AI Agents and their impact on business automation

February 27, 2025

What is an AI Agent?

An AI Agent is an application designed to achieve specific objectives by observing its environment and proactively interacting with it, using specialized tools such as API interfaces, language models, or information processing modules.

These agents can receive general instructions and autonomously determine the most effective strategies to fulfill their purpose, combining learning, planning, and execution in a continuous improvement cycle.

Their ability to operate autonomously, without constant human intervention, enables them to make decisions even without explicit instructions. Additionally, they can plan and execute tasks independently, leveraging real-time data and continuously optimizing processes.

—For example, in the banking sector, an AI agent can integrate with a risk management platform to analyze a client's financial transactions in real time, detect potential fraud, and take or recommend preventive measures. This enhances security, reduces financial fraud, and streamlines critical decision-making.

■ AI Agents vs. Agentic AI. While AI Agents execute autonomous tasks through language models and specialized tools, Agentic AI is a broader approach aimed at developing AI systems capable of reasoning, planning, and acting autonomously across diverse contexts. This approach encompasses both individual agents and collaborative architectures and multi-agent systems.

AI Agents represent a significant advancement in how businesses approach automation and information management.

Components of an AI Agent

An AI agent is built on three essential pillars that form its cognitive architecture:

Language Model (LLM): Functions as the agent’s "brain," responsible for processing information and making decisions. It can be a single model or a combination of specialized models using reasoning frameworks such as:
- ReAct (Reasoning + Acting): Combines reasoning and action to interact iteratively with the environment.
- Chain-of-Thought: Allows the model to break down complex problems into a logical sequence of steps.
- Tree-of-Thoughts: Explores multiple reasoning paths in parallel, evaluating different solutions before making a decision.
Interaction Tools: Enable the agent to connect with the external world, whether through web APIs, databases, or enterprise systems.

These tools significantly expand the base model’s capabilities, allowing integration with extensions, user-defined functions, and data stores that facilitate access to up-to-date and relevant information.
Orchestration Layer: Manages the flow of information between the model and the tools, ensuring a continuous cycle of reasoning, planning, and action.

This layer also handles memory management and learning from experience, improving complex decision-making by aggregating results from multiple sources.
- Frameworks: Various open-source and standardized frameworks, such as LangChain, LlamaIndex, and CrewAI, provide tools for workflow management, language model interaction, and tool integration.
- Evaluation: The interaction between a planning agent and an evaluation agent (which can be the same) is essential for refining response quality.
  
  An evaluator-optimizer workflow enables an LLM to generate a response, have another evaluate it, and provide feedback for improvement. Continuous evaluation is critical to agent performance.
- Fallback: Agents must have robust mechanisms to handle queries beyond their scope or where tools are unavailable. Ensuring that agents "know when they don’t know" is essential to prevent false or hallucinated responses and maintain reliability.

An AI Agent combines acquired knowledge, logical reasoning, and access to external information and tools to autonomously execute tasks.

Types of AI Agents

AI-based systems fall into two main categories:

Workflows: Structured systems with predefined code paths, ideal for repetitive and well-defined tasks requiring high consistency. These are efficient in environments where operational variables are stable and predictable.
Autonomous Agents: Dynamic systems where language models manage their own processes, adapting in real time to changing conditions. These are ideal for complex environments requiring flexibility and responsiveness in dynamic and unpredictable data-driven contexts.

■ Thanks to LLMs, with a well-crafted prompt, you can instruct an agent in writing and explain your expectations verbally. This eliminates the need to program an extensive decision tree.

Use cases

AI Agents revolutionize various business sectors through practical applications such as:

Customer Service: Integration with intelligent assistants or chatbots that access customer histories, handle complex requests, and automate processes like refunds or ticket updates, improving operational efficiency and customer satisfaction.
Software Development: Agents capable of writing, testing, and debugging code autonomously, managing multiple repositories, and optimizing development cycles through continuous error analysis and automated solutions.
Information retrieval and analysis: Agents that gather data from multiple sources, analyze market trends, and generate personalized executive reports, facilitating data-driven decision-making.
Financial task automation: Processing transactions, automatic audits, and financial report generation, reducing human errors and enhancing efficiency in financial data management.
Recommendation systems: Personalization of commercial offers based on customer behavior analysis and purchase history, using advanced algorithms to predict preferences and optimize marketing strategies.

■ AI Agents can integrate with Business Applications to optimize finance, HR, supply chain, and customer experience management. These applications provide a structured environment where agents can automate tasks and enhance decision-making with real-time data.

The ability of AI Agents to learn, adapt, and act autonomously enhances operational efficiency and unlocks new opportunities for business innovation.

AI Agent tools

Tools are the mechanisms that enable agents to transcend their inherent knowledge limitations and interact with the external world, such as:

API Extensions: Facilitate connections with external services, from e-commerce platforms to ERP and Business Applications, allowing agents to interact efficiently with third-party applications.
Custom functions: Code modules designed for specific tasks, such as data formatting or information validation, executed on the client side for increased control over data flow.
Data Stores: Enable access and manipulation of large volumes of structured and unstructured data, facilitating retrieval-augmented generation (RAG) architectures to improve response accuracy.

Security in AI Agents

AI Agents introduce new risk layers that require careful attention. To ensure security, agents must operate under the principle of least privilege, receiving only the necessary access for their tasks.

Additionally, the risks of indirect access to sensitive information through AI Agents must be anticipated, and appropriate security measures implemented.

—For example, an employee without access to payroll documents stored in the company’s ERP could indirectly obtain this information if an internal AI Agent has access to it.

Thus, it is essential to implement strict authentication and authorization methods, client-side functions to better control sensitive data, and a systematic review of indirect access. Additionally, conduct detailed audits to track the Agents' actions.

The introduction of AI Agents adds security risks that must be assessed, requiring well-designed tools and clear documentation.

It is also crucial to maintain simplicity in agent design and prioritize transparency, explicitly displaying the AI Agent’s planning steps to identify and correct security issues.

■ Access the whitepaper Agents (PDF).

— CONTRIBUTED BY Javier Coronado —