The SML Ghost represents a new wave of AI-native agents designed to operate inside spreadsheets and data workflows. Built on large language model technology, it automates repetitive tasks while preserving traceable decision logic.
Unlike generic scripting tools, this agent integrates directly with cell formulas, named ranges, and structured tables. The following sections explore its architecture, use cases, and practical configuration guidance.
| Agent Name | Core Engine | Deployment Mode | Typical Latency | Access Scope |
|---|---|---|---|---|
| SML Ghost | SML-7B-flash | Cloud SaaS with local sync | 200–900 ms per step | Workspace and private |
| Base Agent | Llama 3 8B | Self-hosted Docker | 400–1200 ms per step | On-prem only |
| SheetsGPT | GPT-4o-mini | Browser extension | 600–1500 ms per step | User auth required |
| GridPilot | Mistral 7B | Hybrid cloud API | 300–800 ms per step | Team licensed |
Architecture and Model Design
Layer Breakdown
The SML Ghost stack combines a sparse Mixture of Experts with a retrieval module. This design reduces token waste while keeping reasoning steps aligned with spreadsheet constraints.
A lightweight router decides whether to run a calculation locally or delegate to the LLM backend. The system logs each decision to an audit sheet, enabling transparent debugging.
Data Transformation Workflows
Cleaning and Normalization
One prominent use case is transforming messy exports into analysis-ready tables. The agent applies type inference, deduplication, and standard naming conventions automatically.
Another workflow involves joining multiple sheets by semantic keys instead of fragile column indexes. This approach reduces broken references when source files evolve.
Integration and Connectivity
Platform Support
SML Ghost connects to Google Sheets, Microsoft 365, and Airtable through managed connectors. Rate limits and scopes are handled via a centralized service account dashboard.
For advanced users, a REST API exposes core endpoints so that custom apps can trigger long-running jobs and stream partial results.
Performance and Scaling
Throughput Benchmarks
In controlled tests, the agent processes 50,000 rows with complex lookups in under eight minutes. Memory usage remains stable thanks to row-wise batch processing.
Horizontal scaling is supported in enterprise plans, allowing parallel execution across isolated workspaces while maintaining per-user quota tracking.
Operational Best Practices and Recommendations
- Start with read-only test copies of critical sheets before enabling write access.
- Use named ranges to reduce breakage when columns are inserted or removed.
- Schedule heavy jobs during off-peak hours to align with cost and rate policies.
- Monitor audit logs to review agent decisions and refine guardrail rules.
- Version control transformation templates to ensure reproducible results.
FAQ
Reader questions
Does SML Ghost require advanced scripting skills to set up?
No, most configurations are managed through a guided UI with step-by-step prompts and template galleries.
Can it handle protected sheets and restricted ranges?
Yes, the agent respects sharing permissions and can be granted read-only or read-write access scopes as defined by workspace admins.
Will using SML Ghost increase cloud egress costs significantly?
Typical egress stays low because preprocessing happens near the storage layer, and only transformed results are sent back to the client.
Is there a free tier for evaluation purposes?
Yes, a limited free tier includes up to 100k cells per month and core transformation features without a credit card.