This article provides a complete, practical walkthrough of the Schedule Manual Monitor Scheduled n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automating Sales Call Processing with n8n: A Deep Dive into the CallForge Workflow
   
   Meta Description:  
   Discover how CallForge leverages n8n’s no-code automation to process Gong sales calls using Salesforce data. Learn how this intelligent workflow streamlines pre-qualification and data extraction for AI processing.
   
   Keywords:  
   n8n workflow, Gong API, Salesforce integration, sales automation, sales call AI, Gong call transcription, no-code automation, call processing workflow, Gong Salesforce, AI-enhanced sales, automation SaaS
   
   Third-Party APIs Used:
   
   - Salesforce API (via Salesforce OAuth2)
   - Gong API
   
   Article:
   
   Sales teams are increasingly relying on call intelligence platforms like Gong to extract deeper insights from customer conversations. But turning raw sales call data into actionable insights involves more than just recording; it requires intelligent filtering, enrichment, and automation.
   
   That’s exactly what CallForge achieves using n8n, a powerful open-source workflow automation platform. In this article, we’ll break down a real-world n8n workflow designed to automatically identify, enrich, and route Gong sales calls for AI summarization — filtering them dynamically based on Salesforce opportunity data.
   
   📞 What Is CallForge?
   
   CallForge is an integration framework built to automatically extract key information from Gong call recordings. It’s tailored for cross-functional use, enabling departments like sales, marketing, and customer success to receive AI-parsed call summaries relevant to their functions.
   
   The goal? To reduce manual processing time and ensure only calls with meaningful progress are passed on for deeper analysis or AI transcription parsing.
   
   🔄 High-Level Workflow Overview
   
   This n8n automation operates on an hourly schedule. It polls Salesforce via its Gong integration, checks for new call recordings, and processes only those that meet specific deal stage criteria—ensuring resources are prioritized for valuable conversations.
   
   Let’s explore how this works step-by-step.
   
   🕐 1. Trigger and Schedule
   
   The workflow begins with two triggers:
   
   - ManualTrigger: For testing the workflow
   - ScheduleTrigger: Runs hourly to check for new call data
   
   These triggers connect to Salesforce to fetch Gong call records that were created in the past four hours.
   
   🧩 2. Fetch Gong Records from Salesforce
   
   The Salesforce node “Get all custom Salesforce Gong Objects” fetches recent Gong__Gong_Call__c objects. Key fields retrieved include:
   
   - Gong__Call_ID__c
   - Gong__Primary_Opportunity__c
   - Gong__Opp_Stage_Time_Of_Call__c
   - Gong__View_call__c
   - CreatedDate
   
   These fields are essential for filtering and enriching the dataset before further processing.
   
   🧮 3. Filter and Sort Records
   
   The “Sort by Date” node orders calls by their creation timestamp in descending order, ensuring the latest calls are prioritized.
   
   Two conditional checks are applied next:
   
   a) Check if the opportunity’s current stage (at the time of the call) is either “Discovery” or “Meeting Booked”  
   b) Ensure the call is linked to a primary Salesforce opportunity
   
   Only calls passing both conditions proceed to the enrichment and formatting stages.
   
   🔍 4. Get Gong Call Details
   
   For each qualified call, the “Get Gong Call” node pulls detailed metadata directly from the Gong API using the call ID. This data includes:
   
   - Duration
   - Call direction
   - Scheduled and actual start time
   - Primary user
   - Meeting URL
   - Workspace ID
   - Audio language
   - Purpose
   - Link to Salesforce Opportunity (Gong__Primary_Opportunity__c)
   
   🧱 5. Format for AI Processing
   
   Once enriched, a “Set” node reformats the metadata into a clean JSON structure compatible with downstream AI tools.
   
   This JSON serves as structured input for an AI agent and includes:
   
   - Unique call identifiers
   - User and meeting metadata
   - Timing and duration
   - Prospecting disposition
   - Salesforce Opportunity link
   
   This ensures consistency and reliability in how data is fed into machine learning models.
   
   🤖 6. Route to Preprocessor Workflow
   
   Finally, the data is passed to a sub-workflow — “Gong Call Preprocessor Demo” — using the Execute Workflow node. This allows for modular handling of transcription analysis, AI summarization, or delivery to departmental dashboards/toolkits.
   
   ✅ Why This Matters
   
   Sales organizations handle hundreds, if not thousands, of Gong calls weekly. Manual processing is not scalable. With this workflow:
   
   - Only qualified calls (based on opportunity stage and presence of linked deals) are processed
   - Gong API is used to retrieve call metadata on-demand
   - Calls are transformed into AI-friendly JSON for downstream analysis
   - Everything is run automatically on an hourly interval – fully hands-off
   
   🔐 APIs Used
   
   This integration leverages two critical APIs:
   
   - Gong API: To retrieve call metadata via call ID
   - Salesforce API: To fetch conversation objects and opportunity data using OAuth2 authentication
   
   🎯 Conclusion
   
   For sales operations, automation means smarter workflows, faster insights, and more impactful conversations. Instead of wasting compute resources and time on low-interest calls, CallForge ensures that only qualified engagements are sent for deeper AI processing — all seamlessly orchestrated in n8n.
   
   If you're working with Gong and Salesforce in your tech stack, a no-code automation like this one can elevate your sales intelligence engine and extend the value of every customer call.
   
   —  
   Need help building a similar system for your organization? Explore pre-built templates or start building in n8n today.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.