As generative AI continues to transform industries, AI agents are moving from niche experiments to practical, scalable tools shaping modern workflows. With AI agents stepping in to handle everything from repetitive, routine tasks or code development to reinventing advanced business processes, these systems are becoming increasingly integral to enterprise operations and innovation. Yet, behind the scenes lies a sophisticated interplay of components and workflows that can be challenging to grasp.
In this article, we’ll cut through the complexity to explore:
- The fundamentals of AI agents and some common use cases.
- The building blocks of agentic workflows and systems.
- The landscape of agent-builder tooling — and how platforms like Dataiku are bridging the gap between AI aspirations and real-world applications.
What Are AI Agents?
At their core, AI agents are large language model (LLM)-powered systems designed to achieve objectives across multiple steps, leveraging tools autonomously as needed — that is, without requiring user prompts for every action. This ability to independently and dynamically navigate diverse and complex series of tasks makes AI agents distinct from deterministic, single-task systems.
Expanding on this, AI agents are capable of making decisions and taking actions within set boundaries. They interact with their environment — whether through APIs, databases, or other tools — and adapt to shifting inputs or goals, in order to perform tasks that range from routine automation to complex problem-solving.
These systems excel in handling open-ended tasks within dynamic environments — particularly when directives are provided in natural language, as is the case with conversational applications like virtual assistants or in-app helpers. With limited or no human supervision, AI agents can orchestrate actions, manage workflows, and access external resources to accomplish their goals.
What Is Agentic AI?
Agentic AI represents a specialized subdomain of AI, similar to how computer vision focuses on the use of AI technologies for image analysis. Applications such as agent AI assistants are a byproduct of agentic AI, which encompasses the broader frameworks and techniques that enable such systems to function. This field is central to creating systems that display higher-order behaviors resembling human agency.
The 2 Faces of AI Agents
Agents in AI can be broadly categorized into two primary modalities, each tailored to distinct operational needs and user experiences:
1. Back-End AI Agents: Hidden Workhorses
Back-end AI agents operate behind the scenes without direct user interaction, focusing on process automation, decision-making, and optimization tasks. These “headless” systems are often embedded within enterprise workflows, handling complex processes with minimal human intervention.
AI agents examples of this modality include systems that categorize and route customer service or support requests, automatically adjust and optimize supply chain parameters, or tackle the manual process of identifying proposals (vs an AI agent-automated process).
2. Front-End AI Agents: Interactive Partners
Exposed to end users, these agents offer a conversational or interactive interface, providing hands-on assistance and streamlining everyday tasks. In contrast, front-end AI agents engage directly with users through interfaces, often leveraging conversational or interactive designs to deliver value. These agentive AI systems are responsive and tailored for human usability and experience. Examples range from an agent AI assistant that provides “hands-on” assistance to streamline everyday tasks, to embedded agents in tools like CRM platforms that guide sales teams in real time.
Together, these modalities showcase the versatility of AI agents, seamlessly integrating into backend systems or delivering direct value through engaging user interfaces. Each plays a critical role in driving efficiency and innovation across industries.
Single-Agent vs. Multi-Agent Systems
The answer to “What is an AI agent?” isn’t always straightforward, as it depends on the system’s complexity and the scope of tasks it is designed to handle. Organizations can build both single-agent and multi-agent systems. While both approaches have their strengths, understanding the distinction can help clarify how AI agents are applied to solve real-world problems, as well as which agent frameworks might be appropriate for your use case.
Single-Agent Systems: Focused & Specialized
A single-agent system is designed to handle specific tasks autonomously within a constrained scope. These agents operate independently and are suited for tasks requiring limited decision making. An example of a single-agent system might be an AI agent equipped with multiple recommendation models as tools, that evaluates a situation and selects the most appropriate model to generate tailored suggestions for a user.
Multi-Agent Systems: Collaborative Intelligence
For cases where it would be impossible or impractical to imbue a single agent with all the capabilities required for your use case, it may make sense to build a multi-agent system instead. For example, suppose there is a need to navigate multiple types of content (documents, images, etc.) with specific prompts, or the prompting required would be exceedingly complex or simply too long for the LLM’s context window. These are scenarios when you should consider a multi-agent approach for better modularity and ease of troubleshooting.
In multi-agent systems, several specialized agents work together to solve complex problems. Each agent performs a distinct function, contributing to a shared goal. For instance, a self-driving car is a multi-agent system, where disparate agents handle tasks like navigation, object detection, and decision-making, collaborating to ensure safe operation. These agents may act in sequence or in parallel, depending on what the situation calls for.
The Blurring Line Between Single & Multi-Agent
What is an AI agent, then, when even single-agent frameworks can leverage multiple agents by integrating them as tools? This flexibility means the difference between single and multi-agent systems often lies in how the system is developed, rather than its inherent capabilities. A single agent can leverage external tools or interact with other agents, creating multi-agent-like behavior within a single pipeline.
