Testing Vocci AI's Agentic Workflow: From a Single Voice Prompt to Semantic Search, Google Sheet Generation, and HITL Sharing
Hey everyone,
I wanted to share an end-to-end test I ran with Vocci AI to see how it handles multi-step, agentic workflows using ambient voice capture on the ring.
Specifically, I wanted to test whether it could retrieve data from my personal knowledge base in Google Drive, synthesize that information into a new spreadsheet, look up a specific contact, and safely share the file.
Here is the breakdown of the prerequisites, the prompt used, and the results.
1. The Prerequisites (What Was Configured First)
For this workflow to execute, two key setups were already in place:
- Connected Workspace Integrations:
- I had previously authorized Vocci AI with access to my Google Workspace ecosystem (Google Drive, Gmail, Google Contacts, Google Sheets, and Google Docs).
- Pre-Existing Structured Knowledge Base (Second Brain in Drive):
- My personal Second Brain in Google Drive is built on the Open Knowledge Format (OKF), using structured Markdown/Docs with clear YAML metadata, distinct category headers, and organized text notes.
- Important note: My knowledge base already carried this structure as standard practice and was not modified or restructured for this test in any way. I have not personally tested this on an unorganized or unstructured vault, so I cannot say definitively how it handles messy notes, but maintaining clean structure is good practice, especially as a knowledge base grows in size and context retrieval becomes more challenging.
2. The Test Prompt
I held the physical action button on the Vocci Ring and gave the following spoken command:
>"I want you to search my second brain for information about my pets. I want you to organize the information into a Google Sheet, save it to my Google Drive, and then send it to my wife under the contact name Baby."
3. What Happened (Step-by-Step Execution)
- Semantic Search & Retrieval:
- Vocci AI searched my Drive and located my pet roster note. It accurately parsed the multi-category records (names, statuses, care notes, and background history) without mixing up entries or hallucinating data.
- Spreadsheet Generation:
- It organized and mapped the extracted data into a native Google Sheet with structured tabular columns: Category, Name, Species / Breed, Sex, Status, and Notes / History.
- Contact Resolution:
- It cross-referenced Google Contacts, matched the contact entry saved directly under the name "Baby", and pulled the correct email address.
- Human-in-the-Loop (HITL) Confirmation:
- Crucially, the agent did not silently email the file. It paused execution and prompted me for explicit confirmation before performing the outbound send.
- Execution & Delivery:
- Once confirmed, it saved the spreadsheet to Google Drive, granted Editor/Write permissions to her email, and dispatched the notification email containing the direct file link.
4. Verification & Results
- My wife received the email notification and opened the spreadsheet.
- The file rendered with all categories and columns intact, and write/edit access was fully functional on her end.
5. Takeaways & Practical Tips for Others
- System Structure Pays Off: Having structured source files beforehand made semantic parsing straightforward. While I have not tested less optimized vaults, maintaining clean headings and metadata is likely critical to preventing retrieval rot as more documents accumulate over time.
- Contact Integration Works Well: As long as Google Contacts is connected, referencing contacts by their saved entry name in natural speech works smoothly.
- HITL Safety is Vital: Having the agent pause for confirmation before performing external actions (like sending an email) prevents accidental dispatches. In my test the AI did not show me what the draft would look like prior to sending it so I would probably add this to the prompt before it actually sends it. This gives the user the opportunity to review and refine the email draft.
- Room for Exploration: In this test, the file defaulted to the root directory of Google Drive and granted Editor access. Future tests will explore whether specifying exact folder paths and custom permission levels (such as view-only) can also be controlled directly via voice.
Has anyone else experimented with multi-hop document generation or tool chaining through Vocci AI? Curious to hear what workflows others are building.