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A platform lead, AI architect, or transformation owner asks where to focus next. This is the “spin up multiple agents to scan the entire process tree” use case — agents should fan out in parallel across the hierarchy.

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Example prompts

  • “Find the highest-leverage automation opportunities in our P2P value stream.”
  • “Where in our close cycle should we focus next?”
  • “Scan our org for duplicate invoice processing across geographies.”

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End-to-end recipe

1

Confirm the workspace

Call list_accessible_workspaces and switch if needed.
2

Build the work queue

Call get_process_hierarchy — the whole tree, or filtered to the value stream of interest. The leaves become your work queue.
3

Fan out in parallel across leaves

For each leaf:
The MCP is stateless per call. There’s no penalty for parallel reads — fan out aggressively.
4

Hunt high-leverage patterns in the index

Call search directly on the index with pattern queries: “manual approval”, “duplicate entry”, “data re-keying”, “exception handling”, “swivel-chair workflow”. For each hit, fetch the process to confirm the pattern is real, not just a name match.
5

Check process attributes

Inspect the attributes field already returned by get_process_details — “automation status”, “system of record”, “control level”, or similar attributes that help rank candidates.
6

Confirm each top candidate

For each top candidate:
  • get_process_observations with verbosity="full" — confirm the deviation or manual pattern with concrete evidence.
  • Read the dependencies field already returned by get_process_details for a first cut at blast radius.
7

Synthesize a ranked candidate set

Process IDs, observation counts, dependency depth, similarity to known patterns, evidence trail.

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What to surface in the brief

  • A ranked list of candidate processes
  • For each: observation count, dependency depth, pattern signal
  • Citable process IDs and observation timestamps
  • Gaps where the workspace doesn’t have enough evidence yet