What is Agentic AI?A Guide to 5 Different Types of AI Agents

When you dive deep to explore the foundational basis of Agentic AI you find LLM as their stepping stones. However people often have not fully grasped the concept of Agentic AI or AI agent.

Agentic AI has wider automation capabilities while AI agents are bound by a specific degree of autonomy. To answer what is agentic AI and AI agents let’s take an example. For example: A sales agentic AI will handle customer queries. An AI agent in that specific framework will only generate conversations.

The above example reveals there are different types of AI agents working which builds an agentic AI workflow. So what really qualifies as an AI agent, and how many types of agents are there in AI. Learning the different types will surely give us a hint towards the difference between Agentic AI vs AI Agent.

What is Agentic AI?

Let’s start from the beginning: what makes a system qualify as an “ AI Agent”? Well to be clear there is no specific definition of agents.

• Few users state they automate specific tasks through predefined workflows.
• Most users believe agents can perform multiple tasks without human dependency. On top of that they use LLM as a foundational element for decision making and provide output in user-desired format.
• At Weam, we think agents are smart helping assistants. They just need a starting point and have strong decision making capabilities to perform tasks.

An agent needs a goal, needs instructions, and it needs to understand its role in that instance to reach the goal. Given below are a few key characteristics which help in answering queries revolving around how to build AI agents for beginners.

Key Characteristics

• Autonomy: A characteristic observed while an agent makes a decision retaining its previous experiences as memories.
• Goal-oriented behavior: Agents require goals to perform a task. When provided goals, agents will draft specific actions or workflow to reach those goals and make decisions along the way.
• Environment interaction: An essential requirement, the environment helps agents to become precise over time. Every action affected by environment changes has an impact on autonomous behaviour of an AI agent.

Types of AI Agents by Architecture

As soon as people understood what is agentic AI—they started building AI agents. Whether building from scratch for advance computation or taking help of builder platforms for common tasks the diversity of AI agents is surfacing slowly.

Moreover, how many types of agents are there in AI looking at the current developments? Five including reactive agents, model-based agents, goal-based agents, utility-based agents, and learning agents.

Reactive Agents

Reactive agents simply react to presently provided percepts. Also their reaction is limited to a predetermined set of instructions. If they face a challenge or a task request to go beyond the set they will throw an error. They don’t maintain an internal state or have any memory of past experiences.

• Reactive agents example : Infrastructure monitoring agent that automatically restarts a server or scales up cloud resources when performance metrics indicate potential downtime.

Reactive agents represent the simplest form of agent architecture with direct mapping from perception to action, without considering history.

Model-Based Agents

They maintain an internal model or representation of their environment to track aspects that aren’t directly observable. Model-based AI agent frameworks have inputs considering past states.

• Model-based AI agents example: a chess-playing program that maintains a representation of the board state, or a delivery robot that builds and updates a map of its surroundings.

They differ from reactive agents by being able to handle partially observable environments. This lets them accumulate knowledge, which then enables AI agents to be more sophisticated at decision-making.

Goal-Based Agents

Goal based agents as their name suggests are driven by goals. An AI agent can have multiple goals in a single task. An evaluator in such types of agent helps determine whether the action taken will be fruitful towards completing the goals or not. These evaluators can be simple rubric scales.

• Goal based AI agent example: A scheduling assistant that organizes tasks to meet project deadlines.

They extend beyond model-based agents by incorporating goal information. Understanding about the goals allows them to predict and plan multiple possibilities. So here preferences, prediction, and planning is taken into account for each goal to complete tasks.

Utility-Based Agents

If you build an agent that can assign a particular metric to an action and determine the usefulness of that action wouldn’t it be cool? Utility-based agents operate on the principle of maximizing a utility function.

• Utility based AI agent example: A resource allocation system that optimizes distribution based on multiple competing objectives.

They advance beyond goal-based agents by being able to handle scenarios. In terms of IT agency for example, utility value will be project progress, time requirement, positive and negative feedback, etc.

Learning Agents

Similar in working unlike any other agent the only unique difference is they have the ability to learn. Do mind storing a memory and learning are two different activities an agent performs. Retained memory helps continue progress from nowhere while learning capabilities add reasoning and logical arguments in context.

Types of AI Agents Based on Workflow

Here are some of the AI agentic workflows to take into consideration before building your own AI agent.

Routing

Routing workflows direct user requests to the most appropriate AI agent based on the query content and requirements. They act as intelligent traffic controllers, ensuring each task reaches what agentic AI is best equipped to handle it.

• For example: a customer service system might route technical questions to a product specialist agent, billing inquiries to a financial agent, and general questions to an information agent.

This routing typically involves a classification step that analyzes the content and intent of queries, followed by a selection mechanism that matches the classified request with the agent whose capabilities and knowledge base are most relevant.

Parallel Attempts

Parallel attempts workflows involve sending the same task to a multi-agent AI system simultaneously and selecting the best response based on quality criteria. This approach leverages the strengths of various agent designs to maximize the likelihood of generating high-quality outputs.

• For example: a content creation system might send a blog post request to three agents—one optimized for creativity, another for technical accuracy, and a third for SEO optimization—then combine or select from their outputs.

The key components include a task distribution mechanism, independent agent processing, and a selection or fusion algorithm that evaluates responses based on predefined metrics like accuracy, relevance, creativity, or completeness.

Orchestration

Orchestration workflows coordinate multiple agents working together sequentially or hierarchically to solve complex problems that require diverse skills or decomposition into subtasks. They function as conductors ensuring different specialized agents contribute their expertise at the right time.

• For example: Such a type of workflow enables AI agent use cases that are beneficial for research assistants. They can employ an orchestration workflow where one agent formulates search queries, another evaluates and summarizes search results, and a third synthesizes findings into a cohesive report.

This approach requires task decomposition logic to break down complex requests, a dependency management system to handle sequential workflows, and communication protocols that allow agents to share context and build upon each other’s work.

Valuable Feedback

Valuable feedback workflows incorporate evaluation and improvement mechanisms where specialized critic agents review and enhance the outputs of worker agents before delivering final results. They create a quality assurance layer in the agent workflow.

• For example: a code generation system might have a programmer agent write initial code, which is then reviewed by a code-testing agent that identifies bugs or inefficiencies, with the feedback looped back for refinement.

The essential components include clearly defined evaluation criteria, mechanisms for structured feedback generation, and refinement protocols that allow worker agents to incorporate feedback effectively while maintaining the original intent of the task.

AI agents with Prompt

Wondering how AI agents and prompts can be used in cohesion. In Weam AI you can create an AI agent according to your goals and instructions. For refined output, pick a prompt from its library and add it to the chat window.

• For example: I create an AI agent that helps me write creative copies for social media. My go-to-content tone, brand voices, and product features can be included as a prompt. The same saved prompt information can be used in articles for news platforms too.

A great way to leverage prompt and AI agent in a single query saving my time and refining end results.

Multi Agent AI Systems

Now that you know what is agentic AI system, it’s time to go a little further and try your luck with multi-agent AI.

• Definition: A goal when difficult for a single agent to achieve can be directed towards multi Agent AI. In the environment every agent plays its role following a certain task list and instructions manual. A multi agent AI may be close to AGI as long as it keeps reiterating every possibility without increasing resource cost. These agents interact, communicate, and sometimes collaborate or compete with each other to achieve goals that might be difficult for a single agent to accomplish.
• Use case: Running an entire manufacturing plant or complex business workflows where different agents handle different steps in a process.
• Here’s why: 
  • Division of specialized tasks for different business units.
  • Parallel processing increases efficiency due to multiple AI agentic workflows.
  • Developing deep expertise in narrow domains.
  • If there is fault, the entire process does not collapse.

Wrapping Up!

AI agents are considered to be building blocks of AGI. However the question still remains how to fundamentally create an agentic AI system which is not complicated. At the end of the day AI is being built to simplify human life not make it complex in terms of moving forward as individuals.

Weam AI keeps these fundamentals in check and simplifies building different types of AI agents within the platform itself. It also has its own prompt library and users can leverage various LLMs for performing diverse tasks.

If you are still confused about what is agentic AI, try it yourself on the platform. You can start for free and take a deep dive into how to build an AI agent. Remember learning AI agents and utilizing them will be proven a helpful advancement if you are trying to level up your skills for an AI enabled industry.

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