GOOGLE CLOUD’S AGENTIC TURN: PIPELINES GIVE WAY TO LONG‑RUNNING DATA AGENTS
Google Cloud is pushing data teams toward agentic architectures where long-running agents operate over structured data, not just LLM calls at the edge. A hands...
Google Cloud is pushing data teams toward agentic architectures where long-running agents operate over structured data, not just LLM calls at the edge.
A hands-on reflection after Google Cloud NEXT ’26 argues the center of gravity is moving from deterministic pipelines to agents that reason over events and run autonomously, citing the Gemini Enterprise Agent Platform, an Agentic Data Cloud, and long-running serverless agents PlanetLedger rethink.
If you go this route, you’ll still need observability and debugging around retrieval and context quality; a visual RAG debugger like Docling Studio can help. For multimodal search signals that agents can reason over, building blocks like Vespa-based video search are relevant video search with Vespa.
Expect production realities—ordering, delivery guarantees, and concurrency—to bite; the real-time chat scaling post is a good reminder of what “autonomous” actually contends with in live systems chat at scale.
This shifts decision-making from fixed workflows into agents that act over your data, changing orchestration, observability, and cost models.
Data governance and SLAs must adapt when agents initiate and coordinate work instead of being the last step in a pipeline.
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terminal
Prototype an event-to-agent path: feed domain events into a supervisor agent, compare outcomes vs. your current deterministic pipeline on latency, accuracy, and ops noise.
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terminal
Run a RAG-with-structured-data experiment: retrieval over tables + vectors, then measure answer quality and drift with a visual debugger on a real dataset.
Legacy codebase integration strategies...
- 01.
Wrap existing pipelines with a supervisor agent that decides when to invoke steps; keep events, idempotency, and compensations unchanged initially.
- 02.
Add tracing and guardrails around retrieval and tool use before expanding agent autonomy; budget and alert on token/runtime as a first-class SLO.
Fresh architecture paradigms...
- 01.
Model events as signals for reasoning, not hardcoded triggers; design for long-running agent state with persistent memory and safe retries.
- 02.
Pick storage that supports hybrid search (structured + vector) and plan for RAG observability from day one.
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