AI agents that operate your existing software

Traditional RPA (UiPath, Automation Anywhere) relies on brittle selectors — hardcoded element IDs, pixel coordinates, and rigid scripts that break when the UI updates. Computer use agents see the screen the way a human does: they read labels, understand layout context, and adapt to interface changes. More importantly, RPA automates clicks. Computer use agents make decisions — they can handle exceptions, navigate unexpected dialogs, and escalate to a human when confidence is low. It's the difference between a macro and an intelligent system.

Every agent runs in an isolated, sandboxed environment — a containerised desktop that cannot reach your network, your files, or any system beyond what is explicitly configured. Credentials are stored in per-task vaults with least-privilege scoping: an agent that processes invoices gets access to the accounting software and nothing else. Human-in-the-loop approval gates pause execution before any high-stakes action — sending an email, modifying a record, initiating a payment. Every screen the agent sees and every action it takes is recorded in an immutable audit trail. Whether the agent operates a financial platform or a government portal, the security architecture is the same.

Any software with a graphical interface — including legacy ERPs, medical records systems, proprietary accounting platforms, HR tools, government portals, and industry-specific applications that have no API and no integration layer. The agents also handle browser-based workflows (form filling, data extraction, web research), email automation (reading, triaging, drafting replies with approval gates), and cross-system workflows where data must move between multiple disconnected applications. If a human can operate it through a screen, an agent can too. For systems that do have APIs, API-based automation is more efficient — we help you choose the right approach.

Platform-level computer use agents (ChatGPT Agent Mode, Claude Computer Use) achieve 14–38% success rates on complex desktop tasks in benchmarks. Production-grade reliability requires engineering around the agent: structured error handling, retry logic with exponential backoff, fallback paths for edge cases, confidence thresholds that trigger human escalation, and monitoring dashboards that alert on anomalies. The agent is one component — the reliability comes from the system architecture. The same production engineering discipline applies to automated research systems.

The EU AI Act's high-risk system requirements take effect August 2, 2026. Article 14 mandates human oversight, Article 12 requires comprehensive audit trails, and penalties reach 35 million EUR or 7% of global turnover. Computer use agents are built with compliance as a structural feature: human approval gates satisfy Art. 14, immutable action logs satisfy Art. 12, and sandboxed execution with least-privilege access satisfies risk management requirements under Art. 9. These are the same governance patterns used in AI workflow automation — compliance is a byproduct of building it right.