"Instead of treating each prompt as a one-off request, the new agent remembers what was asked earlier, including datasets, filters, time ranges, and assumptions, and uses that context when answering follow-up questions. This lets users refine an analysis progressively rather than starting from scratch each time," Satapathy added. Satapathy pointed out that this eases the pressure on developers to prebuild dashboards or predefined business logic for every possible question that a data analyst or business user could ask."
"Satapathy pointed out that this eases the pressure on developers to prebuild dashboards or predefined business logic for every possible question that a data analyst or business user could ask. "Rather than encoding every scenario upfront, teams can let the agent interpret user intent dynamically, while still enforcing access controls, metric definitions, and governance rules already defined in BigQuery," he said."
The agent retains conversational context across queries, remembering datasets, filters, time ranges, and assumptions to answer follow-up questions using prior choices. Users can refine and iterate analyses progressively without restarting each query. Developers no longer need to prebuild dashboards or encode predefined business logic for every possible analytical question. Teams can rely on the agent to interpret user intent dynamically while preserving existing access controls, metric definitions, and governance rules defined in BigQuery. Governance and security controls remain enforced during dynamic interpretation. The approach reduces developer workload and speeds exploratory and business-user self-service analytics.
Read at InfoWorld
Unable to calculate read time
Collection
[
|
...
]