Workflow Examples

This guide outlines example patterns for combining orchestration primitives into complete workflows.

Collections Outreach

A weekly collections workflow that asks staff for a list of accounts, calls each one, and routes outcomes automatically.

Flow

1. Recurring Schedule — Every Sunday at 9 AM
2. Email Collector — Sends an email to the collections manager: "Please reply with the accounts to call tomorrow." Parse mode is set to AI with instructions to extract account objects (name, phone, email, balance, notes). Timeout: 20 hours. Store key: accounts_queue.
3. Store Read — Loads the accounts_queue from the store
4. Task Agent — Picks the next account from the queue and prepares outreach data
5. Voice Agent — Places a collection call using the account's phone number
6. AI Router — Classifies the call outcome into one of five branches:

Outcome	Branch Actions
Payment Collected	Task agent drafts confirmation → Send confirmation email → Store write: remove account from queue
Not Reached	Store write: increment attempt count, keep in queue for next cycle
Disputed	Task agent drafts verification letter → Send verification email → Store write: move to disputed_accounts
Promise to Pay	Task agent generates follow-up reminder → Store write: flag account with promise date
Refused	Email collector sends formal notice and waits for written response → Store write: move to escalated_accounts

Timeline

Sun 9am → Email collector asks staff for the list
Sun/Mon  → Staff replies with accounts (CSV or text)
Mon      → AI parses reply → Store write → Voice calls begin
Mon      → Each call classified → Store updated → Ready for next cycle

The loop closes automatically: accounts that are not resolved stay in the queue for the next weekly run.

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Weekly Client Check-In

A recurring workflow that checks in with a list of clients and escalates non-responders.

Flow

1. Recurring Schedule — Every Monday at 10 AM
2. Store Read — Load client_list and no_response_count
3. Task Agent — For each client, generate a personalized check-in message based on their history
4. Send Email — Send the check-in email
5. Wait — 48 hours
6. AI Router — Classify based on email engagement and reply content:

Outcome	Branch Actions
Replied - Happy	Store write: reset their no-response count to 0
Replied - Needs Attention	Task agent drafts action items → Send internal notification email
No Reply	Store write: increment no_response_count for this client
No Reply - Escalate	(When count exceeds 3) Task agent drafts escalation summary → Voice agent calls account manager

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Monthly Report Collection

Gather data from multiple departments through email, compile it, and distribute a summary.

Flow

1. Recurring Schedule — 1st of every month at 8 AM
2. Store Clear — Reset from last month's data
3. Email Collector — Send to sales@company.com: "Please reply with this month's sales figures." Parse mode: AI. Store key: sales_data. Timeout: 48 hours.
4. Email Collector — Send to support@company.com: "Please reply with this month's support metrics." Parse mode: AI. Store key: support_data. Timeout: 48 hours.
5. Store Read — Load all collected data
6. Task Agent — Compile sales_data and support_data into a formatted monthly summary
7. Send Email — Distribute the summary to the leadership team

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Lead Qualification Loop

Score and re-engage leads over multiple weeks until they convert or are archived.

Flow

1. Recurring Schedule — Every Wednesday at 9 AM
2. Store Read — Load active_leads list
3. Task Agent — Score each lead based on engagement history and CRM data
4. AI Router — Route based on lead score and activity:

Outcome	Branch Actions
Hot Lead	Voice agent places an immediate call → Task agent logs outcome → Store write: update lead status
Warm Lead	Send personalized email → Store write: keep in queue, increment touch count
Cold Lead	Send re-engagement email → AI router checks if touch count > 5: if yes, archive; if no, keep in queue
Converted	Task agent updates CRM → Store write: remove from active_leads

Each week, converted and archived leads are removed from the queue while warm and cold leads cycle back for another touch.

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Tips

• Seed the store — On the first run, the store is empty. Use an early task agent step to populate initial data, or use a separate one-time orchestration with a form trigger to upload a CSV.
• Use placeholders in task agent prompts — After a Store Read, reference stored keys with {{ key_name }}. For example, {{ accounts_queue }} injects the stored list directly into the prompt. Objects and arrays are formatted as JSON automatically.
• Let the task agent update the store directly — Task agents running inside an orchestration automatically receive the update_orchestration_store tool. This can reduce the need for a separate Store Write step after every agent.
• Use store-write in branch arms — Each AI router branch should update the store to reflect what happened so the next cycle has the right context. In branches that include a task agent, the agent can update the store directly via its tool.
• Set reasonable timeouts — Email collectors pause the workflow. Set a timeout, such as 20 hours, so the workflow does not wait indefinitely.
• Test with a short schedule — While building, start with a daily recurring schedule. Switch to weekly or monthly once the workflow is behaving correctly.
• Store read before AI router — If the AI router needs historical context in addition to the current step output, add a Store Read before it.
• Chain task agent outputs — Each task agent response is available to the next step as {{ taskAgentResponse }}.
• Use For Each to iterate over lists — When your store contains an array, use a For Each step to run actions per item. Use sequential mode when calls should finish one at a time, batch mode for higher-volume email or SMS work, or time-delayed mode to control pacing.
• Choose the right iteration mode — Sequential waits for each item to complete, batch queues everything at once, and time-delayed spaces out execution.