Understanding AI Agents & Agentic Workflows

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.

Although AI agents are the tangible outputs of the systems we’ve been describing, they are supported by components and workflows that are part of the broader agent AI field. Agent AI is the set of technologies and capabilities that enables autonomous systems that excel in adaptive decision making, task execution, and long-term goal management. Next, let’s walk through some of the core components and technical methods used inside a typical agentic workflow.

Leveraging Tools to Expand Capabilities

A defining feature of an intelligent agent in AI is its ability to choose and then effectively use tools to accomplish tasks. In the context of generative AI, tools are functions or systems that enable agents to execute tasks, solve problems, or automate processes. These tools interact with internal data systems like databases and data lakes, enterprise software such as CRM or ERP systems, APIs for external data, and even other agents. What makes tools so versatile is their schema — a standardized description that outlines what the tool does, when to use it, and how to interact with it. This schema enables autonomous AI agents to operate in a non-directed way, while integrating with a wide range of technologies.

The real underlying challenge for AI agents lies in how they make decisions. While an agent’s capabilities are undeniably tied to the range and quality of the tools available, its effectiveness hinges on how well it selects and uses the right tool for the job.

Building a high-performing agent today still requires a significant amount of business rules and flow management to guide its decision-making processes and ensure it consistently chooses the correct tool at the right moment. This highlights the importance of robust design and thoughtful configuration to bridge current limitations in autonomous reasoning.

The Interaction Loop: A Step-by-Step Process

An AI agent begins by interpreting user input or environmental signals, proceeds to logical reasoning or decision-making, and executes actions using its tools. The process then generates feedback which the agent can use to refine its subsequent actions.

This dynamic flow enables agents to handle complex, multi-step tasks and supports interactive or adaptive workflows.

Chaining Together the Logic With AI Agent Frameworks

Developers rely on specialized frameworks to implement and scale agent systems. Popular open-source Python frameworks like LangGraph, LlamaIndex, AutoGen, and CrewAI offer tools for building single or multi-agent systems, supporting diverse execution logic, human-in-the-loop features, and compatibility with multiple APIs and LLMs. These frameworks enable developers to model agents’ actions as sequential or collaborative processes, ensuring flexibility and scalability in real-world applications.

Considerations for Agentic Architecture

Beyond tool integration as discussed above, agentic AI may require other specialized elements to manage the dynamic and collaborative workflows of AI agents. Because of agents’ adaptive execution flow, agentic architectures often require more flexible pipelines than traditional LLM-powered applications. Execution flow in agentic systems must be dynamic, supporting non-linear paths such as loops, branching logic, and multi-agent interactions. This can necessitate more advanced orchestration tools or specialized middleware.

Furthermore, agentic systems often involve higher-order autonomy, where agents manage goals or environments that evolve over time. This can require persistent memory architectures or access to stateful environments, which may not always be essential for simpler LLM-based applications.

Finally, for multi-agent systems, the architecture must also accommodate interactions between agents, such as message passing, task delegation, and collaborative decision-making. To ensure smooth operation at scale, this may require additional layers for communication protocols or shared memory systems that synchronize tasks effectively while preventing conflicts.

With the rising prominence of AI agents, enterprises have an abundance of tools to choose from, each catering to different needs and levels of expertise. Agentic AI leaders come from different categories of software offerings. For instance, major cloud providers have dedicated offerings, such as Google Cloud’s Vertex AI Agent Builder or Microsoft’s Azure AI Foundry Agent Service, for creating and deploying AI agents tailored to enterprise workflows.

Meanwhile, domain-specific tools, such as Salesforce Agentforce, provide industry-focused solutions. For businesses seeking flexibility and future-proofing, infrastructure-agnostic platforms like Dataiku stand out, empowering enterprises to build not only AI agents but also a wide spectrum of other AI, machine learning, and data analytics applications and pipelines.

Who Makes the Best AI Agent?

The answer depends on the criteria you prioritize. Evaluating the “best” comes down to whether the agent effectively accomplishes its intended purpose — whether automating a workflow, answering questions, or streamlining operations. However, the right AI agent may not exist off the shelf for many businesses. Organizations will often need to build their own agents tailored to their specific processes, goals, and tools, ensuring the solution aligns perfectly with their unique needs.

Develop & Deliver Agents With Dataiku, the Universal AI Platform

In each subsequent section, we’ve incrementally deepened our understanding of AI agents and what’s involved in building them. There’s a healthy amount of information here, but putting it all into practice could prove challenging without additional help.

This is where we come in.

When it comes to building AI agents at scale, Dataiku provides a powerful and flexible platform that supports enterprises in crafting tailored solutions. Beyond agents, Dataiku enables organizations to build and operationalize diverse AI projects, from LLM-powered applications to traditional machine learning models and analytics pipelines. These assets can be directly created within Dataiku and leveraged by agents as tools to make data-driven decisions, execute workflows, or enhance their capabilities. Its comprehensive suite of capabilities and user-friendly design with both code and visual frameworks ensures that data teams can collaborate effectively and build enterprise-grade solutions in weeks, not months.

The Dataiku LLM Mesh & LLM Guard Services

The Dataiku LLM Mesh serves as a secure gateway and abstraction layer for your organization’s approved AI technologies, streamlining orchestration between applications, LLMs, infrastructure, and AI services while removing hard-coded dependencies. It provides access to thousands of hosted LLMs via partnerships with leading providers and Hugging Face for open-source models.

Additionally, it enables seamless access to vector databases and containerized compute resources, offering flexibility for enterprises that prefer to self-host their LLMs. As the cornerstone of our generative AI capabilities, the LLM Mesh ensures scalability, security, and operational efficiency for AI agent development.

Dataiku LLM Guard Services provide an additional, critical layer of safety and oversight for deploying AI agents at scale. These services are integrated with the LLM Mesh and help you manage and control costs, maintain quality, and reduce operational risks due to data leakage or inappropriate/toxic content, or poor AI agent performance.

Deliver Custom Agents as a Service

Dataiku is built for the creation of custom AI agents, enabling developers to build specialized agents directly on top of the LLM Mesh. Integration with the LLM Mesh also means that these custom agents inherit key benefits, such as content moderation, safety guardrails, and access controls, to ensure secure and efficient implementation across workflows.

Developers can craft custom agent logic through a guided coding experience using their Python AI agent frameworks of choice. The custom agents are automatically exposed as virtual LLMs, usable within tools like Dataiku Prompt Studio and Prompt Recipe, Dataiku Answers, and the API completion endpoint.

The built-in process method ensures standardization, while traces of each step (including events and spans) allow for easier tracking and debugging.

Continuous Quality Improvement: LLM Evaluation & User Feedback

Native tooling in Dataiku for LLM evaluation and user feedback offers a comprehensive approach to ensuring the ongoing quality of AI agents. Automated evaluation metrics allow teams to benchmark LLM responses against predefined standards, helping measure factors like accuracy, relevance, and faithfulness. These metrics help ensure that agents are consistently delivering value and meet the set expectations for the business.

In addition, built-in feedback mechanisms enable teams to improve agents based on real-world performance. For example, Dataiku Answers, a packaged web application that enables teams to quickly deliver high-quality, conversational AI use cases, has multiple ways for end-users to submit feedback while using the app.

The managed labeling feature also allows users or subject matter experts to add free-text annotations about agent actions and responses. By keeping humans in the loop and making iterative improvements, organizations can enhance the agents’ decision-making capabilities over time. Together, these tools drive continuous improvement, maintaining the highest quality in AI agents and ensuring that they evolve to meet changing business needs.

AI Governance: Ensuring Oversight & Compliance

The Dataiku platform delivers robust AI governance capabilities designed to give enterprises full visibility into every data project and model. By automatically flagging projects using LLMs, standardizing processes, and embedding mandatory review and signoff stages, teams can ensure proper oversight and scrutiny at each step of an AI project’s lifecycle. These features allow organizations to enforce documentation, lay key foundations for EU AI Act readiness, and meet internal process standards for quality and compliance.

End-to-End AI Platform

In short, Dataiku’s comprehensive platform encompasses everything needed for generative AI agent development: data access and preparation, workflow orchestration and operationalization, performance monitoring, and AI governance. Its combination of visual and code-based interfaces empowers both technical and non-technical users to contribute, accelerating adoption and fostering responsible AI practices critical for enterprise success.