What this does

An AI-powered email automation system reads incoming emails, classifies the sender's intent, drafts context-aware replies, and triggers downstream workflows such as ticket creation orSlack notifications. The system sits between an email provider and the LLM endpoint.

Steps

Step 1 — Connect to the email provider.

Use the email provider's API (Gmail API or SendGrid Inbound Parse) to fetch new emails. Poll at a fixed interval or use webhooks for real-time delivery. Extract the sender address, subject, body plain text, and timestamp.

Step 2 — Classify intent with a prompt to the LLM.

Send the email content to the LLM with a structured classification prompt. Ask the model to output a JSON object with fields: intent (e.g., support_request, billing_inquiry, sales_lead), urgency (low/medium/high), and department (e.g., sales, ops). Parse the JSON response.

Step 3 — Draft a reply using the classification result.

If the intent warrants a reply, construct a second prompt that includes the original email, the classification, and a style guide (tone, length constraints). Request the LLM to generate a draft reply. Validate the draft length and sanitize output before sending.

Step 4 — Route to the appropriate workflow.

Based on the classification, trigger actions: send the drafted reply via the email API, open a support ticket in a CRM, or post a message to a Slack channel. Use a decision table to map each intent to one or more actions.

Step 5 — Handle errors and escalation.

If the LLM call fails, do not send a generic error response. Log the failure, place the email in a dead-letter queue, and flag it for manual review. If the intent is marked high urgency, alert a human immediately regardless of draft availability.

Step 6 — Deduplicate and track.

Maintain a record of processed email IDs to prevent reprocessing on the next poll cycle. Store each email's classification, draft, and routing decision for analytics.

Record the local run evidence. Save the exact command, runtime or package version, model name if applicable, and observed output so the result can be reproduced later.
Confirm the local starting state. Print the active binary, package version, model name, or configuration path before changing the workflow.
Run the smallest complete path. Execute the minimum command or script that proves the guide works end to end on the local machine.
Compare against expected output. Check the final line, status code, generated artifact, or model response against the verification section before expanding the setup.
Record the local run evidence. Save the exact command, runtime or package version, model name if applicable, and observed output so the result can be reproduced later.

Verification

Send a test email with a billing inquiry. Confirm the system classifies it as billing_inquiry, drafts a reply within the configured tone, and routes it to the billing workflow.
Send a second email with the same subject. Confirm it is deduplicated and not reprocessed.
Kill the LLM endpoint mid-cycle. Confirm the email lands in the dead-letter queue and an alert fires.

Common failures

LLM output is not valid JSON: Use a response format that constrains the model (e.g., OpenAI's JSON mode or a regex parser) rather than raw prompting.
Webhook replay on restart: Email webhooks may replay on service restart. Always check the processed-ID set before acting.
Reply loops: If the automated reply is sent to a mailing list or auto-responder, it can trigger a loop. Filter out no-reply and mailing-list domains before drafting.

Related guides

How to Set Up Model Fallback Chains (Local to Cloud) — ensures the LLM call for drafting is reliable
How to Implement AI Agent Logging and Audit Trails — creates an immutable record of each email classification and action taken