AI Agents & Agentic AI
Understanding the Next Frontier in Artificial Intelligence
Two terms that have gained significant traction are AI Agents and Agentic AI.
They represent distinct paradigms in AI development, each with unique capabilities and applications.
A recent study provides a clear taxonomy to differentiate these concepts, offering insights into their architectures, use cases and challenges…
What Are AI Agents?
AI Agents are autonomous software entities designed to perform specific, well-defined tasks with minimal human intervention.
Think of them as smart assistants that excel at executing goal-oriented workflows within a bounded scope.
They leverage large language models (LLMs) like GPT-4, combined with external tools (like APIs, web searches, computer use) to process inputs, reason and produce actionable outputs.
Their core characteristics include:
Autonomy
They operate independently once initialised, handling tasks like scheduling or customer support without constant oversight.
Often Task-Specificity
Built for narrow domains, such as e-mail filtering or personalised recommendations, ensuring efficiency and precision.
Reactivity and Adaptation
They respond to real-time inputs, like user queries, and can adapt through feedback loops, though their learning is limited.
A practical example is Anthropic’s Claude in its “Computer Use” project, where it autonomously navigates software, fills forms, or conducts research by controlling a computer like a human.
AI Agents work well applications like customer support automation or enterprise search, where modular, repeatable tasks dominate.
What Is Agentic AI?
Agentic AI takes things to the next level, representing a paradigm shift toward collaborative, multi-agent systems.
Instead of a single entity, Agentic AI involves multiple specialised AI Agents working together to tackle complex, dynamic goals.
These systems decompose high-level objectives into subtasks, assign them across AI Agents, and coordinate through communication protocols, shared memory, or centralised orchestrators.
Key features can include:
Multi-Agent Collaboration
AI Agents with distinct roles (like planning, execution, monitoring) collaborate to achieve shared goals, like optimising a smart home ecosystem.
Dynamic Task Decomposition
Complex tasks are broken down and reassigned in real-time, adapting to changing conditions.
Persistent Memory & Reasoning
AI Agents retain context across interactions, enabling reflective decision-making and iterative strategy refinement.
An analogy from the study illustrates this: an AI Agent is like a smart thermostat adjusting temperature autonomously, while Agentic AI is a smart home system where agents manage weather forecasts, energy pricing, and security in concert.
Agentic AI excels in domains like robotics coordination, medical decision support, or research automation, where interdependent tasks demand orchestrated intelligence.
Key Differences
The study outlines several distinctions:
Scope and Complexity
AI Agents focus on single, modular tasks (like answering queries), while Agentic AI handles multifaceted, interdependent workflows (like managing a supply chain).
Architecture
AI Agents are more standalone, tool-augmented systems, whereas Agentic AI involves multi-agent ecosystems with communication and coordination layers.
Autonomy Level
AI Agents have bounded autonomy within predefined tasks; Agentic AI exhibits emergent autonomy through collaborative reasoning and adaptation.
Applications
AI Agents power customer support or email prioritisation, while Agentic AI drives advanced use cases like autonomous vehicles or collaborative healthcare systems.
In Closing
The study suggests that addressing these issues will unlock new possibilities…
For AI Agents, advancements in memory buffers and tool integration could enhance adaptability.
For Agentic AI, scalable orchestration and explainability frameworks will be critical for high-stakes domains like finance or autonomous robotics.
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
