> ## Documentation Index > Fetch the complete documentation index at: https://docs.duvo.ai/llms.txt > Use this file to discover all available pages before exploring further. # Playbooks > Step-by-step guides for building Duvo agents around specific business outcomes, end to end. Playbooks are step-by-step guides for building Duvo agents around specific business outcomes. Each playbook covers a real workflow end to end: why to automate it, what you'll build, how to set it up, and how to handle the edge cases that come up in practice. Use a playbook when you want to automate a recognizable business goal — not a feature, but an outcome: "monitor inventory and alert on low stock", "process orders from a shared inbox", "triage customer complaints and route them to the right team". *** ## How Playbooks Are Structured Every playbook follows the same structure so you can quickly find what you need: | Section | What it covers | | ---------------------- | ---------------------------------------------------------------------------------- | | **Why Automate This?** | The business problem, the proposed solution, and expected results | | **What You'll Build** | A concrete description of the agent — inputs, steps, outputs, connections used | | **Before You Start** | Checklist of prerequisites: accounts, credentials, sample data | | **Steps** | Numbered setup instructions from creating the agent through testing and going live | | **Expected Results** | What success looks like in your inbox, your systems, and in Duvo | | **Troubleshooting** | Solutions to the most common issues | | **Take It Further** | Extensions and enhancements once the core workflow is running | | **Related** | Links to other playbooks, connection docs, and feature guides | *** ## Playbook Categories Playbooks are organized by the type of outcome they deliver. *** ### Inbound Intake Turn incoming emails, documents, and forms into structured records — without manual rekeying. Extract orders from a shared inbox, validate fields, write records, and flag anomalies for review. *** ### Documents Process documents at scale — extract structured data, validate against master data, and route exceptions for human review. Extract fields from invoices, receipts, and IDs; cross-validate against your master data; and route low-confidence or anomalous records for human review before writing to your system. *** ### Reporting Pull data from your systems on a schedule and deliver it where it is needed — Slack summaries, email digests, spreadsheet exports. *Coming soon: Forecasting and Alerting — monitor warehouse data and alert on threshold breaches.* *** ### Operations Handle the exception-heavy workflows that fall through the cracks: stuck transactions, failed items, aging cases, supplier follow-ups, QA spot checks. *Coming soon: Exception Handling, Supplier Follow-up, QA Automation.* *** ### Customer-Facing Route, triage, and respond to inbound customer requests consistently and at scale. Ingest complaints from email, Zendesk, Intercom, and Slack; classify severity and topic; draft a tone-checked response for human approval; route to the right queue; and tag SLA deadlines. *** ## Per-Connection Outcome Examples Short outcome-based examples for the highest-usage connections. For step-by-step tutorials, follow the links to the full workflow guides. *** ### Slack These workflows use Slack as an operational hub — team members trigger agents, receive results, and approve actions without leaving Slack. **On-demand reports from shared channels** A team member mentions the Duvo app with a question ("how many orders are stuck in pending?") in any enabled channel. The agent queries your data source and posts a formatted answer back in the thread within minutes. No dashboards. No analyst required. See [Slack Mention Workflows](/user-guide/examples/slack-mention-workflows). **Request intake and routing** Requests that arrive in #helpdesk, #ops-requests, or similar shared channels are read, classified, and routed to the right person or ticketing system automatically. The requester gets an acknowledgment in the thread; the right team gets a ticket or DM. See [Slack Mention Workflows — Request Intake and Routing](/user-guide/examples/slack-mention-workflows#use-case-2-request-intake-and-routing-in-shared-channels). **Automated status updates on a schedule** Every Monday morning, a structured summary of last week's key metrics — revenue, exceptions, open cases — is posted to your team's Slack channel. Built from live warehouse or CRM data; no manual preparation needed. See [Snowflake Workflows — Scheduled KPI Report](/user-guide/examples/snowflake-workflows#use-case-1-scheduled-kpi-report-from-warehouse-data). **Threshold alerts** When inventory falls below the reorder point, a fraud rate crosses a limit, or an error count spikes, your team gets a Slack notification immediately rather than discovering the problem in a weekly report. See [Snowflake Workflows — Threshold Alert](/user-guide/examples/snowflake-workflows#use-case-2-threshold-alert-when-a-metric-breaches-a-limit). *** ### Gmail These workflows turn your inbox from a queue of interruptions into a structured input channel for your business systems. **Inbox triage** Incoming emails are classified by category (support request, lead inquiry, invoice, newsletter), labeled in Gmail, and routed automatically — forwarded to the right team, logged to a spreadsheet, or saved to Drive. You open your inbox to find it already organized. See [Gmail Workflows — Inbox Triage](/user-guide/examples/gmail-workflows#use-case-1-inbox-triage). **Draft replies to common inquiries** For routine customer questions, the agent drafts a reply using your product documentation and preferred tone, saves it to Drafts, and waits for you to review. Complaints are flagged in Slack. Nothing is sent until you approve. See [Gmail Workflows — Drafting Replies](/user-guide/examples/gmail-workflows#use-case-2-drafting-replies-to-common-inquiries). **Email order intake** Order emails arriving in a shared inbox are read, structured fields extracted, records written to your order management system, and anomalies escalated for human review. The sender receives a confirmation or a clarification request automatically. See [Email Order Intake](/user-guide/playbooks/inbound-intake/email-order-intake). *** ### Snowflake These workflows read directly from your data warehouse to power reports, alerts, and exports — without requiring a data engineer or a BI dashboard. **Scheduled KPI report** Every Monday morning, a weekly summary of revenue, order count, exception rate, and week-over-week changes is posted to your team's Slack channel or emailed to stakeholders. The agent queries Snowflake, formats the results, and distributes them automatically. See [Snowflake Workflows — Scheduled KPI Report](/user-guide/examples/snowflake-workflows#use-case-1-scheduled-kpi-report-from-warehouse-data). **Inventory threshold alerts** When SKU inventory drops below the reorder point, the agent posts a Slack alert with the specific items and quantities. Alerts are deduplicated so the same issue does not notify repeatedly before anyone can act. See [Snowflake Workflows — Threshold Alert](/user-guide/examples/snowflake-workflows#use-case-2-threshold-alert-when-a-metric-breaches-a-limit). **On-demand data exports to Google Sheets** A stakeholder needs a fresh data pull — customer list, transaction export, regional breakdown. The agent runs the query and writes results into a named Google Sheet, including a "last refreshed" timestamp. Run on demand or on a schedule. See [Snowflake Workflows — Export to Google Sheets](/user-guide/examples/snowflake-workflows#use-case-3-export-query-results-to-google-sheets). *** ### Scheduling Patterns Most automations follow one of these scheduling patterns. Choose the one that fits your workflow's cadence. **Recurring digest (daily or weekly)** Run an agent on a fixed schedule — every Monday at 7am, every weekday at 8am — to pull data, format a summary, and distribute it. Ideal for KPI reports, inbox roundups, and standing briefings that would otherwise require manual preparation. See [Scheduling Agents](/user-guide/assignment-features/scheduling-assignments) for setup instructions. **Continuous monitoring with threshold alerting** Run an agent every hour (or every 15 minutes for time-critical metrics) to poll a data source and act only when a condition is met. The agent checks silently and does nothing if everything is within bounds, so notifications are signal not noise. See [Scheduling Agents](/user-guide/assignment-features/scheduling-assignments) for setup, and [Snowflake Workflows](/user-guide/examples/snowflake-workflows) for an example using warehouse data. **Event-driven trigger with scheduled follow-up** An agent starts when an event occurs (a new email, a Slack mention, a file upload) and then self-schedules a follow-up check. For example: process an order email immediately, then check 48 hours later whether a clarification response arrived. No separate cron job needed. See [Event-Driven Triggers](/user-guide/assignment-features/event-driven-triggers) and [Scheduling Agents — Self-Scheduling](/user-guide/assignment-features/scheduling-assignments#self-scheduling-during-execution). **Batch processing window** Run a large-volume workflow once per day during off-peak hours — process all unread order emails overnight, reconcile records at 2am, generate invoices for all completed runs at end of day. Keeps interactive workloads unaffected while ensuring nothing accumulates indefinitely. See [Scheduling Agents](/user-guide/assignment-features/scheduling-assignments) for setup instructions.