AI Agents Demystified: Definitions and Effective Patterns

Sascha Wolter

March 06, 2025 12:15

AI Agents promise to revolutionize how we interact with technology, performing complex tasks autonomously and adapting to our needs. From virtual assistants to autonomous systems in various industries, AI Agents are poised to transform human-machine collaboration while enhancing our daily lives and business operations.

For example, contact center AI Agents are AI-powered virtual assistants or bots that help automate and improve customer interactions. They can perform a variety of tasks, such as answering frequently asked questions, routing calls, assisting human agents, and even resolving customer issues autonomously – either independently or in collaboration with a human.

However, despite the excitement, there's a catch. The term "AI Agent" remains somewhat fuzzy, with various interpretations across the tech industry. So, what are AI Agents?

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What are AI Agents?

At their core, AI Agents are solutions which act in a human-like manner and solve tasks or issues for their users. In our case, AI Agents are digital twins of contact center agents, solving issues for customers.

These AI Agents can be categorized into one of two distinct types: AI-enhanced workflows and agentic AI Agents. Each differing in their level of predefined automation and dynamic, self-directed operations.

AI-enhanced Workflows

AI-enhanced Workflows (also simply called “workflows”) refer to predefined systems that, in the realm of AI Agents, orchestrate Large Language Models (LLMs) and tools along fixed paths for specific tasks. These are structured automated processes where each step follows a predetermined logic to achieve an outcome efficiently. Workflows often also are categorized as AI Agents even if they might only use little autonomous agentic behavior.

When tasks require structure and consistency, workflows provide predictability and reliability, while AI Agents are the better option when flexibility and model-driven decision-making are needed. However, for many applications, optimizing individual LLM calls can be often sufficient.

Cognigy.AI combines structured conversational flows, LLM prompts, and NLU to create AI Agents which balance control and autonomy.

For example, a platform like Cognigy.AI allows you to design structured conversational flows combining Questions with LLM prompts for tasks like classification, reasoning, entity extraction, or answer generation. Additionally, these can be chained and combined with more agentic behavior using AI Agents for enhanced flexibility.

Agentic AI Agents

Agentic AI Agents (or “AI Agents”) operate as dynamic systems, utilizing LLMs to plan and execute tasks autonomously. Unlike AI-enhanced workflows, AI Agents can autonomously adapt to new information and make decisions on the fly, enhancing flexibility and responsiveness.

Agentic Behavior

Agentic behavior refers to the capacity of a system, whether a workflow or an AI Agent, to take independent actions, make decisions, and adapt dynamically to new inputs or changing conditions. It encapsulates the ability to execute tasks beyond rigid automation, incorporating a level of autonomy. Additionally, agentic behavior is usually goal-oriented, meaning that AI Agents actively pursue predefined objectives while adapting strategies based on evolving circumstances.

By understanding agentic behavior and its difference to pure automation, organizations can effectively implement AI solutions tailored to their specific needs:

Automation often relies on rigid, rule-based processes that are not inherently agentic, but can be improved by integrating agentic traits such as autonomy.
Autonomy surpasses basic automation by enabling decision-making processes that can incorporate feedback from users or other agents.
Conversation focusses on interactive, adaptive user engagement to better meet user needs and foster effective human-agent and agent-agent collaboration. While "conversation" is not a fundamental aspect of all AI Agent definitions, it is essential for interaction.

Agentic behavior strikes a balance between automation and autonomy, ensuring that a Conversational User Interface (CUI) facilitates seamless user interaction when necessary – whether it’s an AI Agent or an agentic workflow. For simplicity in this article, we use the term AI Agent to refer to agentic behavior in general. In most examples, LLMs and AI Agents can be used interchangeably.

When (and when not) to use AI Agents

The combination of classic rule/intent-based systems, Generative AI, and AI Agents in conversational experiences enables dynamic, context-aware, and efficient interactions.

Rules/Intents provide structure for predictable tasks.
Generative AI brings flexibility, more natural outputs, and reasoning.
AI Agents add autonomous decision-making and tool usage for scalable, intelligent solutions.

When discussing AI Agents and Conversational Experiences, the most effective approach depends on the specific use case. With a Conversational AI platform like Cognigy.AI, you have the flexibility to combine various methodologies as composite behaviors, creating a hybrid experience that leverages the strengths of each approach.

AI Agents are most effective in scenarios that require dynamic decision-making and adaptability. However, avoid using AI Agents where autonomy might be a disadvantage rather than a benefit. In such situations, a classic Rule/Intent-driven chatbot, which provides more deterministic responses, may yield better outcomes. Use AI Agents for tasks requiring agentic traits like adaptability, while integrating Rules/Intents (Natural Language Understanding) and Generative AI (LLM) for a balanced approach.

Building blocks

One foundation of AI Agents lies in their modular components, playing a critical role in ensuring agentic behavior. By leveraging augmented LLMs, structured patterns, and orchestration, AI Agents can seamlessly execute complex jobs while optimizing performance, reliability, and maintainability.

Augmented LLM

An AI Agent powered by an LLM, augmented with tools and contextual understanding.

Language models equipped with tools and contextual understanding are crucial for enabling agentic workflows. Therefore, a key requirement for AI Agents is a language model with an adequate context window and the ability to support function calls (also known as tool calls). This capability enables the AI Agent to autonomously generate search queries, select appropriate tools, and determine which information to retain for agentic behavior.

In Cognigy you can use LLM Prompt nodes for simple direct prompting of Large Language Models and more complex tool calls. Cognigy's AI Agents take that one step further and utilize the mentioned language model capabilities to offer memory, context, knowledge, and tools, enabling agentic conversational experiences. This way you only need to describe the agent’s persona, its job and its skills to get started.

Patterns

AI Agents dynamically integrate workflows and patterns, adapting to open-ended challenges and leveraging combinations of components for specific needs. Typical components in Cognigy.AI that help achieve this include:

HTTP Requests and Extensions: Allow seamless integration of LLMs via APIs.
LLM Entity Extract: Extracts structured information, such as product codes, booking codes, and customer IDs, from text using a LLM.
LLM Prompt: Simplifies sending prompts to an LLM, either as a single prompt or as part of a system message within an ongoing conversation.
Search Extract Output: Integrates knowledge retrieval, also known as Retrieval-Augmented Generation (RAG), to enhance responses with additional data.
AI Agent: Serves as the core component for agentic behavior.

Depending on the use case, you can combine these components in various ways. For example, an autonomous AI Agent can leverage the Search Extract Output for knowledge retrieval, execute a tool, and then chain an LLM Prompt with an LLM Entity Extract to classify information. The final output can then be sent via an HTTP Request or a custom extension to a third-party API, enabling seamless integration and automation. Does that sound complicated? No worries, we’ll walk through some patterns in detail next!

Prompt Chaining

Chaining LLM prompts.

Prompt chaining breaks down complex tasks into sequential steps, improving accuracy through structured processing. Each AI Agent turn or LLM prompt builds upon the output of the previous step, forming a structured, step-by-step workflow. To maintain accuracy and control, programmatic checks (such as a "gate" mechanism, as shown in the diagram below) can be applied at intermediate steps, ensuring the process stays on track and delivers reliable results.

This approach works best when a task can be logically divided into clear, structured subtasks. By breaking down a complex process into smaller steps, LLM prompts become more focused and precise, improving accuracy while potentially reducing latency.

An example of prompt chaining can be: Creating product descriptions, optimizing them for SEO (search engine optimization), then adapting them for different platforms and languages.

Evaluator-optimizer

LLM prompts to generate, evaluate and refine (using a Go To node in Cognigy.AI).

The evaluator-optimizer workflow iteratively refines responses by generating, evaluating, and optimizing output: One LLM prompt generates an initial response, while another evaluates and provides feedback in a loop, continuously improving the output.

This approach is particularly effective when clear evaluation criteria exist and iterative refinement adds measurable value. An indicator of a good fit is. if LLM-generated responses can be demonstrably improved when feedback is provided. And it’s crucial the LLM itself can articulate useful feedback to guide the refinement process.

This workflow is similar to the iterative writing process a human might follow to refine and polish a document. A concrete example of the evaluater-optimizer pattern is: Generating an initial draft of marketing copy or technical documentation, then refining it based on structured feedback from an evaluator LLM.

Routing and Model Garden

Directing an input to the most appropriate process, model, prompt, or AI Agent.

Routing ensures that inputs are classified and directed to the most suitable workflows or models for efficient and specialized handling. By categorizing inputs and assigning them to appropriate follow-up tasks, this approach enhances performance, prevents conflicts across different input types, and allows for more precise prompt engineering.

This pattern is ideal for complex tasks where inputs fall into distinct categories that benefit from separate processing. It is particularly effective when classification can be reliably managed by a large language model (LLM) or a traditional classification algorithm, for example, with leveraging Natural Language Understanding (NLU) for rule- or intent-based decisions.

An example of effective Routing is Service Center Automation where 80% of incoming calls often can be resolved using the top 10 most common questions or processes. The remaining, more complex inquiries can then be escalated to more capable AI models or human agents for specialized assistance.

Model Gardens are a specialized variation of the Routing pattern, shifting the focus from follow-up workflows to model optimization. Instead of routing conversations based on logic alone, Model Gardens intelligently direct inputs to specialized models, optimizing performance, reducing costs, and improving quality: Literally, the best available model in the garden is selected.

For instance, not every use case requires a general-purpose model. In many cases, a fine-tuned or specialized model can deliver similar - or even better - results faster and at a lower cost. This approach ensures that the right model is used for the right task, maximizing efficiency and effectiveness.

Orchestration and Concierge

An Orchestrator or Concierge transfers a conversation to the most appropriate process, model, prompt, or AI Agent.

Orchestration involves a central system that dynamically manages subtasks and delegates them to specialized workers. Depending on the use case, combining the results of multiple workers can be incorporated. This approach is ideal for tasks where the required steps or subtasks are not known in advance. An example is a coding assistant that makes complex changes across multiple files, determining necessary modifications dynamically. Another example is searching across multiple sources, gathering relevant information, and synthesizing insights based on contextual needs.

In Conversational AI an Orchestrator often acts like a Concierge. This Concierge is responsible for managing an entire conversation, determining which model or workflow should handle specific aspects, and ensuring a seamless user experience. A Concierge transfers a conversation typically to another AI Agent. This differs from Routing, where usually only single inputs are directed without managing the entire dialogue structure. A Concierge can be an LLM Prompt or an AI Agent which qualifies the incoming conversation, might ask for details, and transfers the conversation to another AI Agent. The new AI Agent can return the conversation for further orchestration, for example, if the agent could not resolve the user request.

Combining and Customizing Patterns

AI Agents are inherently flexible, allowing for the seamless combination and customization of different patterns to suit specific needs. Building blocks - such as prompt chaining, evaluator-optimizer loops, routing, and orchestration—are not rigid templates but adaptable frameworks that can be tailor to the use cases.

For instance, an AI-driven customer support system built with Cogngiy.AI might use Routing to classify inquiries, the Search Extract Output node for knowledge retrieval, and Evaluator-Optimizer to refine responses dynamically. Similarly, an AI-powered content generation pipeline could integrate Prompt Chaining for structured refinement and Model Garden to leverage specialized models for different tasks, balancing performance and cost efficiency.

Summary

AI Agents represent a transformative shift in technology, offering automation and autonomy across multiple domains. By understanding the core concepts of agentic behavior, organizations can effectively apply AI Agent concepts to streamline processes, improve productivity, and create engaging user experiences.

Successful AI Agents prioritize simplicity and user-centric design. By leveraging well-defined building blocks and patterns, AI Agents can handle complex jobs while maintaining reliability and adaptability. Adding complexity should always be driven by measurable improvements in outcomes, combining classic Conversational AI and agentic behavior to deliver the best outcome for users and businesses.