Most people are familiar with AI as a tool for narrow tasks, such as asking a chatbot to summarize a paragraph or generate an image. However, a shift is occurring toward “agentic AI.”
Unlike traditional AI, which typically responds to a single prompt with a single output, an AI agent is a program capable of pursuing goals and taking actions with a degree of autonomy [1]. It does not just answer questions; it designs its own workflow to complete a task on behalf of a user or another system [2].
How AI Agents Differ from Traditional AI
Traditional machine learning and early chatbots are generally tool-like, meaning they perform specific, limited functions [1]. AI agents move beyond this by incorporating goal-directed behavior and the ability to interact with and modify their external environment [1].
Which means an agent can perform multi-step tasks autonomously [1]. For example, instead of just providing a list of flights, an AI agent could handle the entire process of booking travel plans based on a user’s request [1].
This autonomy is often compared to the levels of self-driving cars [1]. While some specialized applications reach high levels of autonomy, most current AI agent applications are likened to level 2 or level 3 autonomy, with level 5 remaining theoretical [1].
The Architecture of an Autonomous Agent
AI agents are not single models but systems. Their control flow is frequently driven by large language models (LLMs), but the LLM is wrapped in an “agent harness” [1].
This harness is a software layer that manages the essential operational constraints of the agent, including:
- Memory and execution state [1].
- Tool use and permissions [1].
- Context and prompt management [1].
- Sandboxes for safe execution [1].
Beyond the harness, a comprehensive agent architecture may include seven interconnected layers [1]. These range from the foundation models and data operations (such as vector databases and RAG) to agent frameworks, deployment infrastructure, and security compliance [1]. The final layer is the agent ecosystem, which serves as the interface between the agent and real-world users or applications [1].
Multiagent Systems and Advanced Workflows
While a single agent can handle a task, some complex goals require a multiagent system [2]. This involves multiple AI agents working collectively to perform tasks for a user [2].
One advanced application of this is Agentic RAG (Retrieval Augmented Generation) [2]. Traditional RAG systems retrieve information to help a model answer a question. Agentic RAG, however, uses agents to conduct information retrieval across multiple sources and manage more complex workflows [2].
These capabilities allow agents to be applied across various business functions, including procurement, human resources, sales, and customer service [2]. By automating tasks that previously required manual intervention, organizations aim to reduce effort and save time [1].
Security and Governance Challenges
Increased autonomy introduces new risks. One primary concern is agentic misalignment, where the agent’s actions do not align with the user’s actual intent [1].
Security is also a critical factor as agents gain more access to systems [1]. For instance, AI model inversion attacks have become a significant threat, with some costing an average of USD 6M [2].
To mitigate these risks, organizations implement AI agent governance [2]. This involves establishing standards, guardrails, and processes to ensure that agentic systems operate ethically and safely [2].
