Deep Agentics

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Architecture

A typical product build is a thin, explicit set of layers - not a mystery monolith.

Typical application architectureClients connect through a web or messaging channel to an application layer, which uses AI agents and talks to Supabase for data, with Stripe and Twilio as integrations.ClientsWeb / WhatsAppApp layerUI · API · authLovable / customAI agentsAnthropic / toolsSupabasePostgres · RLS · storageTwilioWhatsApp / SMSStripePayments
Typical Deep Agentics build path

Layers

  1. Clients - web UI and/or messaging channels (e.g. WhatsApp).
  2. App layer - UI, API routes, auth session handling; often Lovable-assisted for product apps.
  3. AI agents - tool-using models (e.g. Anthropic) with clear boundaries and human-in-the-loop where needed.
  4. Data - Supabase Postgres with Row Level Security as the default tenant boundary.
  5. Integrations - Twilio, Stripe, email, and others at the edges.

What changes per project

Messaging-heavy products (WhatsApp ordering pipelines) push Twilio and agents forward. Commerce sites emphasise storefront and email. Analytics products may lean on Fabric/Databricks rather than Supabase alone. The diagram is the default shape - not a cage.

Static marketing sites

This website is intentionally simpler: static Astro output, no app database, contact via Web3Forms. Tier the architecture to the problem.