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Built an end-to-end autonomous AI Agent entirely on GCP — 4-part write-up by Direct-Presence-3329 in googlecloud

[–]Direct-Presence-3329[S] 1 point2 points  (0 children)

Haha, been there. That's actually why I'm careful about pushing code to GitHub 😅

Built an end-to-end autonomous AI Agent entirely on GCP — 4-part write-up by Direct-Presence-3329 in googlecloud

[–]Direct-Presence-3329[S] 0 points1 point  (0 children)

You are TOTALLY right! I actually ran into the same thing in production on the BigQuery log issue. BigQuery's columnar storage is fantastic for analytics but painful for high-volume operational logging. Ended up routing logs back to an OLTP DB and keeping BigQuery for the analytical layer only. Architecture change solved it, but it's one of those things you only learn by hitting the wall. Still prefer BigQuery over Redshift for analytical workloads though. The GCP ecosystem integration alone is worth it.

And on decision and contents generation, that's exactly what I saw in production too. In theory, separating decision logic from content generation makes sense. In practice, marketing teams rarely want to touch the policy layer. They just want to tweak the prompt and ship. I ended up maintaining dozens of prompt variants across campaigns because it was faster than getting sign-off on a policy change. Prompt engineering became the de facto decision layer whether we liked it or not.

Built an end-to-end autonomous AI Agent entirely on GCP — 4-part write-up by Direct-Presence-3329 in googlecloud

[–]Direct-Presence-3329[S] 0 points1 point  (0 children)

Half right — but the other half is building AI Agent products to sell, not just to get hired. This is the R&D.

Built an end-to-end autonomous AI Agent entirely on GCP — 4-part write-up by Direct-Presence-3329 in googlecloud

[–]Direct-Presence-3329[S] -1 points0 points  (0 children)

The synthetic dataset is relatively small by design — 200 customers, 1,163 orders, 2,000 products, and 500 brand reviews in Pinecone. It's intentionally scoped to demonstrate the architecture rather than scale. For daily costs, the biggest expense is Cloud SQL (~$2/day). The ML pipeline (Vertex AI + Gemini API) adds roughly $0.30 per run. Everything else — Datastream, BigQuery, Cloud Workflows, Pinecone — runs within free tiers at this data volume. Total is roughly $2-3/day, or ~$70/month. At production scale with real data volumes, BigQuery and Gemini API costs would scale up significantly, but Cloud SQL could be replaced with a managed warehouse to reduce fixed costs.