A practical guide to plan handoff, data boundaries, error review, and undo between ByteQuant Local Agent and Workstation.
Turn the guide into a safe trial
Complete the steps with a synthetic example before using real data. Checkmarks live only in this tab.
Choose the goal before the tool
A useful local plan begins with the expected result, not a product name. A goal such as 'turn a customer list into shareable JSON' can be separated into sensitive-data review, masking, structural validation, and export. Local Agent matches goal signals to the catalog; it does not invent facts like a generative LLM.
Before accepting a plan, read why each step was selected and inspect alternatives for unsupported formats. Confidence is an explainable matching signal, not an accuracy guarantee.
- State the input type and final output.
- Separate consequential operations.
- Compare alternatives by rationale and data compatibility.
One-click handoff is not automatic execution
Sending a plan to Workstation writes a bounded document of tool IDs and edges to same-tab sessionStorage. Workstation builds nodes but never selects a file, enters a password, runs code, or starts a download.
The first node may receive the goal as input. Data reaches later nodes only after the user chooses a result handoff. This boundary prevents a weak plan from silently propagating through the chain.
- Read every node input before running it.
- Check output compatibility with the next tool.
- Undo or disconnect when a step fails.
Experiment safely with version history
Node, edge, and layout changes enter the undo history. Save the project locally before a major change, then work in small verifiable steps so failures remain easy to isolate.
Projects are AES-GCM encrypted in on-device IndexedDB, which does not make a compromised device or malicious extension safe. Keep sensitive input out of recipe URLs and compare P2P safety codes through a separate channel.
Turn the guide into a repeatable review
Use this 3-tool review plan for “Turn a Local Agent Plan into a Safe Workstation Flow”. Goal: A practical guide to plan handoff, data boundaries, error review, and undo between ByteQuant Local Agent and Workstation. Start with a safe example instead of real data, then record each expected result and acceptance decision.
Tool Pipeline: CSV → Masking → JSON
- Prepare
- Choose a CSV file or paste text and verify the column preview. Expected format for Tool Pipeline: CSV → Masking → JSON: For Tool Pipeline: CSV → Masking → JSON, provide fields or lines that follow the tool labels and contain no unnecessary personal data. The requested outcome is to inspect CSV, mask sensitive-data candidates, and download JSON or CSV in one page..
- Apply
- Select masking types and manually review detected candidates and changed cells. Tool Pipeline: CSV → Masking → JSON applies this method: Tool Pipeline: CSV → Masking → JSON uses this disclosed method to inspect CSV, mask sensitive-data candidates, and download JSON or CSV in one page: input is structured with disclosed rules and is not sent to an external system without user action.
- Acceptance check
- Download the cleaned result as JSON or CSV and protect the source separately. Acceptance check for Tool Pipeline: CSV → Masking → JSON: Before accepting a Tool Pipeline: CSV → Masking → JSON result, complete manual review of required fields, dates and numbers, audience fit, and any official requirements; the evidence should support the goal to inspect CSV, mask sensitive-data candidates, and download JSON or CSV in one page..
- Expected output
- When Tool Pipeline: CSV → Masking → JSON finishes, it returns an editable draft, field summary, and explicit next action, organised around the goal to inspect CSV, mask sensitive-data candidates, and download JSON or CSV in one page.. Inspect CSV, mask sensitive-data candidates, and download JSON or CSV in one page.
KVKK / GDPR Data Masker
- Prepare
- Paste text into this browser tab. Expected format for KVKK / GDPR Data Masker: For KVKK / GDPR Data Masker, provide synthetic or minimized code, configuration, identifiers, or file content you are authorized to review. The requested outcome is to mask email, phone, IBAN, card, and IP patterns on-device..
- Apply
- Run masking and review detected types. KVKK / GDPR Data Masker applies this method: KVKK / GDPR Data Masker uses this disclosed method to mask email, phone, IBAN, card, and IP patterns on-device: content is not executed; only explainable static patterns and bounded browser operations are applied.
- Acceptance check
- Manually verify missed or incorrect replacements. Acceptance check for KVKK / GDPR Data Masker: Before accepting a KVKK / GDPR Data Masker result, complete manual review at the source location and independent verification with an appropriate professional security tool or authorized process; the evidence should support the goal to mask email, phone, IBAN, card, and IP patterns on-device..
- Expected output
- When KVKK / GDPR Data Masker finishes, it returns evidence locations, severity, false-positive considerations, and the next verification action, organised around the goal to mask email, phone, IBAN, card, and IP patterns on-device.. Mask email, phone, IBAN, card, and IP patterns on-device.
JSON Formatter & Validator
- Prepare
- Paste JSON data. Expected format for JSON Formatter & Validator: For JSON Formatter & Validator, provide syntactically valid JSON containing the object, array, or fields named by the tool. The requested outcome is to validate, pretty-print, or minify JSON data..
- Apply
- Choose pretty or minified output. JSON Formatter & Validator applies this method: JSON Formatter & Validator uses this disclosed method to validate, pretty-print, or minify JSON data: parsing uses deterministic rules that preserve field and type boundaries.
- Acceptance check
- Copy the validated result. Acceptance check for JSON Formatter & Validator: Before accepting a JSON Formatter & Validator result, complete field names, value types, escaping, and empty or null values compared with the source; the evidence should support the goal to validate, pretty-print, or minify JSON data..
- Expected output
- When JSON Formatter & Validator finishes, it returns a parsed structure, field metrics, and explicit syntax findings, organised around the goal to validate, pretty-print, or minify JSON data.. Validate, pretty-print, or minify JSON data.
Apply this boundary to Tool Pipeline: CSV → Masking → JSON: Tool Pipeline: CSV → Masking → JSON limitation: Verify schema, encoding, and data-loss assumptions in the target system. If that condition is not met, do not pass the output to the next workflow step.
For “Turn a Local Agent Plan into a Safe Workstation Flow”, record the tool, selected setting, browser version, and acceptance or rejection reason for “Preparing sample data for GDPR/KVKK: local analysis with Tool Pipeline: CSV → Masking → JSON”—not the sensitive content. This keeps the review repeatable without copying real data.
Content is checked against visible ByteQuant product behavior and the listed primary sources where available. It is general information, not legal or security advice.