AI Agents - Definition and Types of AI Agents

The Expanding Capabilities of AI Agents 

AI agents now excel at numerous business-critical tasks: 

• Web Browsing: Researching information, gathering competitive intelligence, and monitoring online content

• Code Generation & Execution: Creating software solutions, automating development workflows, and debugging existing code

• Meeting Scheduling: Coordinating calendars, sending invitations, and managing follow-ups

• Payment Processing: Handling transactions, reconciling accounts, and managing financial workflows

• Data Analysis & Querying: Extracting insights, generating reports, and identifying patterns 

With Artificial Intelligence advancing rapidly, capabilities considered impossible today may become standard features tomorrow, continuously expanding what AI agents can accomplish for businesses. 

Types of Agents and Defined by Capabilities 

Core Functional Capabilities

Memory, perception, processing, and action mechanisms that allow agents to collect data, analyse it, and execute tasks efficiently.

Structural Dimensions

The system architecture, including coordination, planning, and scalability, determines how agents are organized and interact within larger systems.

Role and Scope

Objective-oriented or task-specialized functions that define the agent’s specific purpose within an application or system.

Non-Functional Qualities

Attributes like efficiency, ethical behaviour, and resilience influence the reliability and trustworthiness of the agent.

System Interaction

The ability of AI agents to interface and communicate with external tools, environments, and users, enabling integration and real-world applicability. 

Understanding these classifications helps organizations select appropriate agents, design effective multi-agent systems, build scalable architectures, balance performance with ethics, create resilient implementations, improve human-AI collaboration, and future-proof AI investments. 

Core Functional Capabilities 

Memory Agents 

Memory agents store, retrieve, and manage information to enhance learning and decision-making. They help AI systems remember past interactions, improve predictions, and optimize responses. 

Processing and Reasoning Agents 

Processing and reasoning agents are responsible for understanding input data, drawing inferences, and making decisions.  

Perception and Input Modality Agents 

These agents interact with the world by processing various input types. 

Action and Output Execution Agents 

Action-oriented agents perform tasks based on processed data.  

Structural Dimensions

Coordination and Communication Agents

Planning Mechanism Agents

System Architecture Agents

Scalability and Deployment Agents

Role and Scope

Objective and Goal Orientation Agents 

Task Specialization Agents

Non-Functional Qualities

Efficiency and Performance Agents

Ethics and Trust Agents

Robustness and Resilience Agents

Interaction with System

Integration of External Capabilities Agents

Environment and Context Interaction Agents

Why This Matters 

Understanding AI agent types helps organizations: 

• Select the right agents for specific business problems

• Design more effective multi-agent systems

• Build scalable AI architectures

• Balance performance with ethical considerations

• Create more resilient AI implementations

• Improve human-AI collaboration

• Future-proof AI investments 

AI Agents by Application Domain: 8 Key Types 

Enterprise Decision Agents

AI systems that analyze business data, market trends, and internal metrics to support strategic decisions. These agents help with resource allocation, risk assessment, and identifying growth opportunities through data-driven recommendations. 

Financial Analysis Agents

Specialized systems that process financial data, detect market patterns, evaluate investment opportunities, and manage risk. These agents perform portfolio analysis, fraud detection, algorithmic trading, and regulatory compliance monitoring. 

Healthcare Diagnostic Agents

Clinical decision support systems that analyze patient data, medical images, and literature to assist with diagnosis. These agents identify patterns in symptoms, recommend tests, suggest treatment options, and monitor patient progress. 

Educational Tutoring Agents

Personalized learning assistants that adapt to individual student needs, provide customized instruction, and assess understanding. These agents create learning paths, deliver appropriate challenges, and offer immediate feedback. 

Customer Service Agents

Front-line support systems that handle customer inquiries, process requests, and resolve issues. These agents manage multi-channel communications, provide 24/7 availability, and escalate complex issues to human representatives when needed. 

Security/Threat Detection Agents

Cybersecurity systems that monitor networks, identify anomalies, and respond to potential threats. These agents analyze behavior patterns, detect intrusions, manage vulnerabilities, and implement defensive measures. 

Legal Research Agents

AI systems that analyze legal documents, case law, and regulations to support legal professionals. These agents conduct precedent research, document review, contract analysis, and regulatory compliance monitoring. 

Creative Collaboration Agents

AI tools that assist with content creation, design ideation, and creative processes. These agents generate draft content, suggest improvements, facilitate brainstorming, and help optimize creative outputs across multiple formats. 

Autonomous Customer Agents enhance user experience by providing instant support, resolving queries efficiently, and personalizing interactions based on customer data.

Evolution of Agent Technology 

1950s-60s: Foundations

• Turing Test (1950) established machine intelligence criteria

• Dartmouth Conference (1956) formalized AI as a field

• ELIZA (1966) pioneered pattern-matching conversation

1970s-80s: Rule-Based Systems

• Expert systems like MYCIN used rule-based logic for medical diagnosis

• PROLOG introduced logic programming for AI

• Reinforcement learning foundations developed

1990s: Intelligent Agents Emerge

• Systems began autonomous information processing

• Early virtual assistants appeared

2000s: Machine Learning Era

• IBM Watson demonstrated advanced NLP capabilities

• Statistical models improved decision-making

2010s: Deep Learning Revolution

• AlexNet (2012) transformed image recognition

• GPT-3 (2020) achieved human-like text generation

• Robotics and self-driving vehicles advanced

2020s: Agentic AI

• Generative AI enables proactive, independent agents

• Multi-agent collaboration systems emerge

• AI systems develop long-term planning capabilities

Best Open Source Agents

1. AutoGPT

This is one of the first and highly capable open-source AI agents available. For a given goal, it first creates a set of sub-tasks. It then goes through each of those sub-tasks to get the work done. This process may even divide the sub-task into further sub-tasks based on the complexity of the task. 

It is the Agent dividing a complex task into sub-tasks and iterating through them to complete the work.

2. AutoGen

It is another of those remarkable releases in the field of Autonomous Agents. It allows you to build a Multi-Agent Conversational Framework inside your application for enhanced accuracy and better inference from the LLMs. 

For example, you must build an agent to query your structured database. Using AutoGen, you can pass on the initial query results through various other agents in the middle before finally producing the output to the user. Each agent has a defined task and a role assigned to it. If, at any step, an agent finds the responses unsatisfactory, it sends them back to the previous agent for re-evaluation. 

Discover how Akira AI Agents power autonomous operations with intelligent decision-making.

• Agent Analyst – Transforms data into actionable insights for smarter business strategies.
• Agent Force – Automates workflows and enhances operational efficiency across teams.
  
• Agent SRE – Ensures system reliability with proactive monitoring and self-healing capabilities.

Levels of AI Agents 

1. Tools (Perception + Action)

2. Reasoning & Decision making

3. Memory + Reflection

4. Generalization & Autonomous Learning

5. Personality (Emotion + Character) and Collaborative Behavior (Multi-agents)

Figure 1 -  Level of Personal LLM Agents

Figure 2 -  Level of AI Agents

Challenges in Adoption for AI Agents

1. Data Dependent

The backbone of any AI agent is the large language model (LLM). Hence, the accuracy of the response and intelligent behaviour of the overall agents directly depend on the richness of the data on which the LLM was trained.

The Agents may become highly biased if the LLM in the backend is not trained on the right data set. 

2. Limited Understanding of Context - Context Management and Intelligence 

Regarding the currently available open-source AI agents like AutoGPT and others, they have a short-term memory, making it hard for them to hold on to the context in a longer conversation. 

3. Security Concerns

AI Security and LLM software supply chain security are critical aspects for AI Quality and AI assurance and governance aspects. 

4.  Trustworthy AI Aspects 

AI agents lack common sense and ethical perspectives; they can easily be made to work toward goals with malicious intent. Considerations regarding accountability, transparency, ethics, responsibility and bias in decision-making are critical in regulated industries, especially healthcare, finance, transportation.