Baidu introduced DuMateBench, a leaderboard that judges AI agents on whether they finish real office tasks and hand back usable output, spanning 200-plus tasks across six categories and grading task comprehension, tool use, sustained execution, and final-result quality.
The strategic signal matters more than the leaderboard itself. The industry is quietly retiring the accuracy-on-questions paradigm that defined the chatbot era and replacing it with a delivery paradigm: did the agent produce a report, a populated spreadsheet, a completed workflow that a human can actually use. That reframing is where enterprise value lives. It also raises the bar—continuous execution and tool orchestration expose failure modes that single-turn Q&A benchmarks never surfaced, which is precisely why vendor-published leaderboards deserve skepticism until independent replication exists. Baidu pairing this with Dazi, positioned as its fastest-growing desktop work agent under an application-driven ERNIE strategy, shows the playbook: own the evaluation narrative, then funnel it into a distribution product. Google, Microsoft, and OpenAI are running variations of the same loop, so expect a proliferation of 'agent does real work' benchmarks, each tuned to flatter its author.
For Japanese enterprises, the shift from answers to outcomes lands directly on the RPA installed base. A generation of Japanese back-office automation was built on brittle, rule-based scripts that break when a screen layout changes. Outcome-capable agents threaten to absorb that layer, and the pressure will be strongest in exactly the document-heavy, form-driven workflows—expense processing, procurement, internal reporting—where Japanese offices concentrate manual effort.
For SIers, this is both risk and opening. The margin no longer sits in wiring together deterministic bots; it moves to integration, evaluation, and governance—defining what 'usable output' means for a given client, building the guardrails, and standing behind the results contractually. That demands an internal evaluation discipline most integrators have not yet built. The firms that develop Japanese-language, domain-specific benchmarks for finance, manufacturing, and public-sector work will control the trust layer clients pay for. The ones that keep reselling generic agent licenses will find themselves competing on price against the model vendors' own products.
One caution for local decision-makers: a China-origin agent stack carries procurement and data-residency questions that will shape adoption regardless of raw capability. The durable lesson is the methodology, not the vendor—insist that any agent pilot be measured on delivered, verifiable work, and treat every self-published leaderboard as marketing until proven otherwise.
