Agent-Generated Mind Map — Incident Root Cause
An AGMM example: an SRE agent outputs its API latency root-cause reasoning chain as an interactive mind map, reversing the Human-to-Machine explainability flow.
A problem mind map for an AI credit decision, mapping decisions, data, policies, actors, risks, constraints and open questions before any component is designed.
Copy and paste the following FlowZap code into a project in your FlowZap account to see this template diagram.
credit_decision { # Credit decision
n1: circle label:"Decide whether to grant credit"
n2: rectangle label:"Decisions"
n3: rectangle label:"Data"
n4: rectangle label:"Policies"
n5: rectangle label:"Actors"
n6: rectangle label:"Risks"
n7: rectangle label:"Constraints"
n8: rectangle label:"Open questions"
n9: rectangle label:"Approve / decline"
n10: rectangle label:"Amount & rate"
n11: rectangle label:"Human review"
n12: rectangle label:"Client history"
n13: rectangle label:"Income & contracts"
n14: rectangle label:"External score"
n15: rectangle label:"Thresholds"
n16: rectangle label:"Exceptions"
n17: rectangle label:"Regulations"
n18: rectangle label:"Who has authority?"
n19: rectangle label:"Customer"
n20: rectangle label:"Credit officer"
n21: rectangle label:"Supervisor"
n22: rectangle label:"Compliance"
n23: rectangle label:"Hallucination"
n24: rectangle label:"Bias"
n25: rectangle label:"Privacy"
n26: rectangle label:"Liability"
n27: rectangle label:"Audit trail"
n28: rectangle label:"Explainability"
n29: rectangle label:"GDPR"
n30: rectangle label:"Where do thresholds live?"
n31: rectangle label:"Dispute: who answers?"
n32: rectangle label:"Review margin?"
n1.handle(right) -> n2.handle(left)
n1.handle(bottom) -> n3.handle(top)
n1.handle(top) -> n4.handle(bottom)
n1.handle(left) -> n5.handle(right)
n1.handle(right) -> n6.handle(top)
n1.handle(bottom) -> n7.handle(left)
n1.handle(top) -> n8.handle(right)
n2.handle(right) -> n9.handle(left)
n2.handle(bottom) -> n10.handle(top)
n2.handle(top) -> n11.handle(bottom)
n3.handle(right) -> n12.handle(left)
n3.handle(bottom) -> n13.handle(top)
n3.handle(top) -> n14.handle(bottom)
n4.handle(right) -> n15.handle(left)
n4.handle(bottom) -> n16.handle(top)
n4.handle(top) -> n17.handle(bottom)
n4.handle(left) -> n18.handle(right)
n5.handle(right) -> n19.handle(left)
n5.handle(bottom) -> n20.handle(top)
n5.handle(top) -> n21.handle(bottom)
n5.handle(left) -> n22.handle(right)
n6.handle(right) -> n23.handle(left)
n6.handle(bottom) -> n24.handle(top)
n6.handle(top) -> n25.handle(bottom)
n6.handle(left) -> n26.handle(right)
n7.handle(right) -> n27.handle(left)
n7.handle(bottom) -> n28.handle(top)
n7.handle(top) -> n29.handle(bottom)
n8.handle(right) -> n30.handle(left)
n8.handle(bottom) -> n31.handle(top)
n8.handle(top) -> n32.handle(bottom)
}
Install the MCP packagenpm install -g flowzap-mcpMCP documentation
An AGMM example: an SRE agent outputs its API latency root-cause reasoning chain as an interactive mind map, reversing the Human-to-Machine explainability flow.