Agentic AI: The latest frontier in artificial intelligence

Nayna Jaen Nayna Jaen Marketing Director, Enterprise Services & TrainAI, RWS 10 Apr 2025 6 mins 6 mins
Conceptual image of a digital agent organising lots of different content and tasks.
Starting in late 2022 and throughout 2023, large language models (LLMs) like OpenAI’s ChatGPT, Anthropic’s Claude and Google Gemini ushered in a new era of prompt-based natural language processing (NLP) and human-like text generation. The evolution continued in 2024 with the emergence of multimodal AI assistants that could perform specific tasks like answering queries or scheduling appointments. And now, in 2025, we stand at the cusp of the age of agentic AI systems, which have the ability to proactively reason and make decisions on our behalf across domains.

What is agentic AI?

Put simply, AI agents can work in isolation or with other AI agents to autonomously take actions to achieve specific goals, rather than just passively responding to user inputs. They demonstrate a degree of initiative, decision-making and long-term planning. There are several scenarios where AI agents go far beyond LLMs – here’s an example that illustrates the differences in a marketing context.

Goal: Write, publish and promote an article on social media about harnessing AI to improve customer service
Task

Early LLM 

e.g. OpenAI's GPT-3 (2020)

Multimodal AI Assistant or AI Workflow 

e.g. Google Gemini (2023)

AI Agent 

e.g. OpenAI Operator (2025)

Write the article Generates text based on prompt (e.g., "Write an article about harnessing AI to improve customer service"). Drafts content and suggests improvements based on templates and guidelines. Writes, refines, and adapts the article autonomously based on trends, past data, and tone preferences.
Fact-check Cannot autonomously verify facts. Can cross-check facts by pulling information from various sources or by using techniques such as Retrieval-Augmented Intelligence (RAG) and flags uncertainties for human verification. Actively verifies facts using multiple sources and ensures accuracy before proceeding.
Optimize for SEO Can suggest general tips for SEO but cannot implement them. Suggests SEO optimizations, adds keywords, and formats the article accordingly. Automatically optimizes content for SEO, implements best practices, and adapts for specific platforms.
Add images and/or media Cannot generate images or media. Suggests or generates images and media (e.g., graphs, images) for the article. Generates, selects, and formats images or media, optimizing them for the article’s goals.
Format and publish Provides a text draft but requires manual formatting and publishing. Formats the article according to platform standards and prepares for publication (e.g., WordPress, LinkedIn). Formats and publishes the article autonomously, scheduling posts and setting up workflows.
Promote on social media Cannot promote content – requires manual sharing. Can suggest promotional strategies and draft social media posts but requires human action to post. Automatically schedules and posts the article on social media, and tracks engagement.
Track and optimize engagement No ability to track or optimize engagement. Can suggest engagement strategies but needs human input for optimization. Tracks and analyzes engagement, adjusts strategies (e.g., timing, content) based on performance data.
Level of human involvement High – a human must edit, publish, and promote. Moderate – a human must still approve content, but much of the work is automated. Low – AI handles the entire process from start to finish, requiring minimal human intervention.

What would a future powered by AI agents look like?

In a future powered by agentic AI, intelligent AI agents will seamlessly integrate into our daily lives, working alongside humans to complete tasks of various complexities, from automating routine, repetitive tasks to independently managing intricate workflows at work and at home. While humans will set objectives and provide context, AI will interpret that context, devise a plan, and execute necessary actions to accomplish the defined objectives – all while dynamically adapting to changing environments.
 
With human oversight, specialized AI agents will collaborate, working together to tackle even the most complex of challenges. Thanks to their advanced reasoning capabilities, AI agents may one day even be able to anticipate human needs – further boosting our productivity. As they become more skilled, they will handle data-intensive, repetitive work, freeing humans to focus on strategic, creative, and high-value decision-making. By continuously learning and improving, AI agents will help us achieve goals more effectively and efficiently, while operating within the ethical boundaries of responsible AI.

Enterprise use cases for agentic AI

The potential industry-specific use cases for agentic AI are limitless.
 
If we look at finance, agentic AI can be implemented to optimize trading strategies and detect fraud in real time. For example, hedge funds can leverage AI agents in high-frequency trading systems to analyze market trends and execute trades at unprecedented speeds.
 
In healthcare, AI agents can help perform diagnostics (by using AI diagnostic tools, such as IBM Watson Health or Google’s DeepMind for medical imaging), analyze medical results, develop treatment plans, and improve hospital operational efficiency.
 
In the legal sector, AI agents can review contracts, identify potential risks, suggest revisions, and even assist in contract negotiations. Automating such time-consuming processes will enable legal professionals to focus on higher-value tasks like client consultations.
 
Beyond industry-specific use cases, agentic AI can also drive efficiencies in critical, cross-industry functions. For example, in customer service, AI agents can deliver personalized, multi-channel experiences by autonomously resolving common customer issues and escalating complex cases to human representatives. Looking at marketing, AI can automate market research, content creation and campaign analysis, enabling marketers to make faster, data-driven decisions and optimize marketing strategies. And in supply chain management, AI agents can autonomously perform logistics planning to ensure that inventory is continuously replenished.

Ethical and security challenges to consider

As AI systems become more autonomous, their ability to make independent decisions raises serious ethical concerns. Without proper oversight, agentic AI can reinforce biases present in flawed training data or make choices that conflict with human interests. They can also contribute to job displacement as tasks previously performed by humans increasingly are performed by AI agents.
 
From a security perspective, their autonomous nature makes AI agents prime targets for cyber threats, adversarial attacks, and manipulation by malicious actors. The widespread adoption of agentic AI across industries heightens risks such as data breaches and misuse of sensitive information.
 
To mitigate these challenges, it’s essential to align agentic AI with robust security measures, ethical guidelines, and regulatory frameworks.

How organizations can prepare for agentic AI

There are a few ways organizations can prepare for agentic AI starting today:
  • Develop clear AI governance frameworks: Establish clear ethical guidelines, risk assessment procedures, and responsible AI development practices that keep humans in the loop to ensure proper oversight.
  • Deploy explainable AI (XAI): Ensure agentic AI systems are developed with full transparency so that humans can fully understand and control their decisions.
  • Establish robust security measures: Implement stringent security protocols that include adversarial testing and anomaly detection to protect agentic AI systems from exploitation.
  • Upskill employees: Invest in AI employee education to prepare them for a rapidly evolving job market, and equip them to work with and manage agentic AI systems.
  • Keep humans in the loop: Ensure agentic AI systems are developed to augment human expertise (and not replace critical decision-making) while maintaining proper human oversight on a continuous basis.
If organizations start taking these steps now, they’ll stay ahead of the curve.
 
Developing or deploying AI agents for your organization? Reach out to RWS’s TrainAI team to discuss your agentic AI training or fine-tuning data needs.
Nayna Jaen
Author

Nayna Jaen

Marketing Director, Enterprise Services & TrainAI, RWS
As Marketing Director, Enterprise Services & TrainAI, Nayna leads marketing efforts to promote TrainAI data services, localization, and language services and technologies to RWS’s largest clients to drive business growth. She leads all marketing initiatives for TrainAI and RWS’s Enterprise Service division, supporting sales and production teams to effectively deliver for clients.
 
Nayna has more than 25 years’ experience working in marketing, communications, digital marketing, and IT project management roles within the AI, technology, industrial, creative, and professional services industries. She holds a Bachelor of Fine Arts (BFA) degree from Boston University and a Master of Business Administration (MBA) degree with a specialization in Marketing and Information Technology (IT) from the University of British Columbia.
 
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