What are the Best AI Agents?

Artificial Intelligence (AI) has evolved from a chatbot handler to a system that reasons, learns, and acts autonomously or semi-autonomously on a request. The development of agentic AI enhances human-AI interactions, research, business automation, image/video creation and editing, and productivity. 

Broadly, here is what AI agents mean, their capabilities, and use cases. 

What are AI Agents?

Artificial Intelligence (AI) agents (agentic AI) are advanced software systems that perceive their environment, make independent decisions, and take actions to achieve specific goals for users. Unlike earlier AI tools that require instructions at each step, AI agents reason, plan, and adapt their behaviours to tackle complex multi-step tasks. 

This process is powered by advanced large language models (LLMs) that apply predefined functions to new environments and tasks for the desired result.

Types of AI agents

For clarity, we grouped various AI agents into three categories, namely, traditional, collaborative/system-based, and advanced/task-specialised agents. 

Traditional AI Agents

The traditional agentic AI category includes logical decision-making agents like:

• Simple Reflex Agents

Simple reflex AI agents are similar to humans’ short-term memory. Due to their low knowledge retention, they perform better at predictable operations in fully observable environments by applying rules to information gathered via sensors. 

The auto-on and off thermostat, which operates based on temperature readings, is a relatable example of these agents.

• Model-Based Agents

Think about how self-driving cars navigate and use a world model to make decisions beyond their sensor inputs. That is how model-based agents launch a complex situation. They use an internal model to track and understand past states, actions, and functions in partially observable environments.

• Goal-based Agents

As the name implies, goal-based agents act on information available to them about the current situation and defined goals. These work like a GPS navigation system, suggesting the best route from point A to B based on traffic volume, the user’s location, and destination.

• Utility-based Agents

When it comes to utility agents, their assessment of potential outcomes uses utility functions to maximize overall satisfaction. These kinds of agents can handle trade-offs between completing goals, like a financial bot that balances risk and return.

• Learning Agents

Learning agents predict answers and improve performance based on their experience and user feedback. They can learn from performance elements, critics, and a problem generator in spam filters. 

Collaborative and systems-based AI agents

• Hierarchical Agents

Common in robotics and smart manufacturing, hierarchical agents organize and break broad goals into a strategic structure and delegate specific sub-task-level tasks. Because they break the goal down, they work independently on each level and deliver a better result. 

• Multi-Agent Systems (MAS)

Multi-agent systems (MAS) involve the interaction of multiple AI agents within a shared environment to achieve individual collective goals, such as optimizing traffic in smart cities. They use their interconnections to make autonomous decisions to achieve both personal and collective goals. 

• Collaborative Agents

Like a team, collaborative agentic AI’s approach is transforming industries such as customer service and software development. These agents collaborate with other AI models and agents to solve complex problems, learn from data, and improve outcomes. 

Advance & task specialized agents

• Generative AI Agents & Creative Agents

The generative and creative AI agents focus on creating text, images, videos, or code in response to users’ requests. If you want human-like creations in fields like graphic design, marketing, and media, these agents are something worth trying out. They generate vast amounts of human-like responses or creations, such as text-to-image generation or document summarisation.

• Conversational AI agents

Yes, there are AI agents for customer service. Conversational AI agents make human-like interactions using natural language processing. They can serve as your virtual assistant, handling complex queries and engaging in context-aware conversations. 

• Personal productivity agents

Personal productivity agents are specific AI agents that automate personal tasks to enhance productivity. This category of AI agents includes AI-powered schedulers, email managers, and personal knowledge bases.

• Domain-specific agents

Domain-specific AI agents are designed to operate in a specific field or use case, such as medicine, finance, legal, or sales. All they have is training and a dataset suitable for maximum accuracy in a specific field.

Matrix of AI Agent Types

10 Best AI Agent Examples

1. Perplexity

Perplexity AI performs in autonomous research, academic writing, and general tasks. This research AI agent autonomously plans and executes multi-step tasks. In addition to the answers, Perplexity provides comprehensive and well-cited information, shielding you from plagiarism. 

Features of Perplexity AI

• Conversational search with human language.

• Citations of information’s direct source

• Generate questions for deeper exploration

• Task automation

How it Works

• Its retrieval-augmented generation uses LLMs to understand the query, retrieve information from the web, and synthesizes them into direct cited answers based on their source.

• Analyze the current answer and topic to predict related logical sentences

• Work like Gmail and Outlook, and can execute predefined actions by interacting with the app’s API.

Screenshot of Perplexity's answer to a prompt and its citation.

AutoGen (Microsoft)

AutoGen is a sophisticated coding and software engineering agent you can use to orchestrate collaboration between multiple AI systems to solve complex coding and software development problems.

Features of AutoGen 

• Multi-agent conversation

• Customizable conversable agents (AI + human input).

• Code Execution: ability to write, run, and debug code to test solutions.

How AutoGen Works

• Initializes various agents based on the users' provided task, 

• Autonomously communicate with agents, 

• Delegate sub-tasks,

• Reviewing each other's outputs, and 

• Provide output to the user.

AutoGen integration with the code editor

3. Motion

Motion is a great example of a personal productivity and automation agent. This intelligent scheduler and daily planner proactively manages tasks, sets deadlines, and reschedules missed items based on your prompts and habits. 

Features 

• Intelligent scheduling: planning, periodizing, and scheduling all tasks into a calendar

• Dynamic rescheduling of missed tasks.

• Project management: generate project structure (tasks, stages, assignees) from a single prompt.

How it Works

• Schedule by analyzing various data points like deadlines, dependencies, priority levels, estimated duration, and calendar availability to create schedules and time block tasks

• Reschedule tasks by assessing your calendar and task status in real-time.

• Interpreting prompts like launch new website next month and convert it into a structured dependency-mapped ProJet plan based on best practices.

Motion AI Agent’s features

Motion AI Agent’s use cases.

Lindy

You cannot use code? Lindy got you covered. You can use this no-code platform to create custom personal productivity and automation agents that independently manage emails and execute complex workflows.

Features 

• No-code but plain English explanation automation. 

• Multi-trigger workflows automation of emails, meetings, research, and update CRMs

• Recording, translating, transcribing, and extracting actions from a meeting

How it Works

• Translate natural language requests into a structured, executable workflow across various app APIs

• Uses “if this, then that logic to work across connected apps

• For meeting management, it connects to meeting software like Zoom/Meet as a participant, uses speech-to-text (STT) to create a transcript of the meeting, and then uses generative AI models to identify key decisions.

Interaction with Lindy on creating an automation AI agent

CodeGPT

CodeGPT is a specialised coding and software engineering agent designed to help developers navigate large codebases, identify vulnerabilities, and streamline code transformation.

Features 

• Integrated Development partner

• Codebase search

• Refactoring and debugging

How it Works 

• Using its codebase-aware LLM, CodeGPT provides accurate, contextual, in-style suggestions by assessing the underlying user’s codebase, file structure, and open files. 

• Retrieve relevant code files and pass them to the LLM to generate structural explanations. 

• To debug, it analyzes code’s syntax and logic with respect to best practices and common error patterns.

Screenshot of CodeGPT working as an extension on VS Code

IBM Watsonx Assistants

This platform is a prime example of a conversational and customer support agent. It uses generative AI to automate customer service by responding to queries and managing workflows across various channels.

Features of IBM Watsonx Assistants

• Automated customer service

• Omnichannel support: deployment across channels.

• Low/no-code interface

How IBM Watsonx Assistants Work

• Combines the company’s verified trusted knowledge to generate a trustworthy answer. 

• Uses a consistent dialogue model that adapts via API connectors to specific input/output formats for each channel to ensure a unified customer experience.

• Streamlined conversational intents and dialogue flow to the user’s request, using a visual, drag-and-drop editor.

Screenshot of IBM Watsonx Assistants homepage

Bland AI

Bland AI is a unique conversational and customer support agent that specializes in creating realistic AI phone agents. It's used to automate inbound and outbound phone calls for businesses, handling customer interactions over the phone.

Features 

• Ultra-realistic, low-latency voice calls

• Conversational pathways

• In-call actions

How it Works

• Uses speech-to-text (STT) to convert the human caller’s speech to text, sends it on high-speed LLM to generate an intelligent response, and uses text-to-speech (TTS) to respond to the caller in a human-like voice.

• It is powered by a modular flow logic that follows specific business goals.

• After meeting a condition or specific intent, the system triggers a webhook or API call to an external system, and the result is fed back to the LLM to inform its next spoken response.

A screenshot showing what you can do with Bland AI

9. X-Design

X-Design is a generative and creative agent specifically for branding and visual identity. It helps users create logos, posters, and social media graphics.

Features 

• E-commerce image generation

• Lifestyle integration: AI-background generator

• AI-fashion model try-on

How it Works

• To generate an e-commerce image, X-design works on the user’s uploaded product images in three stages. Which are 

1. Segment the product from the background

2. Generate a new, realistic background based on style prompts

3. Blend the product seamlessly into the new scene by adjusting lighting, shadows, and perspective. 

• Like in e-commerce image generation, X-Design uses trained datasets to generate high-quality lifestyle photography with an aesthetically appealing background. 

• For mockup images like in the screenshot below, X-Design Ai agent uses computer vision and generative techniques to wrap the flat product texture into a 3D model, ensuring the fabric folds and the shadow looks authentic, like the models are wearing them.

Screenshot of X-Design changing the background of TECNO Camon 40.

10. Midjourney

Midjourney is a popular generative and creative agent known for producing stunning, imaginative, and hyper-realistic images from text prompts. Artists and designers widely use it.

Features 

• Hyper-realistic image generation.

• Styling and character reference.

• Inpainting and refining images.

How it Works

• Generate an image using a diffusion model trained on billions of image-text pairs to match the image to the prompts.

• For styling, MidJourney converts images into mathematical vectors that allow users to incorporate color, palette, texture, and other character features.

A screenshot showing examples of images and videos you can generate with MidJourney.