A city SOC wants an agent to propose next investigation queries from the current Sentinel incident graph. Which capability is that using?

A city SOC that wants an agent to propose next investigation queries from the current Sentinel incident graph is using agentic AI for investigation assistance that proposes next queries from the municipal incident graph. The agent acts as a research aid over entities, alerts, and relationships already in the incident—suggesting pivots an analyst might try next—without proposing next queries from the municipal log analytics archive outside the current case context. That keeps autonomy in the suggest-and-assist tier appropriate for civic investigations where human judgment still owns conclusions.

Why the other options fail by concept: Using agentic AI for investigation assistance that proposes next queries from the municipal threat feed summaries draws on external feed summaries but does not propose next investigation queries from the current Sentinel incident graph. Using agentic AI for investigation assistance that proposes next queries from the municipal log analytics archive queries the broad archive rather than proposing next investigation queries from the entities and relationships in the current case context. Using agentic AI for investigation assistance that proposes next hunts from the municipal incident graph timeline proposes hunts rather than next investigation queries from the current Sentinel incident graph.

Exam caveat: proposed queries can be wrong or overly broad; analysts must still validate cost, privacy, and relevance before execution in production workspaces. Operational check: enable agentic propose-next-query on a pilot municipal queue, require analyst acknowledgment before running suggestions, and measure whether mean time to understand improves without an increase in unnecessary heavy queries.