Agentic AI
Description
Agentic AI represents the next evolution of intelligent automation. It moves beyond static prediction or generation into continuous decision-making and goal-oriented behavior.
An agentic AI system typically follows a four-stage cycle: Perceiving – gathering and interpreting contextual data; Reasoning – using large language model orchestration to plan actions; Acting – executing tasks via connected tools, APIs or workflows; and Learning – improving through feedback and results analysis.
This iterative loop enables AI agents to handle multi-step tasks such as scheduling, content generation or customer engagement without requiring constant human prompts. Where generative AI creates, agentic AI decides – choosing what to do next, why and how.
The distinction between generative and agentic AI lies in the capacity for independent action. Generative AI excels at creation – producing text, code or images based on a specific prompt – but it waits for human input to function. Agentic AI, by contrast, is designed for execution. It breaks down a high-level goal (e.g. "optimize the supply chain") into a sequence of necessary steps, executes them across different software platforms and adjusts its approach based on real-time data. This shift from "tool" to "teammate" allows enterprises to automate complex, open-ended workflows that previously required constant human supervision.
Example use cases
- Customer experience: Powering intelligent self-service and digital-human interactions that adapt to user intent.
- Content operations: Generating, localizing and publishing marketing or product content autonomously across channels.
- Software engineering: Automating code generation, testing and deployment to boost developer productivity.
- Healthcare and life sciences: Analyzing data, summarizing insights and assisting clinicians in decision support.
- Video analytics: Monitoring and interpreting visual data from sensors or cameras for anomaly detection and quality control.
Key benefits
RWS perspective
At RWS, we see agentic AI as the natural progression of intelligent technology – automation that acts with initiative, guided by human intelligence.
Our AI ecosystem connects TrainAI for curated data, Language Weaver for multilingual communication and Human-in-the-loop frameworks for governance and refinement. This combination ensures agentic systems remain accurate, transparent and culturally aware as they scale. Agentic AI can plan, act and adapt – but it still relies on people to define purpose, ethics and context. By embedding human insight into every layer of AI orchestration, RWS helps enterprises deploy autonomous agents that perform with accountability and empathy.