Compute Verified Campaign Stats with Bedrock AgentCore & Slack

Compute & Verify Campaign Statistics—then publish them to Slack with Bedrock AgentCore

This n8n workflow accepts a webhook payload of campaign numbers, computes descriptive statistics using Amazon Bedrock AgentCore (via Code Interpreter), verifies the result inside n8n, and posts a clean, formatted summary to Slack.

What this workflow does

• Receives input via HTTP webhook: An HTTP POST request sends a JSON body containing a required values array of numeric campaign values, and an optional label.
• Normalizes and sets defaults: The workflow prepares the agent name, Slack channel, label, and the values list for downstream steps.
• Computes statistics in Bedrock AgentCore: It sends the label and values to an Amazon Bedrock AgentCore agent configured to run Python using the Code Interpreter tool, returning strict JSON statistics.
• Verifies results in n8n: n8n parses the agent response, recomputes the mean and count locally, and flags output as verified only if it matches within a small tolerance.
• Posts to Slack: The workflow publishes the verified (or unverified) summary to the configured Slack channel.

Use cases

• SaaS reporting checks: Automatically validate computed campaign metrics before sharing them with teams.
• Ops dashboards: Push mean/count-style descriptive stats to Slack for quick, consistent monitoring.
• Webhook-driven analytics: Trigger stat computation from other systems by sending {"values":[...],"label":"..."} to the workflow webhook.

Technical details

• Nodes / tools: webhook, set, code, slack, sticky note, and the awsn8n-nodes-agentcoreagent core harness.
• Integrations: Amazon Bedrock AgentCore (Code Interpreter with Python) and Slack.
• Activation: Configure Bedrock AgentCore and Slack credentials, copy the webhook URL, and POST JSON payloads to run the workflow.