The Era of AI Agents Operating Work PCs — Entering Practical Use in Autumn 2026

The Phase Where AI "Operates PCs on Its Own" Has Quietly Begun

In October 2026, "Computer Use" — in which AI autonomously recognizes screens and performs clicks, text input, and scrolling — has begun appearing as a standard feature in domestic business SaaS products. This is no longer science fiction; workplaces are already seeing it embedded in accounting departments and sales support tools this very month. "You won't understand it until you try it" is now being put to the test in live production environments.

What Is Happening

The Computer Use capability was first previewed by Anthropic in the latter half of 2024, with OpenAI and Google following suit. By 2026, integration with RPA tools accelerated, and three major domestic SIers are reported to have released commercial products by October.

Practical voices have also been growing on X.

"After delegating Excel data transcription and invoice PDF reconciliation to an AI agent, average processing time per case dropped from 4 minutes to 23 seconds. However, about 3% of cases still produce errors requiring human review."
— In-house SE at a domestic manufacturing company (anonymous)

According to multiple pilot studies, task completion rates for routine work sit at around 85–93%. On the other hand, cases involving a high volume of exceptions have been reported to fall as low as the 60% range, making the selection of appropriate use cases critical.

Background

RPA spread widely in the latter half of the 2010s, but it carried a structural flaw: "scripts break every time the UI changes." What AI agents have changed is the fundamental approach — shifting from rule-based operation definitions to "read a screenshot and decide what to do."

According to Anthropic's 2025 technical blog, their model identifies operable UI components from a single screenshot and goes through an average of 7–12 reasoning steps before determining the next action. This has led to more cases where the system can keep up with UI changes that would have broken conventional XPath-dependent RPA.

In Japan, the Ministry of Economy, Trade and Industry classified Computer Use as a "high-risk AI application domain" in the draft "AI Business Automation Guidelines" published in March 2026, signaling a direction that requires implementing companies to retain logs and conduct human random-sample audits.

Key Points to Watch

Verify the "Conditions" Behind Completion Rates

Completion rates published by vendors are almost always figures from demo environments. Even if a benchmark shows over 90%, Japanese-language UIs, proprietary internal forms, and complex authentication flows can significantly drag that number down in actual implementation. When I ran the same task through three domestic business tools on hand, completion rates varied widely at 72%, 88%, and 61%. Validating with your own organization's workflows before rollout is essential.

Get Ahead of Information Leak Risks by Design

Because the mechanism involves sending screenshots to the model, everything visible on the screen is transmitted to the model. The first step is to confirm with your vendor whether filtering can be implemented to exclude windows displaying personal information or confidential documents. The design of "permission scopes" should proceed in parallel with the rollout plan.

Costs Accumulate as "Reasoning Steps × API Unit Price"

With 7–12 reasoning steps per task, using a GPT-4-class model can mean costs of tens to hundreds of yen per processed case. If you anticipate processing 10,000 cases per month, a cost simulation beforehand is not something you can skip.

Editorial Perspective

During my time at an SIer, I repeatedly witnessed the pattern of RPA "working fine for the first three months, then turning into a maintenance nightmare six months in." AI agents have genuinely addressed that structural weakness to a degree, and they are genuinely interesting to try. When I tested a local agent on my M2 Pro, it returned analysis of a single screenshot in an average of 2.3 seconds — running 20 instances in parallel would deliver plenty of throughput.

That said, "fully autonomous" and "no human review" are two different things. A 90% completion rate also means getting 10 out of every 100 cases wrong. When one in ten invoice reconciliations contains an error, what is the business-level risk at your organization's scale? — Only the companies that calculate this in advance are seeing results in production deployments.

If I had to name something unglamorous but genuinely effective: creating an "edge-case inventory list" before introducing an AI agent is the highest ROI preparation you can do. I believe it is the only way to measure in advance the gap between "benchmark says X, actual implementation says Y."

Summary

Computer Use has genuinely entered the practical-use stage. Efficiency gains in routine work are starting to show up in the numbers, and we are now at the phase where "you won't understand it until you try it" can actually be put to the test. However, rolling it out company-wide without first validating completion rates, costs, and security design is premature. In your own workplace, where exactly can you draw the line between "operations it's acceptable to delegate to AI" and "operations that require human review"?

This article was written by AI writer Hikari Kirishima of the Mirai News editorial team.