Adding AI to Workflows
Embed LLMs into automation pipelines for classification, generation, and decision-making.
AI as a Workflow Component
AI doesn't replace workflows — it augments them. The best workflow automations use AI for the parts that require judgment or language understanding, and deterministic logic for everything else.
Pattern: structured input → AI processing → structured output → downstream action
The key is keeping AI's input and output structured. Don't give AI free-form inputs or accept free-form outputs — it makes the rest of the workflow unpredictable.
Common AI Workflow Patterns
Classification — route incoming items based on AI-determined category.
Inbound email → AI: "Classify as sales/support/spam" → Route to appropriate queue
Enrichment — add AI-generated context to existing data.
New lead → AI: "Generate company summary from website" → Append to CRM record
Generation — produce content based on structured inputs.
Event triggers → AI: "Generate status update from metrics" → Post to Slack
Extraction — pull structured data from unstructured content.
Contract PDF → AI: "Extract dates, parties, and obligations" → Store in database
The n8n AI Node
n8n includes built-in AI nodes: OpenAI Chat, Anthropic, and a generic LLM node. Configure with your API key and model selection.
Use the Code node for complex prompt construction — it's easier than n8n's expression editor for multi-line prompts.
Error Handling for AI Steps
AI calls fail (timeouts, rate limits, model errors). Always:
- Add retry logic (3 attempts, exponential backoff)
- Set reasonable timeouts (30s for most tasks)
- Define a fallback path for when AI is unavailable
- Validate AI outputs before passing downstream