What Are AI Agents? Explaining Their Mechanisms and Key Frameworks

What Are AI Agents?

AI agents are software systems designed to recognize their surroundings, make autonomous decisions, and take actions to achieve specific objectives. Unlike traditional conversational AI, which responds to individual questions, AI agents can independently perform a sequence of processes such as “thinking,” “planning,” “using tools,” and “collaborating with other agents.” They function like digital assistants or colleagues, capable of handling complex, multi-step tasks that can be delegated to them.

For example, with a single directive such as, “Adjust next week’s schedule, book the best meeting room, and send invitation emails to all participants,” AI agents can interact with multiple systems like calendars, reservation platforms, and email tools to complete the task.

Basic Mechanisms of AI Agents

The operation of AI agents is supported by four core components:

1. Planning

The agent formulates a plan to achieve the given goal. This involves “task decomposition,” which breaks down the goal into smaller subtasks and determines their execution order, and “strategy decision-making,” where the agent selects the appropriate tools or APIs to use.

2. Memory

AI agents possess both long-term and short-term memory systems. Long-term memory retains past interactions and learned knowledge, while short-term memory holds the context of the task being performed. This enables the agent to understand context and engage in continuous dialogues or tasks.

3. Tool Use

Agents can utilize external tools or APIs as their “hands.” Depending on the objective, they can choose, access, and interpret tools like web searches, database queries, code execution, image generation, or various software operations.

4. Reflection and Improvement

Agents have a self-improvement function that allows them to evaluate the results of their actions, refine their plans, and devise better approaches. Through trial and error, they enhance their task execution capabilities.

Key AI Agent Frameworks

Various frameworks have emerged to coordinate multiple agents or build robust individual agents. Here are three notable frameworks gaining attention as of 2026:

AutoGen

Developed by Microsoft, AutoGen is a framework enabling multiple AI agents to collaborate and communicate. It also allows human participants to act as “agents” within the process.

Key Features:

• Conversation-driven: Solves problems through natural dialogue between agents.
• Flexible architecture: Enables diverse team structures, such as hierarchical command systems or equal collaboration among agents.
• Human-in-the-loop: Allows humans to intervene in critical decision-making for enhanced reliability and safety.
• Code execution and debugging: Executes generated code and automatically attempts corrections if errors occur.

2026 Use Case: In the finance sector, a system has been implemented where market analysis agents, risk assessment agents, investment strategy planning agents, and human auditors collaborate to create comprehensive investment reports automatically. This collaboration highlights complex risk factors that a single AI might overlook.

LangGraph

Developed by LangChain, LangGraph is a library for constructing stateful multi-agent action graphs. It enables users to visually define complex workflows as directed graphs (nodes and edges) and execute them.

Key Features:

• Stateful workflows: Explicitly manages the state of entire processes, allowing for conditional branching and loops.
• Persistence and fault tolerance: Ensures processes can resume from checkpoints in case of errors.
• Human monitoring and intervention points: Requires human approval at specific steps by design.
• Integration with LangChain: Seamlessly leverages LangChain’s rich tools and chains.

2026 Use Case: In software development automation, a pipeline has been constructed where requirements analysis agents, design agents, code generation agents, testing agents, and deployment agents work together. LangGraph’s state management ensures smooth handovers between phases, supporting iterative development under the supervision of the development team.

CrewAI

CrewAI is a framework where multiple agents collaborate as a “crew,” working together based on defined roles and goals. It aims to replicate teamwork models similar to real-world collaboration.

Key Features:

• Role-based design: Defines clear goals, roles, background stories, and toolsets for each agent.
• Task delegation and collaboration: A process manager breaks tasks into subtasks and assigns them to the most suitable agent or instructs multiple agents to collaborate.
• Self-correction mechanisms: Agents evaluate the quality of their output and retry using alternative methods if needed.
• Simplified API: Enables the construction of complex multi-agent systems with relatively less coding.

2026 Use Case: In marketing content creation, a “production crew” of agents, including market research agents, copywriter agents, SEO specialists, graphic designer agents, and editor-in-chief agents, collaborate. The editor-in-chief agent oversees the entire process, integrating and fine-tuning outputs from specialized agents to swiftly generate high-quality blog posts and social media campaigns.

Benefits and Challenges of Implementing AI Agents

Key Benefits

• Dramatic improvement in operational efficiency: Automates repetitive, time-consuming, complex tasks, allowing humans to focus on creative and strategic endeavors.
• 24/7 availability: Executes tasks continuously without being limited by human working hours.
• Democratization of expertise: Supports tasks requiring advanced expertise (e.g., drafting legal documents, code reviews) for non-specialists.
• Rapid decision-making support: Collects and analyzes large volumes of data, considers scenarios from multiple perspectives, and presents recommendations.

Current Challenges

• Predictability and controllability: It can be difficult to fully predict or control an agent’s behavior during complex dialogues or long-duration tasks.
• Costs and computational resources: Systems coordinating multiple agents and frequently invoking large language models (LLMs) involve significant computational costs.
• Security and privacy: Strict security measures are required for handling sensitive data and accessing external tools or APIs.
• Explainability (XAI): Ensuring humans can understand and explain why agents make certain decisions or take actions remains a challenge.

Prospects and Key Trends for 2026

By 2026, the focus has shifted from relying on a single high-performance model to multi-agent systems combining smaller, specialized models. This approach improves cost-efficiency, response times, and accuracy for specific domains.

Furthermore, real-time multimodal processing is expected to become a standard feature. Agents will process not only text but also audio, video, and sensor data in real-time, enabling interactions with the physical world. For example, industrial agents monitoring production lines could detect anomalies and automatically implement corrective measures.

Additionally, the establishment of standard protocols for secure communication and value exchange between agents is a critical theme. Infrastructure is being developed to allow agents from different organizations or systems to collaborate safely and interoperably.

Conclusion

AI agents are transforming workplaces and development environments, acting as proactive and autonomous problem solvers beyond mere question-answering. Frameworks like AutoGen, LangGraph, and CrewAI are foundational technologies driving this revolution. While challenges remain, the potential of AI agents is immense. The first step is to identify which tasks in your organization can benefit most from automation and collaboration using AI agents.