System Patterns Library

Four reusable operating patterns that show how intelligence should move through your organization.

Pattern

Decision Flow Pattern

Designs how signals become decisions, decisions become ownership, and ownership becomes action.

Select a stage to view the playbook.

Top-down operating flow

Stage 1

InputActive

Signal capture

The organization defines what changes are worth noticing before teams react.

How it works

  • Define the key internal and external signals that trigger decisions.
  • Assign signal owners to keep data quality and timeliness stable.
  • Set review cadence so weak signals are not ignored.

Failure pattern

  • Teams monitor different indicators and reach conflicting conclusions.
  • Critical changes are discovered too late to respond effectively.
  • Noise is mistaken for urgency, creating decision churn.

Governance lens

  • Document approved signal sources and refresh frequency.
  • Define escalation thresholds for high-impact signal shifts.
  • Audit signal reliability and ownership coverage monthly.

Observable signals

  • Percent of key signals with clear owners
  • Average time from signal change to review
  • Rate of false-positive escalations

Next move

  • Create a one-page signal catalog for leadership alignment.
  • Retire duplicate dashboards that fragment interpretation.
  • Pilot shared monitoring on one cross-functional priority.

Pattern

Agent Orchestration Pattern

Coordinates human and AI roles so work moves with speed, control, and clarity.

Select a stage to view the playbook.

Top-down operating flow

Stage 1

InputActive

Role boundary design

The organization decides where human judgment leads and where AI executes.

How it works

  • Define role boundaries by risk, complexity, and business impact.
  • Set clear decision rights for human and AI participants.
  • Document when workflows require human approval.

Failure pattern

  • AI acts in areas where policy requires human control.
  • People duplicate AI work because role boundaries are unclear.
  • Escalations happen too late in high-impact decisions.

Governance lens

  • Maintain an approval matrix for human-in-the-loop controls.
  • Track boundary exceptions and root causes.
  • Review role design after major incidents.

Observable signals

  • Boundary exception count
  • Human override frequency
  • Percent of workflows with defined role ownership

Next move

  • Map one critical workflow with explicit human/AI ownership.
  • Add approval gates for high-risk steps.
  • Train teams on escalation triggers.

Pattern

Memory Architecture Pattern

Builds shared memory so teams and systems can act on reliable context, not fragmented history.

Select a stage to view the playbook.

Top-down operating flow

Stage 1

InputActive

Memory source inventory

The organization identifies where critical knowledge lives and who maintains it.

How it works

  • Catalog high-impact knowledge sources across teams.
  • Assign data stewards for each source.
  • Classify sources by reliability and refresh cadence.

Failure pattern

  • Critical knowledge stays trapped in local tools.
  • No one owns data freshness for key sources.
  • Teams use outdated material without knowing it.

Governance lens

  • Define stewardship accountability for each source.
  • Set freshness standards by decision criticality.
  • Track unresolved source quality issues.

Observable signals

  • Percent of critical sources with assigned steward
  • Source freshness compliance rate
  • Number of unresolved quality incidents

Next move

  • Publish a memory source register for priority operations.
  • Assign ownership where gaps exist.
  • Set minimum refresh standards for executive-critical data.

Pattern

Governance Layer Pattern

Embeds accountability, risk control, and auditability directly in operating flow.

Select a stage to view the playbook.

Top-down operating flow

Stage 1

InputActive

Policy translation

Leadership intent is translated into clear operational rules.

How it works

  • Convert policy into decision rules teams can apply daily.
  • Define what is allowed, restricted, and escalated.
  • Map policies to workflows where violations carry high risk.

Failure pattern

  • Policies stay abstract and are inconsistently applied.
  • Teams rely on interpretation instead of explicit rules.
  • Risk controls are strongest on paper, weakest in operations.

Governance lens

  • Maintain policy-to-workflow traceability.
  • Assign policy owners for each high-risk area.
  • Review policy clarity with frontline operators.

Observable signals

  • Percent of policies mapped to workflows
  • Policy interpretation disputes per quarter
  • Time to clarify ambiguous controls

Next move

  • Rewrite one high-risk policy into operational rules.
  • Publish allowed/restricted/escalated examples.
  • Validate clarity with teams who execute the workflow.