A practical guide to plan handoff, data boundaries, error review, and undo between ByteQuant Local Agent and Workstation.
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 safe trial
Test the steps in “Turn a Local Agent Plan into a Safe Workstation Flow” with synthetic data in Tool Pipeline: CSV → Masking → JSON before using live material. Checkmarks remain only in this tab.
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.
- Apply
- Select masking types and manually review detected candidates and changed cells.
- Acceptance check
- Download the cleaned result as JSON or CSV and protect the source separately.
- Expected output
- an editable draft, field summary, and explicit next action. 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.
- Apply
- Run masking and review detected types.
- Acceptance check
- Manually verify missed or incorrect replacements.
- Expected output
- evidence locations, severity, false-positive considerations, and the next verification action. Mask email, phone, IBAN, card, and IP patterns on-device.
JSON Formatter & Validator
- Prepare
- Paste JSON data.
- Apply
- Choose pretty or minified output.
- Acceptance check
- Copy the validated result.
- Expected output
- The result includes formatted JSON, root type, key count, maximum depth, and UTF-8 output bytes. Key count is not the same measurement as array length.. Validate, pretty-print, or minify JSON data.
Apply this boundary to Tool Pipeline: CSV → Masking → JSON: 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”—not the sensitive content. This keeps the review repeatable without copying real data.
“Turn a Local Agent Plan into a Safe Workstation Flow” was prepared by comparing visible ByteQuant behavior for local automation and reproducible product checks. Its limits and acceptance criteria support review; they do not replace legal or security advice.