What are AI agents?

In today’s fast-paced AI world, AI agents have become a big deal, changing the way we think about automation and intelligent systems. At their heart, AI agents are artificial entities that perceive their surroundings, make decisions, and take actions to reach specific goals. They go beyond simply reacting to inputs; they incorporate autonomy, reasoning, and learning. In this article, we’ll delve into the foundational concepts of AI agents, uncover their potential sophistication, and explore their practical applications—especially within Giselle, empowering users to better understand the theory and fully harness the capabilities of this AI workflow builder. This guide is designed for product managers, designers, and engineers who want a clear picture of what AI agents are all about and how they might fit into more complex systems.

1. Core Concepts of AI Agents

Defining AI Agents

At the most basic level, an AI agent is an artificial entity created to interact with an environment. It takes in information (via “sensors”), processes that information, makes decisions, and then uses “actuators” (like tools or commands) to carry out actions aligned with its goals. These goals could be as simple as completing a single task or as complex as managing an entire strategic operation. AI agents can behave in different ways—from simply responding to incoming data all the way up to engaging in social interactions. This versatility makes them ideal for a wide range of uses.

Unlike traditional software that follows a strict set of instructions, AI agents are designed with a degree of independence. They can adapt to changing environments and make decisions without having each scenario explicitly programmed. You can categorize AI agents by their capabilities:

• Simple reflex agents: Act purely on current input.
• Model-based reflex agents: Use an internal understanding of the world.
• Goal-based agents: Aim to meet set objectives.
• Utility-based agents: Choose actions that maximize their “utility” or benefit.
• Learning agents: The most advanced, capable of improving through experience.

Some AI agents even include features like memory and reflection, so they can learn from past experiences and adjust their strategies. Their evolution goes hand-in-hand with progress in fields like machine learning, natural language processing, and reinforcement learning.

Key Characteristics of AI Agents

Several key traits define AI agents and set them apart:

• Autonomy: They can act on their own without constant human input, relying on their understanding of the environment and their goals.
• Reactivity: They sense changes in real time and respond accordingly, a must-have when dealing with dynamic environments.
• Pro-activeness: They don’t just react; they actively pursue objectives and take initiative.
• Social interaction: They can communicate and collaborate with other agents or humans, enabling them to handle more complex tasks.

Together, these qualities allow AI agents to plan, reason, learn, and handle tasks that would be tough for traditional software.

AI Agents vs. Generative AI

Although AI agents and generative AI can both use large language models (LLMs), they serve different roles. Generative AI creates new content (like text, images, or code), while AI agents take that information and act on it. Generative AI is all about content creation; AI agents, on the other hand, aim to solve problems by making decisions and taking actions—sometimes using generative AI to help in the process. Essentially, AI agents can produce output and then autonomously use that output to decide what to do next. They may tap into generative AI as one tool among many, but their main purpose is to make informed decisions and perform tasks in the real world.

2. Levels of AI Agents

Categorizing AI Agents by Complexity

AI agents vary widely in complexity. Borrowing an analogy from autonomous driving classifications by the Society of Automotive Engineers (SAE), we can think of AI agents in levels:

This framework, from Yu Huang’s paper “Levels of AI Agents: from Rules to Large Language Models,” helps us see how AI agents progress from following simple instructions to behaving more like independent, adaptive beings.

Tool Integration and Action Modules

By integrating different tools—like APIs, knowledge bases, or language models—AI agents can expand their ability to operate in real environments. This “action module” translates an agent’s decisions into tangible outcomes. It’s also where the environment can provide feedback on whether an action worked. Often, there’s a perception module akin to human senses, which detects changes in the environment and passes the info to the agent. In fields like robotics or autonomous driving, the action module might include low-level motion controllers or path planners. Additionally, memory modules let agents reference previous experiences to improve current decision-making.

Reasoning and Decision Making in AI Agents

Reasoning underpins planning, problem-solving, and decision-making for AI agents. Traditional approaches use symbolic logic or imitation/reinforcement learning, but these can be limited if there’s not enough data or if new scenarios pop up. LLM-based agents can handle reasoning during both pre-training and fine-tuning, sometimes displaying surprising emergent reasoning skills as they scale up.

A common strategy, task decomposition, involves breaking a bigger objective into smaller steps. Another is Chain-of-Thought (CoT), a method that helps LLMs solve complex tasks step by step. Variations like Tree-of-Thought (ToT) and Graph-of-Thought (GoT) add layers of planning complexity. While LLMs enhance decision-making, they can still get facts wrong or “hallucinate,” underlining the need for supplementary tools to ensure domain-specific accuracy and expertise.

3. Memory, Reflection, and Learning in AI Agents

The Role of Memory Modules

Memory is crucial for AI agents. It stores info gathered from the environment, which agents can later use to learn and reason more effectively. Typically, memory is split into:

• Short-term memory: Maintains immediate info needed for ongoing decisions.
• Long-term memory: Holds a broader history of events, interactions, or learned patterns.

Short-term memory helps agents with immediate problem-solving, while long-term memory keeps a record of past experiences, enabling them to adapt strategies over time. In essence, memory isn’t just passive storage—it’s a dynamic part of the agent that supports continuous learning and improvement.

Reflection and Self-Improvement

Reflection modules allow AI agents to compress information, verify their own actions, and refine their strategies. This might manifest as self-summarization, self-correction, or even a form of empathy. By incorporating techniques like knowledge graphs or retrieval-augmented generation (RAG), agents can reference domain-specific knowledge or relationships, ensuring more accurate responses. Reflection mechanisms help agents learn from both virtual and real-world feedback, fine-tuning their decision-making and planning.

Generalization and Autonomous Learning

For AI agents to really thrive in diverse or shifting environments, they need to generalize well. Few-shot in-context learning (ICL) lets LLMs adapt to new tasks by providing a few prompt examples, resembling the way humans learn. Instruction-tuned LLMs can often generalize tasks without specific fine-tuning, making them more flexible in unfamiliar situations.

Agents also need to master tool usage across various domains. With instructions and demonstrations, LLM-based agents can build, integrate, or debug tools. Curriculum learning helps them progress from simpler challenges to more complex ones, combatting “catastrophic forgetting” while allowing for a steady skill accumulation. Overall, these learning approaches are essential for developing robust AI agents that can evolve alongside their environments.

4. Personality, Collaboration, and Multi-Agent Systems

Personality and Emotional Intelligence in AI Agents

AI agents can display a kind of personality through their interactions, a bit like humans develop personality via social experiences. This can involve:

1. Cognitive skills (decision-making, problem-solving)
2. Emotional awareness (understanding moods like joy, frustration, etc.)
3. Character traits (unique personality patterns)

When LLM-based agents incorporate emotional intelligence, they become more relatable and effective in human-machine interactions. Personality can be shaped through prompt engineering or specialized datasets, allowing them to demonstrate different behaviors or communication styles, depending on the context.

Collaborative Behavior and Multi-Agent Systems

Communication is critical for LLM-based multi-agent setups. These agents might use several communication structures—layered, decentralized, centralized, or shared message pools—to coordinate:

• Layered: Different levels of agents handle different roles.
• Decentralized: A peer-to-peer network where agents talk directly.
• Centralized: A single agent (or group of agents) manages the conversation flow.
• Shared message pools: Agents post messages and subscribe to relevant ones.

Collaboration techniques range from debate-based or voting-based to role-based approaches. In debate-based systems, agents propose initial answers and discuss them; in voting-based systems, they each present a response and then vote. In role-based systems, agents are assigned distinct roles (planner, manager, worker) according to their strengths.

Collective Intelligence and Social Dynamics

Collective intelligence arises when agents work or compete together, much like in human societies. By analyzing how these agent communities interact, we gain insights into complex social dynamics that can lead to more efficient solutions. Tasks might involve everything from search and optimization to resource allocation or collaborative control. When agents share or debate information, they can combine their strengths for better outcomes—offering a sneak peek into how AI might evolve to solve large-scale, real-world problems in the future.

5. Hierarchical Levels and Practical Applications

Defining Hierarchical Levels of AI Agents

We can define AI agents hierarchically, based on how broad (general) and deep (performant) their skills are:

Adapted from “Levels of AI Agents: From Rules to Large Language Models,” this layout shows how AI agents can progress from basic, rule-based systems to sophisticated, multi-agent systems with near-human intelligence.

Practical Applications Across Industries

Several compelling use cases for AI agents are emerging in industries where precision and safety are paramount. In maritime engineering, AI agents could analyze real-time sensor data during complex welding processes to detect micro-defects that traditional inspection methods might miss.

In chemical process control, AI agents have the potential to continuously optimize hundreds of parameters including temperature, pressure, and flow rates - particularly valuable in batch production processes that require precise control. These vertical applications demonstrate potential specialized uses of AI agents. Additionally, horizontal applications could span multiple industries. In supply chain management, agents could coordinate inventory levels and logistics by processing data from multiple points in the value chain. For compliance monitoring, they could track operations against regulatory frameworks to identify potential issues early.

The key to successful implementation appears to be the combination of domain-specific knowledge with an AI agent's ability to process complex, real-time data streams and adapt to changing conditions autonomously.

Emerging Trends and Future Directions

Key trends in AI agents include the rise of multimodal capabilities, letting them process text, images, and audio to interact more “holistically.” There’s also momentum around building more explainable AI for better trust and transparency, as well as using reinforcement learning and other methods to enable continuous improvement. Meanwhile, researchers are developing advanced collaboration protocols for multi-agent systems to handle the toughest, most real-world problems. Overall, we can anticipate a future where AI agents keep growing in power, versatility, and integration with everyday life.

6. Challenges and Limitations

Factual Accuracy and Hallucinations

One major concern, especially for LLM-based agents, is ensuring factual accuracy and minimizing “hallucinations” (where the model produces incorrect or nonsensical output). This is a big deal in high-stakes domains like healthcare or finance. Proposed solutions include retrieval-augmented generation (RAG), where external knowledge sources help verify information, and reinforcement learning from human feedback (RLHF), which aligns models with human values. However, these strategies are still evolving, and perfect accuracy remains an elusive goal.

Lack of Transparency and Adversarial Attacks

LLM-based decision-making often lacks transparency, making it hard to trace how an agent arrived at a particular conclusion. This is a big issue for accountability in risky scenarios. Additionally, adversarial attacks—specially crafted inputs that trick the model—pose serious security threats. Researchers are exploring techniques like model probing and robust defense strategies to address these vulnerabilities. Improving interpretability and defense is key to building trustworthy and stable AI agents.

Managing Bias and Ethical Concerns

Because AI agents learn from data, any bias in that data can lead to biased outcomes. This becomes a serious issue in areas like hiring, lending, and criminal justice. Carefully selecting and evaluating training data is crucial, along with algorithms specifically designed to counter bias. On top of that, considerations around privacy, data security, and responsible AI development must be baked into the design process. Human oversight and well-defined ethical guidelines are essential, as are regulatory frameworks to ensure that AI aligns with societal norms and avoids harm.

7. Evaluation and Safety Practices

Evaluating Suitability for Specific Tasks

Before deploying an AI agent in a setting, it’s vital to verify that it can handle the tasks it’s meant to perform, under realistic conditions. Testing should cover not just individual subtasks but also how well the agent can chain tasks together to reach its goals. Because real environments can be unpredictable, simulations or pilot tests might be necessary. As best practices for evaluating AI agents are still evolving, you may also want to rely on additional safety measures, like having a human approve high-stakes actions.

Constraining Actions and Requiring Approval

To reduce risks, it’s wise to limit the actions an AI agent can take and require human approval for things that could cause significant harm. For instance, you wouldn’t want an agent to control weaponry or make irreversible financial transactions without explicit human authorization. Keeping humans in the loop is especially valuable for complex or high-stakes scenarios, ensuring a safety net if the agent encounters something unexpected. The challenge is giving users enough context to make an informed decision without overloading them.

Setting Default Behaviors and Monitoring

Developers can boost AI safety by defining default behaviors that favor minimal disruption and user-aligned actions. Whenever an agent isn’t 100% sure what the user wants, it should ask for clarification. Monitoring systems can also be put in place to track an agent’s reasoning and actions—helping detect mistakes or suspicious activities early. That said, these monitoring solutions should respect user privacy and avoid centralized control. Used thoughtfully, monitoring can be a powerful tool to increase transparency, trust, and security.

8. The Future of AI Agents and Multi-Agent Systems

Emerging Trends in AI Agent Development

As we look ahead, several key trends are shaping AI agent development. Researchers are working on more advanced reasoning and planning techniques, so agents can tackle tasks that go beyond rote learning. Continuous learning and adaptation are also hot topics, letting agents improve their performance on the fly without massive retraining. And with the focus on better human-agent collaboration, these systems may soon understand user intentions more deeply and work more seamlessly with people.

Multi-Agent Systems and Collective Intelligence

Multi-agent systems, where multiple AI agents team up to solve complicated problems, are on the rise. These setups leverage the combined strengths of different agents to tackle tasks that would be too challenging for one agent alone. Coordinated communication is critical, and researchers are experimenting with layered, decentralized, or centralized structures to help agents work in harmony. As these systems grow more powerful, they could be used for everything from optimizing supply chains to breakthroughs in medical research.

Ethical Considerations and Societal Impact

While AI agents bring tremendous promise, they also raise tough ethical questions around bias, fairness, accountability, and more. As they become more integrated into everyday life, it’s essential to create guidelines and regulations that promote responsible development. Transparency and accountability are crucial so that people understand how decisions are made and who’s responsible if things go wrong. It’s also vital to think about how AI might affect jobs and equity. If we handle these issues carefully, we can reap the benefits of AI agents while minimizing downsides.

9. How Understanding AI Agents Contributes to the Effective Use of Giselle

A solid understanding of AI agents—including their core principles, varying levels of sophistication, and potential challenges—can help you make the most of Giselle, an AI workflow builder. Giselle provides a framework to connect multiple LLMs and data sources, making it possible to create custom AI agents tailored to your unique needs. By familiarizing yourself with the various agent levels, you’ll be better equipped to design workflows that not only automate tasks but also integrate elements of autonomy, reasoning, and learning, fostering more intelligent and adaptable systems.

Having insight into how memory, reflection, and learning mechanisms operate can help you build solutions that are both adaptable and user-focused. At the same time, awareness of potential challenges, such as hallucinations and transparency issues, allows for the creation of workflows that are resilient and trustworthy. For those venturing into multi-agent systems, understanding collaboration and oversight strategies will be essential for managing more complex workflows, such as multiple agents working on different aspects of a larger task. In short, this foundational knowledge enhances your ability to leverage Giselle’s capabilities to create meaningful and secure AI applications.

Giselle aims to be an accessible platform for building AI agents and sharing specialized expertise. By enabling automation of tasks beyond the reach of traditional RPA systems, Giselle helps free up time for higher-value, more impactful work. Whether streamlining routine processes or innovating the next generation of AI solutions, a solid grasp of AI agents is key to unlocking Giselle’s potential. With this understanding, you’ll be well-positioned to develop transformative applications that streamline workflows, enhance productivity, and deliver lasting value.

References:

• Yu Huang | evels of AI Agents: from Rules to Large Language Models
• OpenAI | Practices for Governing Agentic AI Systems

Note: This article was researched and edited with assistance from AI Agents by Giselle. It is designed to support user learning. For the most accurate and up-to-date information, we recommend consulting official sources or field experts.