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Automation

What Automation Is

Automation use cases have AI execute repeatable routine tasks with minimal human involvement. Unlike the other primitives where you interact with AI in real time, automation runs on its own — on a schedule, in response to a trigger, or as part of a pipeline. You configure it once, and it produces consistent results without manual intervention.

This is the highest-autonomy primitive. Automation typically builds on the other five primitives: a content creation workflow becomes automation when it runs weekly without prompting. A research workflow becomes automation when it monitors sources continuously. A data analysis workflow becomes automation when it generates reports on a schedule.

Automation delivers the largest time savings because it eliminates recurring manual work entirely. But it also requires the most upfront investment in configuration, testing, and monitoring — you're trusting AI to act independently, so the instructions, guardrails, and error handling need to be robust.

Automation is one of six use case primitives identified in OpenAI's Identifying and Scaling AI Use Cases guide. The examples here are adapted to be platform-agnostic and mapped to Agentic Building Blocks.

Key Characteristics

  • Runs without real-time human involvement — the workflow executes on a schedule, trigger, or as part of a pipeline
  • Builds on other primitives — automation is rarely a primitive by itself; it's the operational wrapper around content creation, research, coding, data analysis, or ideation tasks
  • Requires robust instructions — since nobody is there to course-correct in real time, the instructions must handle edge cases, errors, and unexpected inputs
  • Monitoring matters — automated workflows need logging, alerting, and periodic review to ensure they're still producing quality output
  • Compounds over time — each automated workflow frees up recurring hours, and the savings accumulate week over week

When to Apply This Primitive

Use Automation when:

  • A workflow runs on a predictable schedule (daily, weekly, monthly)
  • The same steps execute with different inputs each time
  • The workflow has clear success criteria that don't require subjective judgment
  • Speed and consistency matter more than creative nuance

NOT the right primitive when:

  • The task requires real-time human judgment or creative direction (use the underlying primitive directly)
  • The workflow is unpredictable — different steps, different logic each time
  • You're still iterating on the workflow design (automate after you've proven the process works manually)

Department Examples

Department Use Case What AI Does Typical Building Blocks
Marketing Weekly competitive update Monitors competitor sites, compiles changes, and distributes a summary report every Monday Agent, MCP, Skill
Sales Lead enrichment pipeline Automatically researches new leads, scores them, and populates CRM fields when leads enter the pipeline Agent, MCP, Skill
Finance Recurring report generation Pulls data from accounting systems, generates formatted reports, and distributes to stakeholders on schedule Agent, MCP, Skill
HR Onboarding document preparation Generates customized onboarding packets when a new hire is confirmed, pulling role-specific content and formatting Agent, Skill, Context
Product Release communication Generates release notes, changelog entries, and customer-facing announcements when new versions are deployed Agent, MCP, Skill
IT/Operations System health summaries Aggregates monitoring data, identifies trends, and produces daily operational summaries Agent, MCP, Skill

Building Block Patterns

Complexity Building Blocks Example
Simple Skill (scheduled) A skill that runs daily to format and distribute yesterday's key metrics from a spreadsheet
Intermediate Agent + MCP An agent that monitors a data source via MCP, detects changes, and takes predefined actions
Advanced Agent + MCP + multiple Skills A pipeline agent that orchestrates multiple skills — gathering data, analyzing it, generating content, and distributing results — all running on a schedule

Use Cases
Department Marketing
Autonomy level Autonomous
Building blocks Agent, MCP, Skill
Problem The competitive intelligence report takes 4 hours every Monday — manually checking competitor websites, noting changes, comparing positioning, and formatting a summary for the team
Solution A scheduled agent runs every Sunday night: checks competitor sites via MCP, compares against the previous week's snapshot, generates a structured delta report using a content skill, and posts the summary to the team Slack channel
Department Sales
Autonomy level Autonomous
Building blocks Agent, MCP, Skill
Problem New leads sit in the CRM with minimal information — reps spend the first 15 minutes of every outreach sequence manually researching the company and contact before they can personalize their approach
Solution A triggered agent fires when a new lead enters the CRM: researches the company and contact via web search and LinkedIn, scores the lead against ideal customer criteria, and populates CRM fields with enriched data — so reps start every outreach with full context
Department Finance
Autonomy level Autonomous
Building blocks Agent, MCP, Skill
Problem Monthly board reporting requires pulling data from four systems, formatting it into the approved template, calculating variances, and writing commentary — the same process every month with different numbers
Solution A monthly agent pulls data from accounting, CRM, HR, and product analytics via MCP, populates the board report template, calculates period-over-period variances, drafts commentary on significant changes, and places the draft report in the CFO's review folder

Common Mistakes

Automating before the process is proven. Automate workflows that you've run manually at least several times and are confident work correctly. Automating an untested process just means you'll produce bad output faster and more consistently.

No monitoring or alerting. Automated workflows fail silently. Build in logging, periodic output review, and alerting for failures or anomalous results. The worst automation failures are the ones nobody notices for weeks.

Over-automating judgment calls. Automation works best for structured, predictable tasks. If a workflow step requires nuanced judgment (should we respond to this complaint? is this data anomaly real or a reporting error?), keep a human in the loop — even if that means the workflow pauses for review at that step.