Examples & use cases

These examples show how teams use Cascades to coordinate workflows, enforce execution controls, and generate verifiable execution records across operational environments.

Patterns below map cleanly to workflow templates and integrations. Providers, thresholds, approvals, and policies can be adapted to fit individual teams and environments.

Incident response automation

Trigger: Security alert from PagerDuty, SIEM alert, or ITSM webhook.

Workflow:

1. Alert ingestion
2. Threat enrichment
3. Ownership routing
4. Communication approvals
5. Remediation tasks
6. SOC evidence export

Examples:

• enrich alerts with threat intelligence
• route incidents to the correct team
• trigger containment workflows
• notify stakeholders in Slack
• export execution records for incident review

Operational advantage: deterministic task ordering, escalation paths, and auditable execution records for post-incident analysis. See execution proofs and architecture for how ordering and proofs connect.

Compliance workflows

Trigger: scheduled audit checks, infrastructure drift scans, or source control changes.

Workflow:

1. Configuration attestation
2. Policy validation
3. Drift verification
4. Audit export
5. Transparency logging

Examples:

• validate infrastructure definitions
• compare current workflows against immutable snapshots
• detect configuration drift
• export verification artifacts
• reference transparency logs when Sigstore Rekor is enabled

Verification model: snapshot hashing at execution time ensures teams can compare historical definitions against current configurations. Deep dive: immutable snapshots, drift detection.

Customer onboarding workflows

Trigger: CRM closed-won event or onboarding request.

Workflow:

1. Provision tenant
2. Assign integrations
3. Configure baseline services
4. Notify internal teams
5. Validate readiness

Examples:

• create tenants
• provision baseline environments
• assign permissions
• trigger onboarding notifications
• confirm operational readiness

Operational advantage: eliminates manual onboarding bottlenecks while maintaining visibility across provisioning workflows. Long-running paths should pin timeouts and retries (retries & timeouts).

AI workflow orchestration

Trigger: product actions, queued inference jobs, or AI-driven workflows.

Workflow:

1. Policy validation
2. Model execution
3. Tool execution
4. Output validation
5. Trace capture

Examples:

• PII validation
• output controls
• model routing
• tool execution
• trace storage and immutable execution records

Operational advantage: enables deterministic execution for AI workflows that typically become difficult to audit. Avoid unbounded fan-out—use explicit DAG edges (DAG execution).

Multi-agent orchestration

Coordinate multiple agents without relying on opaque agent loops or hidden orchestration layers.

Workflow:

1. Supervisor router
2. Retrieval agent
3. Tool execution agent
4. Merge layer
5. Policy validation
6. Final output

Examples:

• retrieval agents
• tool-use agents
• policy enforcement layers
• merge tasks
• verification tasks

Operational advantage: each task remains independently observable, retryable, and verifiable—failures stay isolated instead of collapsing entire workflows.

Revenue operations workflows

Trigger: subscription changes, invoice failures, entitlement updates, or pricing changes.

Workflow:

1. Validate billing inputs
2. Apply pricing rules
3. Update entitlements
4. Notify downstream systems
5. Generate audit trail

Examples:

• pricing updates
• entitlement changes
• billing reconciliation
• finance notifications
• operational record generation

Operational advantage: creates deterministic financial workflows with stronger operational traceability. Stripe-oriented setup is described in environment and Stripe billing internals where applicable.

Building custom workflows

Most teams begin with:

• webhook triggers
• API integrations
• approval gates
• retry policies
• execution proofs
• drift detection

Recommended rollout path:

1. Sandbox workflows
2. Operational testing
3. Proof validation
4. Drift monitoring
5. Production deployment

Putting it together

• Start from architecture for the end-to-end platform map.
• Use workflow lifecycle for enqueue, execution, and completion semantics.
• Map vendors through integrations and webhooks.