Agentic AI: The latest frontier in artificial intelligence


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?
Enterprise use cases for agentic AI
Ethical and security challenges to consider
How organizations can prepare for agentic AI
- 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.