Why the Focus Has Shifted from AI Agents to Agentic Workflows

We are currently on a technological evolution ladder—starting from large language models (LLMs) and moving toward AI agents that can simulate human digital interactions. However, in commercial deployment the industry focus has shifted from AI agents to agentic workflows and data synthesis.

Why has the industry temporarily moved away from AI agents? Companies such as Salesforce and ServiceNow heavily bet on AI agents, but existing technology accuracy is far from commercial standards. Anthropic’s latest AI Agent Computing Interface (ACI) shows performance at only 14% of human level. Research from TheAgentFactory illustrates that current AI agents achieve low success rates (around 20%) and high costs across execution steps.

OpenAI’s new Agent Operator framework improves accuracy on computer operation and web‑browsing tasks to 30‑50%, still well below the human benchmark of over 70%.

Agentic workflows are gaining attention because knowledge‑type work is inefficient; studies show professionals spend about 30% of their time on information retrieval and struggle with multi‑document integration. Agentic workflows decompose complex tasks into subtasks and automate reasoning through task chains, enabling precise data synthesis.

Modern AI models now embed reasoning as a core capability, breaking down complex problems into manageable components. This approach makes the reasoning process transparent, with clear generation paths for each conclusion and coherent decision logic, ultimately delivering outputs that are easier for users to interpret.

Conclusion : Enterprises must shift their focus from chasing specific tools or trends and instead address real business challenges. True innovation is measured not by mastering the latest technology but by applying advances to create tangible value—enhancing customer experience, optimizing operations, and solving societal needs.