Self-Assembling Landing Page

Overview

Self-Assembling Landing Page is agent 12 of an 18-agent concept series. The brief describes an agent that, on each visitor pageview, reads the search query, ad parameters (UTM, GCLID), geo, and any known visitor profile, then assembles a landing page at the edge from a library of pre-approved content blocks (headline, hero, product set, proof) chosen to match that visitor's likely intent. It also watches in-session signals like scroll depth, dwell time, and exit intent, and can swap the offer or proof block if it detects hesitation.

Problem

The brief frames the problem as: paid traffic lands on one static page regardless of why the visitor clicked, so ad-to-page message match is poor, Quality Score suffers, and most paid spend bounces. Building a separate landing page per campaign by hand does not scale, so the static, generic page becomes the default and leaves conversion on the table.

Approach

The design follows a perceive, decide, act, learn architecture. At the edge, an edge function resolves context (query, UTM, GCLID, geo, returning-visitor profile), a model maps that context to a predicted segment and intent, and the agent assembles the page by selecting blocks and a product set for that segment, then server-side renders it fast enough to serve paid traffic without hurting load time. During the session it watches for hesitation signals and can swap the offer or proof block. Afterward, an experiment engine attributes conversion by segment x layout and feeds that back to improve the assembly policy. The brief specifies Claude as the decision model, pgvector for memory, and a guardrail that blocks and offers must come only from an approved library, so the agent never generates unvetted claims or prices on the fly. It also proposes a three-stage autonomy ramp (shadow, assisted, autonomous) and a phased build plan starting with a rules-based MVP that maps a few campaign segments to pre-built block combinations by UTM or query, before adding prediction and in-session adaptation.

AI work

• Defined the perceive, decide, act, learn loop and mapped each stage to concrete system components (edge function, model plus feature store, block library plus catalog, SSR/edge render, experiment engine)
• Specified Claude as the decision model and pgvector as the memory layer for segment and visitor context
• Wrote the guardrail policy restricting all output to a pre-approved block and offer library, explicitly ruling out on-the-fly generated claims or prices
• Designed the in-session adaptation logic (hesitation-signal detection triggering offer or proof swaps)
• Authored the starter agent prompt (JSON-in, JSON-out contract for block, product set, and headline selection) meant to seed implementation
• Laid out failure modes and mitigations (unvetted claims and prices, slow render hurting ad performance, thin or cloaked-looking pages) and a three-stage autonomy ramp

Status

This is a concept and design brief only: a build playbook with an architecture model, data flow, autonomy ramp, and starter prompt, not a built or deployed application.