Equipped with specific instructions and this custom toolset, the agent autonomously investigates the alert by actively gathering external context. It can query BigQuery for a user’s transaction history, analyze unstructured data like receipts, or ground its findings with Google Search to verify a merchant’s reputation. Ultimately, it categorizes the transaction as a FALSE_POSITIVE or flags it as ESCALATION_NEEDED.
The Human-in-the-Loop Advantage
This approach is central to the architecture’s scalability. By effectively filtering out the noise, it dramatically reduces operational overhead and ensures that your investigators only spend their time on the most complex cases. And since ADK offers an impressive array of tools and integrations, you can have your agent escalate events to a wide array of enterprise systems for both human-in-the-loop engagement, or even automate pipelines end-to-end using human-on-the-loop observability.
Bringing it All Together: Agent Analytics
Once your pipeline is live, the work shifts from building to monitoring. Unlike traditional software, autonomous agents run persistently in the background. Because they operate behind the scenes, having deep observability into what they are doing, how long they take, and how much they cost is critical.
By initializing the BigQuery Agent Analytics plugin during deployment, the ADK automatically logs all trace data, tool usage, and execution latency directly into BigQuery:
