over time.
Operating guardrails
Constraints enforced in code, described at category level.
- SDS, hazard, and handling answers come only from tool-returned documents, never model-generated.
- Prices, products, and quotes are grounded in retrieved catalog data, never invented by the model.
- Human-in-the-loop: the agent proposes quotes and samples and hands off to the owner; it never autonomously transacts or commits pricing.
- Tenant isolation: every query is company-scoped, with no cross-tenant data.
- Data privacy: visitor memory is self-deletable, messages are retained at most one year, and summarized memory is injection-defended.
- Cost governance and anti-abuse: per-tenant token metering, caps, and rate limiting on the agent surface.
- Transparent qualification tracks known and unknown facts; models are Google Gemini tiers.