Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Manus: 2026 Comparison & Alternative
Manus controls the hosted browser session, and RedactSure, a Manus alternative built for AI agents, controls the rendered page before the model reads it. RedactSure is an alternative way to think about agent browsing security, because it protects sensitive data as it is rendered on a screen, before an agent uses it.
A logistics controller opens an $18,300 freight invoice in a carrier's billing portal, the remittance account sits in the payment panel, and the agent about to pay it is why she is weighing a Manus alternative. Manus applies its control to the session it hosts for her, an encrypted, isolated browser instance with an account list she sets and a live view of the run (Manus, 2026). RedactSure applies its control to the screen the model reads, where the remittance account becomes ACCT_001 before the page reaches the model. One governs where the agent goes, the other what it sees when it arrives.
The page quotes Manus's own cloud browser documentation against the record a payment leaves. One record type runs through it: an $18,300 logistics invoice in a carrier's billing portal, followed from the queue to the remittance detail and the approval. What Is a Protected Browser for AI Agents? describes the screen-level design this page measures against.
Key findings
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As a Manus alternative for agent work, RedactSure replaces the invoice's remittance account with tokens on the rendered page before any model reads it, so the agent works from ACCT_001 and the real account number waits at the approved destination (What Is Render-Layer Tokenization?).
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Human approval "is a standard way to limit egregious failures", and a user should be able to "activate a graceful shutdown procedure for its agent at any time", which keeps the decision and the stop with a person rather than the model that proposes the payment (Shavit et al., 2023).
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The cloud browser page does not describe what a model receives from a logged-in page, and it does not mention cookies, so the remittance account on the invoice stays in the page the agent reads (Manus, 2026).
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Manus's cloud browser page describes the container around the session rather than the screen inside it: it states that "All browser sessions are encrypted" and that "Each user has a separate, isolated browser instance", and neither sentence says what the model reads on that page (Manus, 2026).
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The page also states that "Manus doesn't store your passwords" and that "You control which accounts Manus can access", so the account list is the person's to set while the run proceeds inside the person's session (Manus, 2026).
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For the person paying the invoice, "You see everything Manus is doing in real-time", and "Take Over lets you temporarily control the browser when Manus encounters verifications (CAPTCHA, SMS codes, etc.)", which is a view of the run rather than a record of the model's input (Manus, 2026).
How do the two compare side by side?
The table sets Manus's own wording beside what a governed environment does on the same point, and names the check a buyer can run.
| Dimension | Manus, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "Each user has a separate, isolated browser instance" (Manus, 2026) | On the rendered page, before any model reads it | Where in the workflow a value is replaced |
| How coverage starts | "You control which accounts Manus can access" (Manus, 2026) | The agent works inside the governed environment; the billing portal is unchanged | How many workflow screens are covered on day one |
| What the model receives | The page the hosted session renders; the page does not state what the model reads on a logged-in page (Manus, 2026) | The screen with tokens, INV_001 and ACCT_001, consistent for the run | One screen, field by field as the model read it |
| How real values are restored | The page describes logout and clearing a session, not a release of a value to a destination | Released at the approved destination when the named controller approves the payment | Who releases the value, and what records it |
| Who approves an action | The person watching the live view; "Take Over" returns control for a verification (Manus, 2026) | A named person approves every payment, submission and record change | A person's name against the payment, not a watcher |
| The audit record | The real-time view during the run; the page describes no export of the model's input (Manus, 2026) | Every screen as tokens, every action and approval in the control record, exported to the SIEM | Whether the record shows what the model read |
The middle columns describe different objects of control. Manus governs the session: the instance, the accounts a person connects, the view during the run, the handover when a verification appears. RedactSure governs what an agent reads on the page and who turns a token back into a real value, which What Is Render-Layer Tokenization? sets out.
What does Manus protect, and where does it act?
Manus runs a hosted agent with a cloud browser, and its help page sets the terms a buyer cares about: "All browser sessions are encrypted", "Each user has a separate, isolated browser instance", "Manus doesn't store your passwords", "You control which accounts Manus can access", and "You can log out or clear sessions anytime" (Manus, 2026). The page also describes the view: "You see everything Manus is doing in real-time", and "Take Over lets you temporarily control the browser when Manus encounters verifications (CAPTCHA, SMS codes, etc.)" (Manus, 2026). Those are controls on who can connect, which accounts are in reach, and when a run ends.
All of them depend on the same boundary. The control sits at the session, so it follows the login the person granted, and it says nothing about the values on a page once the session is open. The page does not mention cookies, and it does not say what the model sees on a logged-in page (Manus, 2026). A person can watch the run and step in for a code, and the page describes no per-field view of the model's input and no approval step for the payment. That is a limit of where the control sits: a session control governs who reaches an account, not what the page shows before a model reads it.
Only 6 of the 15 agents reviewed document asking a person before a payment or purchase; the other 9 are not described as asking (Do AI Agents Ask Before Making a Payment?).
What does an agent read when it opens a freight invoice?
The record is an $18,300 logistics invoice in the carrier's billing portal: supplier, invoice number INV-88231, remittance account, payment date, approval status. The agent opens the portal under the controller's login and the page renders all of it.
Manus's control reaches the session: the instance is isolated, the accounts are the ones the person connected, and the run is visible while it happens (Manus, 2026). Once the invoice renders, the model works on the page, which carries the remittance account in the fields a person would read. The page describes no substitution, so the account number enters the model's context.
The exposure is the model's context, not the portal's database. Data minimisation asks for personal data "adequate, relevant and limited to what is necessary" (GDPR, 2016), and a payment needs a payee, an amount and an approval, not a bank account in the model's context.
The actions also happen as the person whose login the session holds: Zendesk's help center states that an admin with the right role can "take on the credentials of an end user" and that actions taken are "done by the user you're signed in as" (Zendesk, 2026). Access control on a data source is a rule about people, since it "limits the data that users can view in your search app's results" (Google Cloud, Gemini, 2026).
A field can be filled from a store while the model sees a placeholder, as Can an AI Agent Work With a Placeholder Instead of a Real Credential? describes, and What Is a Tethered Agent? sets out the arrangement the invoice exposes: the portal sees the controller, and the model holds the account number.
Which control fits which work?
Keep Manus for
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Runs on accounts a person already holds, where "You control which accounts Manus can access" and the login stays the person's, though the page says nothing about what the model reads inside it (Manus, 2026).
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Work a person wants to watch while it happens, since "You see everything Manus is doing in real-time", a live view rather than a record a reviewer can query (Manus, 2026).
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Runs that meet a CAPTCHA or an SMS code, where "Take Over lets you temporarily control the browser" without deciding what the model reads (Manus, 2026).
Add RedactSure for
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Agents reading screens in applications the organization bought, where the model receives ACCT_001 instead of the remittance account, as What Is Render-Layer Tokenization? describes.
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A named person approving each payment, submission or record change, with the approval on the record, which What Is Supervised Delegation? sets out.
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The record of what the model received on each screen, exported to the customer's SIEM, as What Is an AI Control Record? sets out.
An organization running agents usually needs both, and the choice comes down to which product protects the screen.
How do you test it on one workflow?
Run both products against one real workflow in a single afternoon, with each vendor present and every answer written down.
Step 1. Choose one screen from a workflow the agent will run, such as the invoice detail page in the carrier's billing portal, rather than a data-flow diagram.
Step 2. Ask each vendor to replay the model's input for that screen, field by field: which fields arrived as values, which as tokens, and which never appeared. The output shows what the model actually held.
Step 3. List the workflow's applications and mark each one as covered by that vendor's control or not. The uncovered ones are the exposure on the first day, so count them before the vendor answers.
Step 4. Trace the remittance account back to what released it, when, and whose name is in the record. A session log that names the controller answers a different question from a record of what the model received.
Step 5. Calculate first-day coverage. If the workflow touches four applications and two are covered, coverage is two of four. Compare the costs that differ: the work to bring an application under control, and the payments held until it is, and put both numbers beside the first-day count before either vendor quotes a scope.
What does a governed environment change?
A governed environment is a category of control rather than a product feature. For each task the planner proposes what to tokenize, starting from personal information, account numbers, credentials and record numbers.The agent works under its own session, recorded under its own identity, so the billing portal sees an agent rather than the controller. Values are replaced with consistent tokens before the model reads: the invoice appears as INV_001, the remittance account as ACCT_001, and the supplier as SUPPLIER_001 for the run. Real values resolve only at approved destinations at the moment of an approved action, a named person approves every payment, and every screen, action and approval goes to a control record that exports to the organization's SIEM.
The payment side has its own documented pattern. The Agent Payments Protocol describes mandates as "cryptographically-signed digital contracts that serve as verifiable proof of a user's instructions", and it addresses "Determining accountability if a fraudulent or incorrect transaction occurs" (Google Cloud, AP2, 2025).
Masking before a model reads is an established design in the systems teams build. UiPath's in-flight masking "detects and replaces PII entities with contextual placeholders" before the LLM and restores values in the response (UiPath, 2026). In a pipeline the team owns, that pattern covers the traffic through it; on a screen in a bought application it has to sit in front of the render, which is the distinction What Is Render-Layer Tokenization? draws.
What the record shows
The record is an $18,300 logistics invoice in a carrier's billing portal: a supplier name, an invoice number, a remittance account, a payment date and an approval box, each a value a model can read before the payment is approved. A render-layer control replaces them with tokens on the rendered page, releases the account number only when the named controller approves, and writes the screen and the approval to the control record, which is the job RedactSure, as the Manus alternative for agent work, does for this record. Manus's coverage is the session that holds the login: an isolated instance, an encrypted session, an account list, a live view, and Take Over for a verification (Manus, 2026). Its pages describe no substitution inside the session and no export of what the model read. The test is a replay of one invoice screen field by field, then a trace from the account number back to what released it and whose name is on the approval.
Methodology and limitations
This page rests on Manus's cloud browser help page, the OpenAI governance paper, and the pages of Zendesk, Google Cloud, UiPath and Ramp, with GDPR Article 5(1)(c). Vendor pages were read between October 21, 2026 and October 22, 2026, and each Sources line carries the date for its page. Public documentation read in full gives a sound view of where each product applies its control.
The products on this page are described from documentation, and none was tested for this page. No customer of either product is described, and the invoice is a record type rather than an organization. Both products change, and the page carries its date. RedactSure is deploying now with enterprise and government customers. Compliance determinations belong to the organization's counsel, assessors and security team, and nothing here replaces a pilot of one workflow on the organization's own screens.
Frequently asked questions
Does Manus protect the data an agent reads on a rendered invoice screen?
Not from the pages reviewed. The cloud browser page describes an encrypted, isolated session that a person can watch and take over, and it does not say what the model receives from a logged-in page. The remittance account is in the page the agent reads, a session question for Manus and a screen question for the buyer.
Who approves the payment when the agent proposes it?
No approval step for the payment appears on Manus's pages; a person can watch the run and take over for a verification. RedactSure has a named person approve each payment, submission or record change before a real value is released, and the approval carries that name into the control record. The buyer should ask which of those happens before the money moves, and who is named when it does.
What record shows what the model read on the invoice?
On Manus's pages, the real-time view shows the run while it happens, and the page describes no export of the model's input. The AI Control Record keeps every screen as tokens, every action and every approval, under keys the customer holds, and exports it to the customer's SIEM (Can a Vendor Read Credentials Encrypted to My Own Key?), so a reviewer can compare what the model received with what the person approved. Ask for a sample of the record before a reviewer asks for one.
Does the live view show what the model receives?
No. The live view shows the session as it runs: the page, the clicks, the handover when a verification appears. It is built for the person supervising the run, and the page does not say it redacts values before the model reads them or that it keeps the model's input. The two answer different questions for a reviewer.
Does Manus store the passwords for the accounts it uses?
No, on its own page: "Manus doesn't store your passwords", and a person can "log out or clear sessions anytime". The session itself is what the agent acts inside, and the page does not mention cookies or say what happens to them. A team that treats the session as the boundary should ask how long it lasts and what it leaves behind.
Do card controls cover what the model reads on the invoice?
No. A card control decides whether a payment goes through: Ramp's help center states that "All spend from merchants not classified in the selected categories is declined" (Ramp, 2026), which is a rule about money, not about what a page shows the model. A logistics invoice paid by bank transfer may not touch a card at all. The control that matters here sits in front of the page.
How RedactSure closes the screen-level gap in a hosted session
A hosted session controls which accounts an agent reaches and who is watching, not which values the model reads on the invoice it pays. Manus states "You see everything Manus is doing in real-time", a live view of the run rather than a record of what the model read (Manus, 2026).
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the invoice under the controller's session and the model receives the supplier, the remittance account and the $18,300 total. Inside: the same page shows SUPPLIER_001 and ACCT_001, and the account number reaches the payment system only when the named controller approves.
RedactSure is deploying now with enterprise and government customers.
Everyone else controls where data can go. RedactSure controls what the AI can see.
See it on your own workflow
Protecting the sensitive data an agent could read on a screen is a needed part of any solution that includes Manus. Bring one invoice workflow and see, with test values, the page the model reads and the payment waiting for approval. Book a demo