Agentic AI: The next-gen AI approach

Artificial Intelligence (AI) has evolved significantly in recent years, with AI Agents emerging as one of the transformative developments in the field. AI agents represent a critical shift in designing intelligent solutions, enabling systems to understand, decide, and take action independently. Let’s explore AI Agents from a technical perspective, understanding their architecture, capabilities, and applications.

What Are AI Agents?

An AI Agent is a system capable of automating the decision-making and action to achieve specific business goals. AI agents are different from traditional software, AI agents can adapt to changes, using advanced algorithms to optimise their performance.

An AI Agent comprises three core components:

1. Understanding
2. Reasoning
3. Action

These agents often rely on data-driven approaches (like deep learning models) and symbolic reasoning to make context-aware decisions. Let’s discuss these in detail.

How do AI Agents gain understanding?

AI agents perceive their environment through structured and unstructured data inputs, such as:

• Natural Language Processing: Parsing text or speech data.
• Computer Vision: Processing images or video streams.
• IoT devices/Network Devices: Interfacing with physical environments.

Once the data is collected, we might need to perform a few data cleaning techniques or an agent can perform tasks like data normalisation, feature extraction, dimensionality reduction etc. This ensures that the data is normalised and the raw data is actionable.

How do AI Agents learn reasoning?

Rule-Based Systems

This is a traditional approach where the system/agents follow the predefined rules to make the required decision.

Example: If a chatbot detects the word refund in a query, it applies the rule: “If the customer mentions refund, show the return policy.”

Pros:

• This approach to making the decision is simple since the rules are defined based on the business logic hence it is also easily interpretable.
• This is easy to implement for structured and repetitive tasks.

Cons:

• Since this method has hard-coded rules, this method lacks adaptability and cannot handle unforeseen scenarios.
• Rules need to be explicitly programmed, which gets complicated when scaling comes into the picture.

Machine Learning Models 

Latest developments in AI agents often use supervised or unsupervised learning techniques to learn from data. The agents learn from the data and identify the necessary patterns to decide. The AI agent requires historical data where input features are mapped to desired outputs.

Example: An email triage agent might classify emails into “spam,” “important,” or “low priority” based on historical data that it has been trained on.

Let’s discuss the pros and cons of this method of reasoning:

Pros:

• This method can handle structured and unstructured data like text or images.
• The Agent can be improved over time as it will be trained on the latest data.

Cons:

• Training the agent requires high-quality training data.
• Decision-making is not transparent, especially with complex models like neural networks.

Planning

In this, the agent determines the optimal sequence of actions to achieve a goal using planning algorithms for specific tasks, such as A-star, which identifies the shortest path in a weighted graph for navigation, Markov Decision Processes (MDPs) for decision-making in environments with uncertain outcomes, and Monte Carlo Tree Search (MCTS) for optimizing strategies by exploring decision trees.

Example: A delivery robot might use A-star to navigate a warehouse efficiently, planning the shortest route to its destination.

Pros:

• This method is optimized for achieving specific goals.
• It is suitable for tasks requiring long-term planning.

Cons:

• This method is computationally expensive for complex environments.
• Requires accurate models of the environment.

Multi-Agent Reasoning

This method is focused on how multiple agents work together or compete to solve problems and achieve goals. These agents could be robots, software programs, or even virtual assistants. In systems with many agents, each must make decisions based not only on their objectives but also by considering the actions of others. Multi-agent reasoning is used in many areas, such as coordinating delivery drones, managing traffic with self-driving cars, modelling ecosystems, or creating realistic simulations for training and research.

• Pros:
  • Enables collaboration in complex systems.
  • Can achieve collective goals efficiently.
• Cons:
  • Requires robust communication protocols and conflict resolution mechanisms.

How do AI Agents take action?

Decision Implementation

The action capability of AI agents is the final and most important part. After understanding the data/input and reasoning through possible outcomes, AI agents make decisions by interacting with the surroundings and delivering outputs.

The action could be implemented via APIs, or by executing a command on any IOT device or software. An example of action is, In a self-driving car, a “stop” decision from the reasoning phase translates into brake engagement.

Feedback Loop

Since the entire process works autonomously, feedback becomes very important. This loop performs the following points:

• Monitors the effects of the agent’s actions on the environment.
• Incorporates observations back into the understanding/perception component for continuous improvement.
• Example: If a warehouse robot’s action deviates from the intended path, the system recalibrates in real-time.

Types of AI Agents

AI Agents are categorised based on their autonomy and adaptability:

1. Reactive Agents: These are intelligent systems that respond directly to changes in their environment without relying on complex planning or reasoning. They follow an intent-action model, where predefined rules or behaviours dictate their actions based on the current situation. This makes them fast and efficient for dynamic, real-time tasks.
2. Deliberative Agents: This type of AI agent makes decisions by reasoning about goals, available actions, and the environment. They use a structured decision-making process which often involves planning and knowledge representation, to choose actions that align with their objectives. These agents are designed to think ahead like humans when solving problems or making plans.
3. Learning Agents: These agents improve their performance over time by learning from experience. They adapt their behaviour based on feedback through reinforcement learning or supervised learning. This enables them to handle complex tasks and make better decisions as they gain more knowledge. An example of such an agent is financial trading bots that adapt to market trends.
4. Collaborative Multi-Agent Systems: This type of agent involves multiple agents working together to achieve a common goal or solve a shared problem. These agents communicate, coordinate, and collaborate by sharing information to improve system performance. For example – Fleet management for autonomous delivery systems.

Challenges in AI Agent Development

Developing effective AI agents is technically a very complex task due to several challenges:

1. Scalability: Large-scale deployment of multi-agent systems requires significant computational resources and efficient coordination mechanisms. As the number of agents increases, ensuring smooth communication, task delegation, and resource management becomes more difficult, especially in systems with limited bandwidth or processing power.
2. Data Availability and Quality: High-quality, labelled datasets are essential for training AI agents. Noise or bias in data can severely impact performance. In some cases, it can be challenging to gather variation, and representative data, which can also lead to the overfitting scenario, thus the agent can not generalise when encountering new, unseen scenarios.
3. Real-Time Processing: Agents operating in dynamic environments must process data and make decisions within minimal time. Thus to ensure that agents can quickly analyze inputs, provide the outcomes, and take action immediately while avoiding errors is challenging.
4. Robustness: Agents must handle a scenario which they have not seen previously. In real-world applications, factors such as sensor errors, environmental changes, or deliberate attacks can disrupt an agent’s functionality, making it crucial for agents to adapt to condition changes without affecting the accuracy.

Applications of AI Agents

AI agents are increasingly used across industries to automate complex tasks:

1. Autonomous Vehicles: Use perception (LIDAR, cameras), decision-making (path planning), and action (steering, braking).
2. Smart Assistants: Agents like Siri and Alexa integrate NLP and provide personalized assistance.
3. Cybersecurity: AI agents can detect and respond to threats if any abnormal activity persists in the system by using anomaly detection and real-time monitoring.
4. Customer Support: AI agents can be used for customer support to answer the customer’s question 27/7.  The bot can also ask any follow-up questions if the customer’s question is not simple.
5. Manufacturing: AI agents can gather data from multiple sources or sensors and implement predictive maintenance tasks which reduces downtime thus assuring high productivity.

AI agents have changed the perception of how we approach automation and intelligence. By integrating advanced perception, reasoning, and action mechanisms, these agents can solve complex problems with minimal human intervention.

AI agents will transform industries, opening the doors to smarter systems and innovations. Feel free to write to us at connect@dataslush.com and discuss how our team of AI experts can help your organization accelerate forward.