Common Workflow Patterns
Common agent-driven patterns for running reliable operations in Supervity
This guide covers common agent-driven patterns you’ll use when running operations in Supervity.
These patterns describe how AI agents typically operate in real systems — not just how workflows are wired.
If you arrived here from Quick Start or Key Features, use this page to map capabilities to practical usage.
Approval-Based Operations
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Use this pattern when AI agents can operate autonomously, but certain actions require human judgment or sign-off.
When to use this
- Actions are high-impact or irreversible
- Compliance or policy enforcement is required
- Humans must remain accountable for decisions
How the pattern works
- An AI agent plans and executes initial steps
- A sensitive action triggers a human review checkpoint
- A reviewer approves, rejects, or modifies the action
- The agent resumes execution based on that decision
The workflow pauses safely until a human responds.
Common use cases
- Expense approvals
- Content publishing
- Purchase orders
- Access provisioning
- Compliance-sensitive actions
This pattern is most commonly owned by Ops and Admin teams.
→ Next:
Human Review & Approvals
Notification & Alerting Operations
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Use this pattern when AI agents need to inform humans or systems about status, outcomes, or anomalies.
When to use this
- Humans need visibility, not control
- Systems must be notified of outcomes
- Exceptions or thresholds matter
How the pattern works
- An agent monitors events or execution results
- Conditions determine when notification is required
- Messages are sent to one or more destinations
Notifications can be conditional, aggregated, or scheduled.
Common use cases
- System alerts
- Status updates
- Daily or weekly summaries
- Failure or anomaly notifications
Notifications can be sent via:
- Slack or Microsoft Teams
- Webhooks or APIs
This pattern is common across business users and ops teams.
Data Processing & Transformation Operations
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Use this pattern when AI agents need to move, clean, transform, or enrich data across systems.
When to use this
- Data exists in multiple systems
- Manual data handling is error-prone
- Outputs must be consistent and repeatable
How the pattern works
- An agent fetches data from one or more sources
- Data is transformed, filtered, or enriched
- Results are written to downstream systems or reports
Execution may be:
- Scheduled
- Event-driven
- API-triggered
Common use cases
- Data migrations
- Report generation
- ETL-style pipelines
- Scheduled exports
This pattern is often combined with:
- Scheduling
- Conditional logic
- Parallel execution
→ Next:
Data Transformation & Logic
Event-Driven Operations
Use this pattern when external events should immediately trigger agent execution.
When to use this
- External systems produce events
- Work must react in near real time
- Human initiation is not required
How the pattern works
- An external system emits an event
- A webhook or integration triggers execution
- The agent evaluates context and acts accordingly
Common use cases
- New lead received
- File uploaded
- Support ticket created
- Form submission
This pattern is frequently used by IT and platform teams.
→ Next:
Scheduling & Automation
Combining Patterns
Real-world operations almost always combine patterns.
Examples:
- An agent processes data → requests approval → sends notifications
- A scheduled agent runs nightly → transforms data → alerts on anomalies
- An API-triggered agent executes → pauses for human review → resumes
Supervity is designed to support these composed patterns without manual orchestration.
Agents manage the flow.
Workflows ensure safe execution.
Humans intervene only where needed.
Where to Go Next
Choose your next step based on your role:
🧑💼 Business Users
- Explore Common Use Cases
- Try a guided Tutorial
⚙️ Ops & Admin Teams
- Configure Human Review & Approvals
- Set up Scheduling & Automation
🧑💻 IT & Developers
- Trigger agents via API & Webhooks
- Explore Integrations Overview
These patterns are starting points.
Supervity agents adapt, combine, and scale them as your operations grow.