마침내, 행동이 현실이 됩니다
당신의 손끝에서 시작됩니다
당신이 지시를 내리면, act-1은 계획하고, 클릭하고, 작업을 완수합니다.
마치 사람 비서처럼 말이죠.
당신이 지시를 내리면, act-1은 계획하고, 클릭하고, 작업을 완수합니다. 마치 사람 비서처럼 말이죠.
ACT-1 Use Case
What sets ACT-1 apart
Learn from Mistakes
There is no one-size-fits-all solution complex objectives demand continuous refinement. ACT-1 learns through trial and error, examining outcomes and adjusting its strategy in response. With every iteration, the agent sharpens its policy, delivering faster and more reliable results.
Learn from Humans
We teach, ACT-1 learns through imitation learning it masters complex tasks. Because natural-language commands can be ambiguous, a human demonstrates the workflow while the agent observes, capturing both actions and context. It then generalizes this insight to new, related tasks instead of merely replaying clicks.
Builds Domain Knowledge
Even proficient users must first explore site-specific knowledge finding where promotion info lives or which control triggers a unique feature. As it searches, explores, and discovers, ACT-1 captures these details in a structured domain model, then reloads it on future visits to bypass rediscovery and execute tasks faster and more robustly. As its cache of schemas grows, the agent reuses this prior knowledge to tackle new but similarly structured sites.
True Scalability
After each run ACT-1 logs a fine-grained action graph it can execute instantly, without rerunning the full decision pipeline. A live monitor checks the current DOM against this graph and, if the page has shifted, triggers on-the-fly re-planning. This replay-and-react loop drives thousands of repetitive requests in parallel with sub-second latency, keeping the marginal cost of each new task near zero while remaining resilient to site changes.
