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Half the calls ended in silence.
The agent said its line - "one moment, let me check the calendar" — and then did nothing. No error, no exception. The transcript looked perfect, which is exactly why it survived for weeks.
I found it by ignoring what the agent said and counting what it actually did: tool invocations in the call artifacts. On roughly half of all calls: zero.
The model was narrating the action instead of performing it. Two changes fixed it - a larger model, and moving the filler line to the platform's request-start hook, so speech and execution stopped sharing a path.
The lesson I keep re-learning with voice agents: transcripts lie. They show you what was said, not what happened. If you are debugging a live agent, count the tool calls.
Yes yes. You are on point
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AI Assistant Using Your Business Knowledge Base — RAG on Your Documents
THE PROBLEM
Q&A bots break down when knowledge lives in documents: a 100-page manual has no "questions" to match, it can't fit into a prompt, and generic chatbots hallucinate instead of admitting what they don't know.
THE SOLUTION
A RAG (Retrieval-Augmented Generation) knowledge base: documents are split into meaningful chunks, embedded into a vector index, and the assistant answers from the right sections — by meaning, not keywords.
Any format as-is: PDF, DOCX, TXT, Markdown — 100+ pages is fine
Answers grounded in YOUR documents — it says "I don't have that information" rather than inventing
Source references — every answer shows which document and section it came from
Runs on your infrastructure — documents never leave your control
One command to re-index after updating documents — documented, no programmer needed
LIVE DEMO
Ask it about my products, pricing, deployment and support — it answers from 6 real documents with sources:
https://kb-assistant-demo.dvc-ai.workers.dev
The 'I don't have that information' line is the part most RAG builds skip, and it's the one that matters. How do you set the cutoff? On mine, a fixed similarity threshold broke once I filtered results by user role. Scores shifted and it refused questions it could answer.
Obviously automated
Lost 2 days building a complex n8n workflow. Here's what I learned.✌️
The project: A 12-node AI Receptionist automation for a luxury resort in Bangladesh. Multi-channel (WhatsApp, Telegram, Voice), 9 Google Sheets, Gemini API, payment automation, customer CRM.
What went wrong: Added a new voice integration node. Accidentally deleted the entire workflow.
Why it happened:
No JSON exports after milestones
No version control (Git)
Testing changes LIVE on production workflow
No documentation
Building locally with zero redundancy
The fix (now implemented):
✅ JSON backups after every 2-3 nodes
✅ GitHub versioning
✅ Clone workflows for testing
✅ Modular design
✅ Complete documentation
For automation professionals: You're not just builders—you're engineers.
Treat workflows like software:
Documentation
Version control
Backup strategy
Modular testing
Client delays = lost revenue + credibility damage.
Make the systems bulletproof.
How do you prevent data loss in your automation builds? 👇
#n8n #Automation #WorkflowAutomation #NoCode #LessonsLearned #make #AIAutomation #DataAnalyst #MachineLearning
I'd try restoring one of those JSON backups into a cloned workflow before the next handoff. That way you know it can actually get you running again.