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Documentation Index

Fetch the complete documentation index at: https://docs.adopt.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

Pipelines shine when you need data to be ready before an agent runs. If your agent requires context — historical records, aggregated totals, filtered lists — a Pipeline ensures that data is pre-processed and available instantly. This page covers the most common use cases for Pipelines and helps you decide when to use a Pipeline versus a Tool.

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Core Use Cases

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1. Aggregating Financial Data for Analysis

Scenario: A finance agent calculates R&D tax credits each month. To do this, it needs a clean, structured view of all relevant expenses from your accounting system. Why a Pipeline: Pulling thousands of expense records from QuickBooks at agent runtime would be slow and unreliable. Instead, a Pipeline extracts and aggregates expenses daily — so when the agent runs, it simply reads from the pre-built aggregated_expenses outcome.
Use Pipelines when you need to aggregate expense data from QuickBooks for monthly R&D tax credit calculations.

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2. Pre-Processing CRM Data for Sales Agents

Scenario: A sales support agent needs to show a rep the full history of a customer — deals, contacts, notes — before a call. Why a Pipeline: CRM data is large and relational. A Pipeline extracts and joins the relevant records on a schedule, producing a clean customer_records table. The agent reads from this table instead of making multiple live API calls during the session.
Use Pipelines to pre-process customer records so your support agent can show relevant deal history and contact details without latency.

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3. Monitoring Risk and Compliance

Scenario: A compliance agent flags any deals or transactions above a risk threshold for human review. Why a Pipeline: Risk data needs to be continuously refreshed. A Pipeline set to run hourly pulls deals from your CRM, filters those above the threshold, and writes them to a deals_over_10k outcome. A HITL Experience then displays this table to the reviewing team.

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4. Populating HITL Dashboards

Scenario: Your operations team reviews an AI-generated summary each morning. The dashboard needs to show live data from multiple systems. Why a Pipeline: HITL Experiences bind to Pipeline Outcomes to display structured data in tables and charts. The Pipeline runs overnight to prepare the data; when the reviewer opens the Experience, the dashboard loads instantly.

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5. Normalizing Data Across Systems

Scenario: Your company uses both Salesforce and HubSpot. An agent needs a unified view of accounts. Why a Pipeline: A Pipeline can extract data from both systems (using two Connectors), transform and normalize the schemas, and write a unified accounts table. The agent works from a single, clean dataset.

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Pipelines vs. Tools: Decision Guide

Use this table to decide whether to use a Pipeline or a Tool for a given need.
NeedUseExample
Pre-fetch and store data before an agent runsPipelinePull all deals from CRM nightly
Display data in a HITL dashboardPipelineShow expense totals in a review table
Aggregate or transform large datasetsPipelineGroup expenses by category and project
Refresh data on a schedulePipelineUpdate customer records every hour
Send an email during agent executionToolSend Email tool called when agent triggers
Create a record in real timeToolCreate Salesforce Opportunity called by agent
Fetch a single live record on demandToolGet Contact by ID called during agent run
Trigger a webhook or external actionToolNotify Slack Channel called by agent
A good rule of thumb: if the data exists before the agent runs and doesn’t change per-request, use a Pipeline. If the action or data fetch happens during agent execution in response to something specific, use a Tool.

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What Data Sources Work With Pipelines?

Pipelines connect to external systems via Connectors. Any system with a Connector configured in Adopt AI can serve as a Pipeline data source. Common examples include:
  • CRM systems — HubSpot, Salesforce
  • Accounting & finance — QuickBooks, Xero
  • Cloud storage — AWS S3, Google Drive
  • Databases — internal databases via JDBC or REST
  • Productivity tools — Google Sheets, Airtable
  • Support platforms — Zendesk, Zoho Desk
If you don’t yet have a Connector for your data source, you can create one during the Pipeline setup flow. See the Connectors documentation for details.

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When NOT to Use Pipelines

Pipelines are not the right choice when:
  • The data is user-specific and requested at runtime (e.g., “fetch the details of this specific ticket the user just mentioned”)
  • The operation writes data to an external system (e.g., creating a record, sending a notification)
  • The data changes so rapidly that scheduled pre-computation would always be stale
  • The operation requires real-time context from the conversation (e.g., looking up a specific customer by name the user just said)
For these cases, use Tools instead.

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Next Steps