What Are AI Agents? Understanding Their Role and Capabilities

By - Blink AI Team / First Created on - January 8, 2025


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Updated on - Jan 08, 2025

What Are AI Agents?

AI agents are systems or programs designed to autonomously perform tasks on behalf of users or other systems. They achieve this by creating workflows and leveraging available tools to execute actions efficiently.
These agents go beyond simple natural language processing, offering capabilities such as decision-making, problem-solving, interacting with external environments, and executing specific tasks.
AI agents are utilized across diverse applications, addressing complex challenges in areas like software development, IT automation, code generation, and conversational assistants. By leveraging advanced natural language processing techniques from large language models (LLMs), these agents can understand user inputs, respond step-by-step, and determine when to engage external tools for optimal results.

How AI Agents Work

AI agents are powered by large language models (LLMs), which is why they are sometimes called LLM agents. Traditional LLMs, like IBM® Granite™ models, generate responses based on their training data but have limits in knowledge and reasoning. In contrast, AI agents use advanced tools in the background to access real-time information, streamline workflows, and break tasks into smaller steps to handle complex goals.
Over time, AI agents adapt to user needs by learning from past interactions and planning future actions. This allows them to provide personalized experiences and detailed responses. They can use these tools without human input, making them highly versatile for real-world tasks.
The process AI agents follow to achieve user goals is divided into three main stages:

Goal Initialization and Planning
While AI agents are capable of making decisions on their own, they still rely on humans to define their goals and environments. Three key factors influence how an autonomous agent behaves:
  1. The developers who design and train the AI system.
  2. The team that deploys the agent and provides user access.
  3. The users who set specific goals for the agent and define the tools it can use.
Once the user defines the goals and tools, the AI agent breaks down the main task into smaller tasks and subtasks to improve efficiency and performance. This process, known as task decomposition, helps the agent achieve complex goals effectively.
For simpler tasks, detailed planning isn’t always necessary. Instead, the agent can reflect on its responses and improve them step-by-step without creating a full plan.

Reasoning with Available Tools
AI agents make decisions based on the information they gather, but they don’t always have all the knowledge needed to handle every subtask of a complex goal. To fill in these gaps, they rely on tools such as external datasets, web searches, APIs, or even other AI agents. Once they retrieve the missing information, they update their knowledge base, reassess their plan, and adjust their actions as needed.
For example, imagine a user asking an AI agent to determine the best week next year for a surfing trip in Greece. Since the AI's core language model doesn’t specialize in weather forecasting, it retrieves historical weather data for Greece from an external database.
After gathering this data, the AI realizes it still needs more specific information about ideal surfing conditions. It creates a new subtask and consults another agent specializing in surfing. This agent provides details, such as the need for high tides, sunny weather, and minimal rain for optimal surfing.
With this combined information, the AI identifies patterns and predicts the best week for surfing in Greece based on weather and tide conditions. Finally, it presents its findings to the user.
By leveraging multiple tools and combining their outputs, AI agents become versatile problem-solvers, capable of tackling broader challenges than traditional AI models.

Learning and Reflection
AI agents improve their performance by using feedback from various sources, such as other AI agents or human-in-the-loop (HITL) interactions. For example, in the surfing trip scenario, after the agent provides its recommendations, it stores the learned information along with user feedback. This helps the agent refine its responses and better align with user preferences for future tasks.
If the agent collaborated with other AI agents to achieve the goal, their feedback can also be incorporated. This multi-agent feedback reduces the need for extensive user input, saving time. However, users can still provide guidance during the agent's reasoning process to ensure results match their expectations.
These feedback mechanisms enable what’s known as iterative refinement, improving the agent’s reasoning and accuracy over time. Additionally, AI agents can store data about previous challenges and solutions in a knowledge base, helping them avoid repeating the same mistakes in the future.

Types of AI Agents

AI agents come in different levels of complexity based on their capabilities. Simpler agents are suitable for basic tasks, reducing unnecessary computational power. Here are five main types of AI agents, listed from simplest to most advanced:

1. Simple Reflex Agents
These agents base their actions on current conditions without memory or external interaction. They operate using predefined rules, known as reflexes, that connect specific conditions to actions.
  • Limitations: Cannot handle unexpected situations or operate in environments with incomplete information.
  • Example: A thermostat that turns on heating at 8 PM every day. The rule here is: "If it’s 8 PM, turn on the heating."
2. Model-Based Reflex Agents
These agents combine current perception with memory to build an internal model of the environment. As new data is received, the model is updated, allowing the agent to respond to changes.
  • Capabilities: Can handle partially observable environments and changing conditions.
  • Limitations: Still bound by predefined rules.
  • Example: A robot vacuum that senses obstacles like furniture, adjusts its path, and remembers which areas have already been cleaned to avoid repetition.
3. Goal-Based Agents
Goal-based agents not only build an internal model of the world but also work towards achieving specific goals. They plan and search for action sequences to meet those goals, making them more effective than reflex agents.
  • Capabilities: Can prioritize actions based on goals.
  • Example: A GPS navigation system that identifies the fastest route to your destination. If a faster route becomes available, it updates the recommendation to reflect this change.
4. Utility-Based Agents
These agents not only achieve goals but also aim to maximize utility or reward. They use a utility function to measure the usefulness of actions based on specific criteria, such as time, cost, or efficiency.
  • Capabilities: Selects the optimal solution from multiple options that achieve the same goal.
  • Example: A navigation app that considers factors like fuel efficiency, traffic conditions, and toll costs to recommend the best route.
5. Learning Agents
Learning agents possess all the capabilities of other types but also improve over time by learning from experience. They autonomously expand their knowledge base, enabling them to adapt to new situations.
  • Components:
    • Learning: Gathers knowledge from the environment.
    • Critic: Provides feedback to evaluate performance.
    • Performance: Chooses actions based on learned knowledge.
    • Problem Generator: Suggests new actions to explore.
  • Example: Personalized e-commerce recommendations. The agent tracks user behavior, learns preferences, and refines its suggestions over time to enhance accuracy.
Each type of agent serves a specific purpose, with the more advanced ones excelling at complex, dynamic tasks.

Use Cases of AI Agents

1. Customer Experience
AI agents can enhance customer interactions by serving as virtual assistants on websites and apps. They can assist with tasks like providing mental health support, simulating interviews, and other user-specific needs. No-code templates further simplify the process of integrating these agents, making implementation accessible to a wide range of users.
2. Healthcare
In the healthcare sector, AI agents offer practical solutions for various challenges. Multi-agent systems are particularly effective in this domain, aiding with tasks like treatment planning in emergency departments and managing drug processes. These systems save valuable time and effort for medical professionals, allowing them to focus on more critical cases.
3. Emergency Response
During natural disasters, AI agents can leverage deep learning algorithms to identify individuals in need of rescue based on social media activity. These agents map the users’ locations to guide rescue teams, enabling quicker and more effective responses. By assisting in both routine tasks and critical, life-saving situations, AI agents demonstrate their versatility and impact.

Benefits of AI Agents

1. Task Automation
AI agents, powered by advancements in generative AI, enable intelligent automation by streamlining workflows and automating complex tasks traditionally handled by humans. This allows organizations to achieve goals faster, at scale, and with lower costs. These AI agents can independently create and manage tasks without requiring constant direction from human agents, boosting efficiency significantly.
2. Enhanced Performance
Multi-agent frameworks often surpass the capabilities of singular AI agents. By leveraging multiple plans of action and collaborating with specialized agents, they facilitate better learning, information synthesis, and reflection. These frameworks fill knowledge gaps through backend collaboration, making them a key innovation in artificial intelligence.
3. Improved Response Quality
AI agents deliver responses that are more comprehensive, accurate, and tailored to user needs compared to traditional AI models. By exchanging information with other agents, leveraging external tools, and dynamically updating memory streams, AI agents generate higher-quality responses. These emergent behaviors occur naturally rather than being explicitly programmed, resulting in a better user experience.

Risks and Limitations

1. Multi-Agent Dependencies
Complex tasks often require collaboration between multiple agents. However, multi-agent frameworks based on the same foundational models are susceptible to shared weaknesses, which could lead to system-wide failures or vulnerabilities to attacks. This highlights the need for robust data governance, thorough training, and rigorous testing processes.
2. Infinite Feedback Loops
While AI agents can operate autonomously, there’s a risk of agents repeatedly invoking the same tools due to incomplete planning or lack of reflection, creating infinite feedback loops. Real-time human monitoring can help address such redundancies and ensure smoother operations.
3. Computational Complexity
Building high-performance AI agents from scratch is resource-intensive, requiring significant time and computational power. Depending on the task complexity, training and execution can take several days, which adds to the operational costs.

Best Practices

1. Activity Logs
Providing users with access to logs of agent actions, including the external tools and agents utilized, enhances transparency and trust. These logs allow users to trace decisions, identify errors, and understand the AI’s iterative processes.
2. Interruptibility
To prevent agents from running indefinitely due to malfunctions or feedback loops, it’s essential to implement interruption mechanisms. These systems allow human users to terminate actions or entire operations when necessary. However, decisions to interrupt must be thoughtful, especially in critical scenarios, such as life-threatening emergencies where the agent's actions may still provide some benefit.
3. Unique Agent Identifiers
To address concerns about malicious use, assigning unique identifiers to agents can help trace their origins, including the developers, deployers, and users. Requiring these identifiers for accessing external systems ensures accountability and fosters a safer environment for AI operations.
4. Human Supervision
Occasional human feedback can help AI agents improve during their early learning stages. This allows agents to align their performance with expected standards and adapt to user preferences effectively.
In high-risk scenarios, such as financial trading or mass email campaigns, human approval should be mandatory before an AI agent takes impactful actions. Continuous monitoring is especially recommended in such domains to mitigate risks and maintain control.