Smartsheet AI Agents Alternative: The MCP Write Record

Smartsheet has moved its AI to where many teams already type: the Smartsheet AI page says its MCP Server connects live work to Claude, Copilot, Gemini, ChatGPT, Amazon Quick and more, and that agents can act on that work from the AI tools a team already uses. For an operations director or a Head of Delivery who runs a portfolio in sheets, that changes a practical question. When an assistant in another product updates forty status cells overnight, what is left in the sheet the next morning, and could a reviewer tell what happened, who asked for it and whether the result was right?

This guide answers that question at the level of mechanics. It reads Smartsheet's own help articles and developer documentation for the MCP Server: where the write permission is decided, what the cell history records, which writes cannot be undone and how far a reviewer can reconstruct a change. It then sets that record beside the way Cognibl, from DILR.AI, records agent work, where a task cannot reach done without attached proof. The scope is deliberately narrow. The broader argument about whether a person confirming an agent's suggestion is the same as evidence belongs to our Wrike AI agents comparison, the general audit trail question to our ClickUp comparison, and regulated record keeping to our AI agent audit trail guide for UK banks.

This guide is shipped by the team behind Cognibl, a work tracker where people and AI agents share one board and a task cannot reach done without proof attached. Or see DATS, our five-stage consulting system for placing AI inside the operating model.

What do Smartsheet AI agents and the MCP Server actually do in 2026?

Smartsheet AI agents are AI features that read and act on Smartsheet data, either inside Smartsheet through Smart Assist or from an outside assistant such as Claude, ChatGPT or Microsoft 365 Copilot. Both routes run through the Smartsheet MCP Server, which exposes sheet, row, column, report and discussion tools. Smartsheet says agents work from live data, within the user's permissions, and ship with checkpoints and approval steps built in.

The detail matters because there is one doorway, not two. Smartsheet's help article on Smart Assist and MCP states that Smart Assist uses the Smartsheet MCP Server for all requests, and that the same server is available to MCP-enabled assistants including ChatGPT, Claude, Gemini Enterprise Plus, Microsoft 365 Copilot and Cursor. Smart Assist needs a Pro, Business, Enterprise or Advanced Work Management plan; an outside assistant needs Business, Enterprise or Advanced Work Management. So whether a planner asks the built-in assistant or a colleague asks Claude, the change reaches the sheet through the same tool layer, a pattern our Jira and Rovo comparison also describes for Jira, which can bring its context into other AI tools through an MCP server.

The capability list on the AI page is broad. Smartsheet describes asking what is at risk and fixing it in the same conversation, enriching hundreds of rows automatically with AI columns, building a workspace or dashboard from a description, and scheduling AI requests that run without the requester present, with results sent to chosen people under their approval. It also describes itself as a four-time Leader in the 2026 Gartner Magic Quadrant for collaborative work management, which is a vendor claim we repeat as Smartsheet's own. Underneath, the page says, Smartsheet applies its own intelligence layer and Execution Graph on top of foundation models from Azure OpenAI and Amazon Bedrock.

What the help centre adds is the hands-on view. The article on using the Smartsheet connector with Claude shows compound prompts that analyse, act and document in one conversation: scan for at-risk items, update their status, comment on each row explaining what changed, then pull the report again to confirm the fixes took effect. That is the pattern this guide examines, because it is the one where the record matters most. A read-only question leaves nothing behind. A chain of writes leaves a trail, and the quality of that trail decides whether a portfolio review the following week can trust the sheet. Our proof of done guide for agent work sets out why the finished state of a task deserves its own evidence; here the narrower subject is the edit itself.

Where does the write permission sit when an assistant edits a sheet?

For an outside assistant, the decision to let a Smartsheet write run is split across three places: the Smartsheet sysadmin, who can turn read and write permissions on or off for the organisation; the assistant's own settings, where Smartsheet documents per-tool controls for Claude; and the user's existing sheet permissions, which cap what any tool can touch.

Smartsheet's setup article for Claude is precise about the middle layer. In Claude, under the connector's configuration, each Smartsheet tool runs under one of three permissions. Always allow executes the tool automatically without asking. Needs approval prompts the user for confirmation, and if the user declines, the tool is skipped. Blocked prevents the tool from running at all. Permissions can be set tool by tool, or applied broadly to all read-only or all write functions. That is a sensible design, and it is also worth reading carefully: for a write made from an outside assistant, the approval step lives in the assistant client's settings, not in the sheet. Smartsheet's own checkpoints for Smart Assist and its native agents are a separate layer this guide does not test.

The other two layers sit elsewhere. The same article says a Claude Owner must enable the integration for the organisation, that the Claude Connector capability is on by default for eligible plans, that a System Admin can activate it in the Smartsheet Admin Center, and that organisation sysadmins can turn read and write permissions on and off. The assistant can reach only sheets, workspaces and objects the user can already view or edit, and only when the user starts a request. Smartsheet's AI page puts the principle plainly: if a user cannot access a sheet, the AI cannot either.

Two practical consequences follow for a delivery lead. First, Smartsheet's article describes these three permission levels for the Claude connector; it does not say how ChatGPT, Microsoft 365 Copilot or Gemini present the same choice, so each client your teams use needs its own check. Second, the Model Context Protocol specification puts the consent duty on the host application, which must obtain explicit user consent before invoking any tool, and it says MCP itself cannot enforce its security principles at the protocol level. In other words, the protocol relies on each client to implement the gate well, a point our MCP skills library guide makes for tool access generally. A team that standardises on one assistant can set Needs approval on every write tool and know where the setting lives. A team whose people use several assistants has a setting to check in every client, for every user who connects one.

Smartsheet's developer documentation adds the scope layer. Every tool in the MCP server tools reference lists the OAuth access scopes it needs: reading a cell's history needs read access to sheets, updating rows needs write access to sheets, and adding columns needs an admin scope on sheets. The toolsets page explains that tools are grouped into toolsets, each with up to two executors, one for reads and one for writes, so a client can discover and load only the tools it needs. That separation is helpful when you want an assistant to report but never edit: Smartsheet's Claude article lets you block all write functions while leaving the read-only ones allowed.

What record does an MCP write leave in a Smartsheet sheet?

An MCP write leaves the new cell value and an entry in that cell's history. Smartsheet's developer documentation says cell history returns timestamped changes with old and new values and user attribution, crediting each change to the user who modified the cell. Because the connector acts on the user's behalf, a reasonable expectation, worth testing, is that the account that connected the assistant is the one credited.

That is a useful record for one kind of question: when did this cell change, from what, to what, and under whose account. The tools reference describes get_cell_history as built for audit trails, debugging changes and user accountability. It is also a record with edges, and the reference names them. For regulated firms, our AI agent audit trail guide sets out what a record has to prove. History is retrieved one cell at a time, with the sheet, row and column identifiers all required. There is no bulk retrieval across cells in one request, and no way to retrieve history for an entire sheet at once. Entries come back in pages of at most 100, and the call is rate limited to 30 requests per minute per API token.

The write side has its own sharp edges, again in Smartsheet's own words. The reference marks update_rows as destructive and irreversible, and states it in one sentence a buyer should copy into any internal guidance:

"Updates are permanent and cannot be automatically undone." Smartsheet MCP server tools reference, update_rows

The same tool supports partial success: if some rows fail, the operation continues and returns which rows succeeded and which failed. Each call can touch up to 500 rows. When adding rows, cell values longer than 4000 characters are silently truncated. Deleting rows is likewise permanent. None of this is a defect. It is what a spreadsheet API does, and Smartsheet documents it plainly. But it shapes the record: on the reference's description, a batch update of a few hundred status cells is reviewed one cell at a time, and none of the documented history fields records which prompt caused a change or what the assistant was trying to achieve.

What fills the gap is convention. Smartsheet's own Claude examples ask the assistant to comment on each row explaining what changed, and to re-pull the report afterwards to confirm the fix landed. That is good practice and worth adopting. It is also a record the assistant writes about its own work, in the same session, under the same user's account. Whether it is complete depends on the prompt. We return to that distinction below, because it is the whole difference between a log of edits and evidence that the work is right. The same diagnostic approach sits behind our AI placement diagnostic, which starts by mapping where a decision is actually recorded before any tool is chosen.

What can a reviewer reconstruct from cell history, and what not?

From cell history, a reviewer can reconstruct which value changed, when, from what to what, and which user account the change is credited to. A reviewer cannot read from that history alone which prompt caused the change, which assistant made it, what the assistant was asked to achieve, or whether the new value is correct. Those answers live, if anywhere, in comments the assistant was told to write and in the assistant's own chat history.

Picture the Friday portfolio review. A programme manager asked Claude on Thursday evening to find every task due in the next two weeks that was still Not Started and mark the ones with no owner as At Risk. By Friday, sixty status cells have changed. The reviewer wants to know three things: were the right rows changed, were any rows changed that should not have been, and is At Risk actually true for each one.

The first question is partly answerable, slowly. Each changed cell has a history entry, so a reviewer who knows which cells to check can confirm the change and its time. But history is retrieved cell by cell, so finding which cells changed means either knowing in advance or comparing the sheet against an earlier export. Smartsheet's reference notes that get_sheet_version returns only a version number, with no change details, so it can tell you something changed but not what. The second question, whether anything changed that should not have, is harder for the same reason. The third question, whether At Risk is true, cannot be answered from history at all, because history records a value, not a reason.

That last point is the useful one for a buyer. Smartsheet's AI page says every AI action is traceable back to its source and every AI output is labelled. Those are Smartsheet's commitments, and a buyer should test how they appear for a write made from an outside assistant: open the cell history after a connector edit and look for the label, then check whether a report or an admin export shows assistant edits as a group. If the label is there and filterable, the first two questions get much easier. Even then, the third question needs something history was never designed to hold: the evidence that the work is right, which our task verification guide treats as a separate design problem.

A short test plan, run in a sandbox sheet before any assistant gets write access in production:

The plan takes an afternoon and answers the question a steering committee will eventually ask. If the answer is that the record is good enough for your work, Smartsheet's approach is the simpler one to run. If it is not, the next sections describe a different model.

How does Cognibl record agent work differently?

Cognibl, from DILR.AI, records agent work on the task rather than in the cells it touched. People and AI agents share one board, a task is assigned to a named agent the way it is assigned to a colleague, and a task cannot reach done until proof of work is attached. Proof is a CSV, the documents it references and, for code, a coverage report. Without it, the status change is refused, for people and agents alike.

The difference is where the evidence lives and who writes the gate. On the Cognibl product page, proof is a CSV with one row per piece of work, filled in against the template of the workflow the project runs, backed by documents such as coverage reports, decision records, exports and screenshots referenced from the CSV. Cognibl keeps a reference and a content hash for each file, never a second copy, and editing proof creates the next version. The refusal is not a reviewer's habit: the status change is refused without proof attached, and the same rule applies to a person and to an agent.

Checking happens in three layers, cheapest first. Deterministic checkers decide what a machine can decide outright, such as the template format, the required files and thresholds. Then an AI verification flow reads the proof against the definition of done line by line and writes the summary a reviewer starts from; Cognibl's two AI flows, proof validation and project status, summarise and flag but never decide. Then a person reviews a sample of the passes. A run that ends without proof does not slip through: the task moves to Blocked with the comment "Run ended without proof." so somebody looks at it. Our explainer on what Cognibl is covers the product in more depth.

The tool record is separate from the proof. Cognibl agents reach other tools through a gateway that records every call: allowed, refused or failed, each call is written to an append-only, hash-chained trace, redacted at capture. Tools are granted to a project toolset by toolset, all off by default, and the gateway keeps the credential, so the agent never holds it. Connections are read-only unless an admin ticks a write level for Google Workspace and the project turns it on. Our cloud agent isolation guide covers why the credential stays outside the agent's machine. So for agent work in Cognibl, a reviewer's questions land in three places: which agent did the work is on the task, what it called is in the trace, and whether it is right is in the proof.

It is worth being exact about what Cognibl is today, because this is a comparison of record models, not a plug-in. Cognibl agents run on Claude Code or Codex, each in its own microVM, with a token scoped to one task and revoked when the run ends. They take a task, work on a branch, open a pull request and attach proof, and only a person can merge. Cognibl does not sit between Claude and Smartsheet, and it does not govern writes an assistant makes to a sheet. If your agents' work is code, or work that can be described as a task with a definition of done and an artefact to prove it, the model applies directly. If your agents' work is editing a portfolio sheet, Cognibl is not a substitute for Smartsheet. Our Jira and Rovo comparison sets the same model beside an issue tracker.

The same principle underpins our AI operating model consulting, which decides who owns the definition of done for agent work before any platform is bought.

How do Smartsheet over MCP and Cognibl compare side by side?

Smartsheet over MCP and Cognibl answer different questions about agent work. Smartsheet over MCP lets an assistant a team already uses read and edit sheets under the user's own permissions, with approval set in the client and history kept per cell. Cognibl gives agents their own identity on a shared board and makes finished work carry attached, versioned proof before a done status is accepted.

The table below compares the record each leaves, using only what each vendor publishes. It is not a feature scorecard; rows where the two do different jobs are marked as such.

Read the last row first. It decides most of the others. A sheet is a shared data surface, and a good sheet record is about values: what changed and who changed it. A task tracker is a queue of claims that work is finished, and a good record there is about evidence. Smartsheet's approach is built for the first; Cognibl's for the second. A buyer who frames the choice as one product replacing the other will usually be comparing the wrong things. Our Monday AI agents comparison and Notion AI agents comparison set Cognibl beside other work-management incumbents.

Where does Smartsheet clearly win?

Smartsheet clearly wins wherever the work itself lives in sheets: portfolio and programme tracking, resource plans, operations trackers, cross-sheet reports and dashboards maintained by a PMO. An assistant reaching that data through the Smartsheet MCP Server, under the user's existing permissions, avoids moving the work into another tool, and Cognibl's agents do not edit Smartsheet sheets.

Several of Smartsheet's choices are genuinely strong for that work. One server serves the built-in assistant and outside assistants alike, so a governance team reviews one tool layer, not several integrations. Access is capped by the permissions people already hold, which keeps the AI inside the sharing model the organisation has already designed. Regional endpoints for the US, the EU and Australia are documented in the Claude setup article, which matters for data residency conversations. Smartsheet also says every AI feature operates within the same compliance framework as the rest of the platform, naming SOC 2, GDPR and data residency, which gives a UK buyer a starting point for its own UK GDPR assessment. The read and write split in the toolset design lets a cautious team allow questions and block edits with a few settings. And the cell history, for all the edges described above, is a real record, which is the kind of existing control our AI consulting work looks for before proposing anything new.

Smartsheet also wins on breadth of non-code work. Building a dashboard from a description, enriching rows with AI columns, summarising discussion threads and scheduling a Friday status digest are all things a programme office does every week. None of them is a task with a test suite, and forcing them into a proof template would add process without adding trust. For that work, the better investment is usually the test plan above, a firm Needs approval setting on write tools in every client, and a habit of asking the assistant to comment the reason on each row it changes.

Where Smartsheet is weaker is narrower than a feature list suggests: when a change needs to be shown correct, not just shown to have happened. That is the situation the next section describes.

When should a Smartsheet team look for a Smartsheet AI agents alternative?

A Smartsheet team should look for a Smartsheet AI agents alternative when agents start doing work whose correctness has to be proved, not just recorded: code changes, data extractions, test runs or deliverables that someone signs off. At that point a value-level history credited to a user is the wrong record, and a model that attaches evidence to the task before done is accepted fits better.

There are three common signals. The first is that the sheet has become a status board for work done elsewhere, for example an engineering backlog mirrored into a programme tracker, and agents are now doing some of that work. The second is that reviewers are re-doing agent work to check it, because nothing on the row says how the result was produced. The third is that an incident or audit question has already been answered with "the assistant changed it under my account", which is honest but not enough. Any one of them suggests the record model, not the tool, is the gap, a problem our engineering review queue guide looks at from the reviewer's side.

In those cases the answer is rarely to leave Smartsheet. Keep the portfolio where it is. Move the agent-executed tasks to a place where the claim of done carries proof, and let the programme tracker show the result. That is the shape we would propose for a developer productivity programme: engineering agents work tasks in a tracker built for them, and the PMO keeps its sheets. Our Cognibl explainer covers that split in more detail.

If you need that change run as a programme rather than a pilot, our AI execution office carries agent rollout through to an operating cadence with named owners.

What is the best Smartsheet AI agents alternative in 2026?

The best Smartsheet AI agents alternative in 2026 depends on what the agents do. For portfolio, project and operations data that lives in sheets, the best choice is usually to stay on Smartsheet and tighten client permissions and review habits. For agent work that must be proved finished, such as code or task-shaped deliverables, a tracker that refuses done without attached evidence, such as Cognibl, fits better.

The criteria behind that verdict are the ones this guide used. Where is the write approved, and is the setting consistent across every assistant your teams use? Can a reviewer list a batch of agent changes without checking cells one at a time? Does the record say which agent acted, or only which account? Is there evidence that the result is right, attached where the work is judged? And can the change be reversed through a tested route? Bring those five questions to a scoping call with our team if you want a second view. For sheet work, Smartsheet's documentation gives a clear account of where writes are approved and what cannot be reversed, and the test plan shows how far its record goes on the rest. Cognibl answers the third and fourth for task work, because the agent is named on the task and the proof sits where the work is judged. It claims no rollback of agent work beyond versioned proof and a person deciding whether to merge, and it does not attempt the sheet job at all.

There are other honest options. Wrike, Asana, Monday.com and ClickUp all now describe agents inside their own products, and each makes its own choices about approval and audit. A team that already runs Jira for engineering may find that a workflow validator covers part of the need, as our Jira comparison discusses. The right move for most organisations is a short paid pilot on one queue of real work, with the test plan above run on both candidates and the decision taken on the record each leaves, not on the demo. Our AI agent work management guide and our category of strategy posts are a sensible next read.

Frequently asked questions

Can Cognibl write to Smartsheet?

This guide makes no claim that Cognibl connects to Smartsheet, and Cognibl does not govern writes that Claude, ChatGPT or Copilot make to a sheet. Cognibl agents reach tools through a gateway where every toolset is off by default and connections are read-only except Google Workspace, where an admin can enable writes. Cognibl is a separate tracker for agent tasks, used alongside Smartsheet rather than inside it.

The practical pattern is to keep the portfolio in Smartsheet and run agent-executed tasks in Cognibl, with a person updating the programme tracker from the result. Our MCP skills library governance guide explains how Cognibl's deny by default gateway handles the tools it does connect.

Does Smartsheet label changes made by AI?

Smartsheet says every AI output is labelled and every AI action is traceable back to its source, and its developer documentation says cell history credits each change to the user who modified the cell. How the label appears for an edit made from an outside assistant through the MCP Server is worth testing in a sandbox sheet before relying on it in a review.

The test takes minutes: make one edit by hand and one through the connector, then compare both cells' history and any admin report your plan includes. Record what you find in your own guidance for assistant use.

Can an assistant's Smartsheet edit be undone?

Smartsheet's MCP documentation marks update_rows and delete_rows as destructive and irreversible, and states that updates are permanent and cannot be automatically undone. An assistant's edit can still be corrected by a further edit, using cell history to find the old value, but there is no automatic rollback, so a tested route back should exist before an assistant gets write access.

Setting write tools to Needs approval in Claude, and keeping a recent export of critical sheets, are two simple safeguards; Smartsheet's documentation itself advises verifying row IDs and cell values before an update runs.

Want to see the proof rule in practice? Read about Cognibl on cognibl.com, book an AI placement diagnostic, see our DATS methodology, or read about our approach to placing AI inside enterprise systems.

Written by the Dilr.ai engineering team, practitioners who ship enterprise AI in production. Follow us on LinkedIn for shipping notes, or subscribe via the RSS feed.