When you dive deep to explore the foundational basis of Agentic AI you discover LLM as their stepping stones. However people often haven’t fully grasped the concept of Agentic AI or AI agent.
Agentic AI has wider automation capabilities while AI agents are sure by a particular degree of autonomy. To answer what’s 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 various kinds of AI agents working which builds an agentic AI workflow. So what really qualifies as an AI agent, and what number of forms of agents are there in AI. Learning the differing types will certainly give us a touch towards the difference between Agentic AI vs AI Agent.
What is Agentic AI?
Let’s start from the start: what makes a system qualify as an “ AI Agent”? Well to be clear there isn’t any specific definition of agents.
An agent needs a goal, needs instructions, and it needs to know its role in that instance to achieve the goal. Given below are just a few key characteristics which assist in answering queries revolving around easy methods to construct AI agents for beginners.
Key Characteristics
- Autonomy: A characteristic observed while an agent comes to 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 achieve those goals and make decisions along the way in which.
- Environment interaction: An essential requirement, the environment helps agents to turn out to be precise over time. Every motion 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’s agentic AI—they began constructing AI agents. Whether constructing from scratch for advance computation or taking help of builder platforms for common tasks the range of AI agents is surfacing slowly.
Moreover, what number of forms of agents are there in AI the present 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 response is restricted to a predetermined set of instructions. If they face a challenge or a task request to transcend the set they are going to throw an error. They don’t maintain an internal state or have any memory of past experiences.
- Reactive agents example : Infrastructure monitoring agent that routinely restarts a server or scales up cloud resources when performance metrics indicate potential downtime.
Reactive agents represent the only type of agent architecture with direct mapping from perception to motion, without considering history.
Model-Based Agents
They maintain an internal model or representation of their environment to trace facets 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 having the ability 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 forms of agent helps determine whether the motion taken might be fruitful towards completing the goals or not. These evaluators may be easy rubric scales.
- Goal based AI agent example: A scheduling assistant that organizes tasks to fulfill project deadlines.
They extend beyond model-based agents by incorporating goal information. Understanding concerning the goals allows them to predict and plan multiple possibilities. So here preferences, prediction, and planning is taken into consideration for every goal to finish tasks.
Utility-Based Agents
If you construct an agent that may assign a selected metric to an motion and determine the usefulness of that motion wouldn’t it’s 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 having the ability to handle scenarios. In terms of IT agency for instance, utility value might be project progress, time requirement, positive and negative feedback, etc.
Learning Agents
Similar in working unlike every other agent the one unique difference is that they have the power to learn. Do mind storing a memory and learning are two different activities an agent performs. Retained memory helps proceed progress from nowhere while learning capabilities add reasoning and logical arguments in context.
The thing is there isn’t a general agentic AI system able to doing every task. The more we study how a single AI agent must operate, we dig deeper into defining their workflow. But why do we want to define their workflow? For predictable outcomes, collaboration between two various kinds of AI agents, and continuous improvement.
Types of AI Agents Based on Workflow
Here are a few of the AI agentic workflows to take into accounts before constructing your personal AI agent.
Routing
Routing workflows direct user requests to essentially 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 support system might route technical inquiries to a product specialist agent, billing inquiries to a financial agent, and general inquiries to an information agent.
This routing typically involves a classification step that analyzes the content and intent of queries, followed by a variety 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 identical task to a multi-agent AI system concurrently and choosing the very best response based on quality criteria. This approach leverages the strengths of assorted agent designs to maximise the likelihood of generating high-quality outputs.
- For example: a content creation system might send a blog post request to a few agents—one optimized for creativity, one other for technical accuracy, and a 3rd for search engine optimisation optimization—then mix or select from their outputs.
The key components include a task distribution mechanism, independent agent processing, and a variety 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 unravel complex problems that require diverse skills or decomposition into subtasks. They function as conductors ensuring different specialized agents contribute their expertise at the precise time.
- For example: Such a sort of workflow enables AI agent use cases which are helpful for research assistants. They can employ an orchestration workflow where one agent formulates search queries, one other evaluates and summarizes search results, and a 3rd synthesizes findings right into a cohesive report.
This approach requires task decomposition logic to interrupt down complex requests, a dependency management system to handle sequential workflows, and communication protocols that allow agents to share context and construct upon one another’s work.
Valuable Feedback
Valuable feedback workflows incorporate evaluation and improvement mechanisms where specialized critic agents review and enhance the outputs of employee agents before delivering final results. They create a top quality assurance layer within the agent workflow.
- For example: a code generation system may need 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 employee agents to include feedback effectively while maintaining the unique intent of the duty.
AI agents with Prompt
Wondering how AI agents and prompts may be utilized in cohesion. In Weam AI you possibly can create an AI agent in accordance with your goals and directions. 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 may be included as a prompt. The same saved prompt information may be utilized in articles for news platforms too.
A fantastic technique to leverage prompt and AI agent in a single query saving my time and refining end results.
Find useful AI agents!
Better if you construct one in every of your personal to find what’s possible with Weam AI
Multi Agent AI Systems
Now that you understand what’s agentic AI system, it’s time to go a bit of further and check out your luck with multi-agent AI.
- Definition: A goal when difficult for a single agent to attain may be directed towards multi Agent AI. In the environment every agent plays its role following a certain task list and directions manual. A multi agent AI could also be near AGI so long as it keeps reiterating every possibility without increasing resource cost. These agents interact, communicate, and sometimes collaborate or compete with one another to attain goals that is perhaps difficult for a single agent to perform.
- Use case: Running a complete manufacturing plant or complex business workflows where different agents handle different steps in a process.
- Here’s why:
- Division of specialised tasks for various business units.
- Parallel processing increases efficiency on account of multiple AI agentic workflows.
- Developing deep expertise in narrow domains.
- If there may be fault, your complete process doesn’t collapse.
Wrapping Up!
AI agents are considered to be constructing blocks of AGI. However the query still stays easy methods to fundamentally create an agentic AI system which shouldn’t be complicated. At the top of the day AI is being built to simplify human life not make it complex by way of moving forward as individuals.
Weam AI keeps these fundamentals in check and simplifies constructing various kinds of AI agents throughout the platform itself. It also has its own prompt library and users can leverage various LLMs for performing diverse tasks.
If you’re still confused about what’s agentic AI, try it yourself on the platform. You can start at no cost and take a deep dive into easy methods to construct an AI agent. Remember learning AI agents and utilizing them might be proven a helpful advancement when you are attempting to level up your skills for an AI enabled industry.
Frequently Asked Questions
What is the difference between Agentic AI and traditional AI?
Agentic AI could make autonomous decisions and adapt to latest situations, while traditional AI typically follows predefined rules.
What are some common applications of Agentic AI?
Applications include autonomous vehicles, smart home devices, and advanced customer support chatbots.
Can Agentic AI replace human jobs?
While Agentic AI can automate certain tasks, it often complements human work fairly than completely replacing it, especially in creative and complicated roles.
How does Agentic AI learn and adapt?
Agentic AI uses machine learning algorithms to investigate data, learn from interactions, and refine its decision-making processes over time.
What are the moral considerations of using Agentic AI?
Ethical concerns include accountability for decisions made by AI, potential biases in algorithms, and the impact on employment and privacy.
