Agent self-tracking on a platform you don't control

A reference integration for Google’s CX Agent Studio demonstrates agent self-tracking for a fictional luxury-fashion concierge by having the agent call OpenAPI-based HTTP tools that record shopper intent, decision rationale, and unmet catalog constraints as Snowplow events in BigQuery. Because the agent runtime and tools are managed externally, tracking behavior is governed by detailed prompt instructions, while endpoints validate event vocabularies, attach shopper and conversation identities, and return only an acknowledgement so analytics never affects the customer response. Session, user, and per-turn identifiers are injected into tool-call headers through session variables, allowing client-side browsing activity, server-side tool execution, and agent-side reasoning events to be joined across a conversation and traced from recommendations to clicks, carts, and purchases. The implementation highlights operational issues such as parallel tool calls suppressing text responses, immutable published agent versions requiring code-like testing, and widget timing behavior affecting turn IDs. Included BigQuery queries examine context-lookup health, intent confidence, unmet shopper needs, recommendation funnels, and model drift, helping teams refine prompts, tools, catalog coverage, and agent configurations. Although built for Google’s platform, the approach is intended to work with any agent platform supporting OpenAPI tools and session-scoped request variables, while the published demo remains unsuitable for production without stronger security and hardening.

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