UPX AI Agent
Your 24/7 Code Reviewer for AI-generated
projects
You ship fast with Cursor, Claude Code, Lovable or Replit. The agent scans what went in, fixes what's exposed and hands back a locked-down app.
+1,400 integrations available
You define how far the agent goes on its own.
You choose the level per task type. Increase autonomy when you trust the results, and dial it back at any time without stopping what is already running.
Auditor
It analyzes and comments
The agent reads the repository and the pull requests your AI tools open, and returns its analysis as comments. It doesn't create branches, write files or open pull requests. It lets the team measure its accuracy against their own review, and find out how much of what's already going into the repo would pass this bar.
Advisor
It proposes as a draft
The agent creates a branch and opens a draft pull request, with the change, the rationale and the test results. Review and the merge request stay with the team.
Executor with approval
It executes after approval
Approval covers that task, not the repository policy. Once the proposal is approved, the agent applies the change, follows the pipeline, fixes lint and test failures and updates the ticket.
Autonomous executor
It fixes and logs
For low-risk fix classes you define, secrets out of the bundle, security headers, rate limiting, dependencies with known CVEs, input validation, the agent fixes it on a branch, runs the tests and logs before and after.
Integrates with your repository, your CI and your pipeline.
Works no matter which tool generated the code: the review happens on the pull request. The agent runs on top of what already exists, via API, MCP or webhook, always with least privilege and a full trail.
AI coding tools
Repository and CI
Application security
Tickets and planning
Observability
We don't replace your pipeline or your coding tool.
Your repository is a critical asset, and access to it is treated as such
Code does not become training data
Your repository is not used to train models, nor shared across customers.
Repo content is not a command
Instructions hidden in an issue, README, comment or MCP tool description are treated as content to analyze, never as an order to execute. What the agent can do comes from its configuration.
No production access
Secrets, credentials and customer data are out of scope. The agent works in a development environment.
Isolation
A dedicated environment per customer, with segregated network and credentials.
Least privilege
A token per task and per repository, with permissions limited to what that flow requires.
Retention
You define how long context and history are stored, and delete them whenever you want.
It works where your team already talks.
No new portal to learn. You ask, follow along and approve in the app that is already open. Scope, limits and audit trail are the same in every interface.
CLI and pull request
The agent comments where the review already happens. Approval by command or through the repository's own review.
Slack and Teams
The agent replies in the team's thread, flags what needs attention and takes approvals in one click.
Discord
For teams that already operate there. Commands, alerts and approvals inside the squad's channel.
Web console
Where you configure the rules, set the autonomy level per task, review the audit trail and browse the full history.
Switching interfaces does not switch the rules. Same scope, same limits, same log in all of them.
Join the waitlist and be notified at launch.
Leave your contact details. When the agent is available, UPX will reach out.
Frequently asked questions
Common questions about the Code Review Agent
What engineering teams usually ask before putting an agent to review code.
Who has access to what the agent processes?
Access is restricted to the UPX operations teams assigned to your contract, with an audit log of every access. UPX runs its own SOC, with no outsourcing. Whoever accesses your operation is accountable for it.Who operates the Secure AI Agent after deployment?
Operation is run by UPX's own SOC, 24/7, under the Managed Operation model. Nothing is handed off to third parties. Whoever operates is accountable.Does the agent act on its own or is there human oversight?
You define the level of autonomy. The agent can execute actions within the agreed limits, and higher-impact actions go through validation by the operations team before execution.How do I set the limits of what the agent can and cannot do?
Limits are defined in the agent's configuration: which actions it executes, which require validation and which are out of scope. These limits are operating rules, not suggestions.Can I audit the agent's decisions and actions?
Yes. Every action the agent takes is logged, with an audit trail available for review. You have visibility into what was executed, when and under which criteria.How is it different from a generic AI assistant?
A generic assistant answers questions. The Secure AI Agent operates inside your infrastructure, under defined security rules, with UPX's own SOC behind it. The difference is not the AI model, it is who operates and answers for it: over 20 years in critical infrastructure, with security as the rule, not a module.Do you replace Cursor, Copilot or Claude Code?
No. They generate, the agent reviews. It works inside the pull request, no matter which tool wrote the code.Does our code train a model?
No. Your repository does not train a model and is not shared across customers.Do I need to know how to code?
In the self-managed model, no: configuration happens through the interface. In the managed model, development and integrations are handled by UPX.Who defines what the agent is allowed to do?
The company, always. Tasks, access, limits and human approval points are set when the agent is designed — by you, through the interface, or with UPX's guidance in the managed model.Do I need to replace the systems the company already uses?
No. The agent works on top of the systems already part of the operation, based on the feasibility of each integration.How long does it take to go live?
In the self-managed model, it depends on how long your team takes to connect the tools and configure the rules. In the managed model, the timeline is defined after the diagnostic, based on scope, integrations and level of customization.Can I create my own skills?
Yes. You can combine your own skills with those in UPX's library. In the managed model, UPX also develops specific skills for the project.How will we know if it worked?
Metrics are defined before go-live and compared against the initial scenario — execution capacity, time spent on the process and quality of the output. In the managed model, this definition is part of the diagnostic.Who operates the agent after it goes into production?
Configuration, rules and usage remain under the company's control in both models. In the managed model, UPX monitors performance and refines the agent based on usage and the limits set.What if the agent stops working?
UPX maintains the platform's infrastructure and handles technical outages. Configuration, rules and usage remain under the customer's control.Can we start with just one agent?
Yes, and that's the recommendation. Start with a process with clear impact and expand from the result.How many agents can I have?
As many as you need. Each agent is contracted individually and operates in its own environment.
Companies that trust UPX
