Most large language models are impressive with natural language, but they consistently break down when you ask them to think like Excel—operating on a grid, not a paragraph.
For business leaders betting on AI spreadsheets, that gap is more than a curiosity; it is a risk surface touching financial accuracy, operational automation, and the integrity of your tabular data workflows.
The hidden flaw: LLMs weren't built for spreadsheets
LLMs were trained on text, not row-column logic.[1] Each cell in a spreadsheet becomes a tokenized fragment in a long sequence—stripped of its spatial meaning and drowned in context the model cannot reliably track at scale.[1][2]
That's why, when you push a real-world Excel model through an "AI Spreadsheet" wrapper that just dumps CSV data into a prompt, you see:
- Hallucinating numbers when doing basic mathematical operations
- Silent context drift as the model "forgets" which row it's on
- Subtle data drift where values shift columns or tables
- Unreliable handling of large datasets and complex grid state[1][2]
In other words: you ask for an income statement variance, and you get something that looks right but isn't traceable, auditable, or reproducible.
If your business runs on Excel, that is not "AI magic." That is unmeasured model risk.
Why the grid breaks the model
Think about what Excel does that plain text doesn't:
- A spreadsheet is a two-dimensional information system, not a sentence.[1]
- Every number means something because of its row and column, not just its value.
- The same pattern can apply across thousands of rows—where mistakes compound quietly.
When this structure is linearized into tokens:
- Row-column relationships get blurred.
- Formula ranges (e.g., a SUM over 20 cells) explode into dozens of tokens with no native notion of a grid.
- As the sheet grows, the model's attention fragments—and mathematical errors creep in.[1][2]
Most current "AI Spreadsheets" never address this. They just rely on better prompt engineering, bigger context windows, or more powerful LLMs. The result: more expensive hallucinations.
A different architecture: make the LLM an orchestrator, not a calculator
The core shift is conceptual: stop asking the LLM to be Excel. Ask it to coordinate Excel-like operations using tools that are actually good at data manipulation and mathematical operations.
Instead of letting the model touch your numbers directly, you can:
- Treat the LLM as an orchestrator that understands user intent in natural language.
- Have it generate Python or SQL code to perform precise dataset manipulation.
- Execute that code inside a sandboxed environment—for example, a Docker container—so all operations on the grid state are deterministic, auditable, and isolated.
This is the agentic framework approach behind Motion Excel:
- The agent (LLM) interprets: "Filter all customers with ARR > 50K and flag churn risk."
- It writes Python/SQL targeting your underlying tabular data.
- The code runs in a sandboxed Docker container, updates the spreadsheet-like grid, and returns exact results—no hallucinated formulas, no invisible context loss.
- Every step is observable, testable, and repeatable.
When engineered correctly, this architecture has delivered a 68% accuracy fix over naive "LLM-only" spreadsheet interaction on complex grid tasks—without asking the model to magically become a perfect calculator.
Why this matters for Excel-heavy organizations
If your teams live in Excel and AI is on your roadmap, this architecture reframes the opportunity:
From "AI in Excel" to "Excel as a programmable data plane"
Excel becomes the user-facing grid; the intelligence layer lives in an orchestrated network of Python, SQL, and agents running in containerized infrastructure.From hallucinations to governed automation
Because code runs in a sandboxed Docker container, you can log, review, and test every transformation—bringing engineering-grade software architecture discipline to spreadsheet automation.From prompt-driven hacks to robust data processing
The LLM does what it does best—language understanding, task decomposition, and agent routing logic—while your data stack handles the heavy lifting of computation.From opaque "AI Spreadsheet" wrappers to transparent systems
Instead of black-box answers, you get explicit, inspectable logic: the generated SQL, the Python script, the updated CSV representation of your grid.
The strategic question: where should intelligence live in your spreadsheet stack?
Once you stop treating the LLM as a calculator, more provocative questions emerge:
- Should artificial intelligence sit inside the workbook, or above it as a control plane?
- How much of your current Excel logic should be codified into reusable automation running in containers, instead of living only in cell formulas?
- Could a network of agentic frameworks—each specialized for cleansing, reconciliation, forecasting—work together to transform your spreadsheet ecosystem into a governed, machine-assisted data fabric?
- How will you use sandboxing and containerization to manage risk when AI agents are allowed to change financial or operational sheets?
For organizations looking to implement similar agentic AI frameworks, understanding these architectural decisions becomes crucial for successful deployment.
Motion Excel as a living experiment
Motion Excel is exploring this architecture in a private developer preview, with code available on GitHub at https://github.com/hritvikgupta/motion-excel.git and early access via www.motionexcel.co.
Under the hood:
- LLMs interpret intent and manage agent routing logic.
- Operations on tabular data are expressed as Python or SQL routines.
- Execution happens in a sandboxed Docker container that maintains and manipulates the spreadsheet-like grid state.
- The system is being tested and refined with the Excel community on Reddit (r/ExcelTips), focusing specifically on the hardest edge cases where standard AI Spreadsheets fail.
The aim is not to replace Excel, but to redesign how machine learning interacts with it—shifting from "LLM as all-knowing brain" to "LLM as orchestration layer in a disciplined software architecture."
For teams exploring similar approaches, comprehensive guides on building AI agents provide valuable implementation insights.
Concepts worth sharing with your leadership team
If you want to spark a deeper conversation about AI and Excel inside your organization, here are some shareable ideas:
"Most AI spreadsheets don't understand spreadsheets—they only see CSV."
Without a notion of grid state and row-column logic, you are just running language models on de-structured data."LLMs hallucinate; containers don't."
Move arithmetic and data manipulation out of the model and into deterministic code running in sandboxed Docker containers."Treat the LLM as a routing brain, not a calculator."
The competitive edge will come from sophisticated agent routing logic and tool orchestration, not ever-larger context windows."Spreadsheet automation is a software architecture problem, not a prompt engineering trick."
The real unlock is combining Python, SQL, containerization, and agentic frameworks with your existing Excel workflows."The future of Excel is not just smarter formulas—it's orchestrated, auditable AI pipelines operating on the grid."
If your business runs on spreadsheets, the question is no longer whether to use artificial intelligence with Excel, but where you choose to place intelligence, control, and accountability in that stack.
For organizations ready to explore these possibilities, Zoho Projects offers powerful workflow automation capabilities that can complement your AI-enhanced spreadsheet initiatives, while Zoho Flow provides the integration backbone needed to connect your various data sources and automation tools.
Why do large language models (LLMs) struggle with spreadsheets?
LLMs are trained on linear text, not two‑dimensional row/column structures. When you linearize a sheet into tokens (CSV or plain text), the model loses native notions of cell coordinates, ranges, and spatial relationships. That leads to context drift, misplaced values, and arithmetic errors as the sheet scales.
What is "grid state" and why does it matter?
"Grid state" is the two‑dimensional layout of a spreadsheet: the cells, their row/column coordinates, formulas, and interdependencies. It matters because a number only has meaning in context; losing that structure (by tokenizing into text) breaks traceability, reproducibility, and correct computations.
What kinds of failures should I expect when an LLM directly manipulates spreadsheet data?
Common failures include hallucinated numbers in arithmetic, silent context drift (model "forgets" row/column), data drift (values shift between columns/tables), incorrect handling of large ranges, and non‑reproducible results that appear plausible but are wrong.
Won't bigger context windows or better prompts fix these spreadsheet issues?
Not reliably. Larger context windows and prompt engineering can reduce some errors but don't restore inherent two‑dimensional semantics or deterministic math. They also increase cost and can still fragment attention as sheets grow. The more robust solution is to move numeric operations to deterministic tools and use the LLM for intent and orchestration.
What does it mean to make the LLM an "orchestrator, not a calculator"?
Instead of asking the LLM to compute directly on cells, treat it as the component that understands user intent, decomposes tasks, and generates precise code (e.g., Python or SQL). That code runs in deterministic environments that manipulate the grid. The LLM coordinates work; the execution engine performs calculations.
How does sandboxed code execution (e.g., Docker) improve safety and auditability?
Sandboxed containers run generated scripts in isolated, reproducible environments. All transformations become code that can be logged, tested, and replayed. That eliminates silent model edits, makes results auditable, and confines risk—containers don't hallucinate arithmetic the way LLMs can.
Which languages or tools should the LLM generate to manipulate spreadsheet data?
Common choices are Python (pandas) for flexible data manipulation and SQL for set‑based operations against tabular stores. The LLM can generate either, and the chosen runtime executes the code in a sandbox to update the grid state deterministically.
How does this approach handle large datasets and complex formula ranges?
By delegating heavy computation to engines built for such tasks (databases, pandas, or other data processing stacks) you avoid the LLM's attention fragmentation. Code can operate on ranges efficiently, use vectorized operations, and scale to large tables while preserving correctness and performance.
What is an "agentic framework" or "agent routing logic" in this context?
An agentic framework coordinates multiple specialized agents (e.g., cleansing, reconciliation, forecasting). The LLM routes tasks to the right agent, composes code, and sequences operations. Each agent may have a defined toolset (SQL runner, Python sandbox) so responsibilities are modular and auditable. For organizations looking to implement similar systems, comprehensive agentic AI frameworks guides provide detailed implementation strategies.
How do you validate and govern results produced by this architecture?
Use code review, unit/integration tests, deterministic replay of container runs, change logs, and signature checks on produced artifacts (updated CSVs, SQL statements, Python scripts). Governance layers can approve or flag automated changes before they land in production sheets.
How much accuracy improvement can organizations expect compared to naive LLM-only approaches?
Results vary by use case, but experiments with this architecture have shown substantial improvements. For example, Motion Excel reports a 68% accuracy fix on complex grid tasks versus naive LLM‑only wrappers—because deterministic code replaces error‑prone model arithmetic.
When should an organization adopt an orchestrator + sandbox approach rather than sticking with conventional Excel automation?
Adopt this approach when accuracy, auditability, and scalability matter—especially for financial, forecasting, reconciliation, or operational automation workflows. If you need reproducible transformations, traceable logic, and safe automation across large tables, orchestrated code execution is preferable to black‑box LLM answers. For teams ready to implement such systems, Zoho Flow provides powerful workflow automation capabilities that can serve as the integration backbone for connecting various data sources and automation tools.
What infrastructure and operational practices are required to implement this architecture?
Key components: an LLM for intent parsing and routing, a code generator (Python/SQL), sandboxed execution environments (Docker containers), secure data access controls, logging and audit trails, CI/testing for generated code, and an approval/workflow layer before applying changes to production sheets. Organizations can leverage n8n for flexible AI workflow automation that provides the precision of code with the speed of drag-and-drop interfaces.
Where can I explore implementations or learn more about this pattern?
Motion Excel is exploring this architecture in a private developer preview; code is available at https://github.com/hritvikgupta/motion-excel.git and early access at www.motionexcel.co. For general implementation guidance on agentic frameworks, see resources on building AI agents with LangChain/LangGraph and related agent architecture guides.
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