Using chatbots based on artificial intelligence is the standard. Generating content with these assistants stopped being disruptive as more companies learned how to use them. Now, the real qualitative leap is happening with AI agents.
IBM reported that 99% of developers in companies are researching ways to build agents to integrate them into their operational workflows. The figure is clear and speaks of an interest that is impossible to reverse.
2026 will be the year in which these agents consolidate as the main accelerator of business productivity and the novelty that will separate leaders from the rest. Discover one by one the keys to this transformation.
What distinguishes AI agents from common assistants?
| Dimension | Chatbots | AI Agents |
|---|---|---|
| Main function | They act as a conversational interface that interprets text or voice and returns a contextual response to the user. | They act as intelligent task executors that integrate with APIs, databases, and platforms to carry out tangible end-to-end actions. |
| Type of output | Mainly informational responses or guidance within a limited conversational flow. | Operational results: report generation, script execution, triggering automations, drafting and sending emails, or other actions in real systems. |
| Level of autonomy | They operate reactively and almost always depend on the user to initiate and direct the interaction. | They have operational autonomy: they can plan intermediate steps, decide what information to consult, and what actions to execute to achieve a given objective. |
| Learning capability | They learn slowly and mainly through retraining or updates. | They integrate continuous learning mechanisms, using feedback, dynamic data, and APIs to adjust policies, processes, and content in near real time. |
| Context management | They handle context limited to the session or a short history, with reduced memory and little capacity for anticipation. | They retain long-term memory, manage dynamic contexts, and coordinate multiple subtasks using representations of previous tasks and historical data. |
| Task complexity | They focus on reactive, simple, and loosely coordinated tasks, such as answering direct questions or executing short commands. | They execute proactive, complex, and coordinated tasks that require reasoning, planning, and orchestration of multiple steps and systems, such as designing and scheduling a complete training plan. |
Main function
The chatbot fulfills a conversational interface function: it interprets text or voice from the user and delivers a contextual response.
The AI agent, on the other hand, operates as an intelligent task executor. It integrates with APIs, databases, or corporate platforms, which allows it to carry out tangible actions without direct human intervention, such as:
- Generating reports.
- Executing scripts.
- Initiating automation processes.
- Drafting and sending emails.
AI agents manage complete workflows with the right configuration and training, reserving your talent for the task of supervising their results, correcting deviations, and adding the human touch when needed.
Level of autonomy
A chatbot almost always depends on user intervention. In customer support, assistants operate as users describe their issue. With this information, the chatbot responds using only the information contained in its training.
On the other hand, AI agents have operational autonomy, which means they can plan intermediate steps to achieve a goal from a set of specific instructions.
Imagine an agent receives the following instruction: optimize the lowest-performing ad campaign. With this, it must decide what information to consult, what actions to take, and, if applicable, when to request human validation.
Learning capability
A normal chatbot depends on manual training based on user instructions or frequently asked questions. They are updated thanks to explicit and periodic reconfiguration. However, their learning is very slow and outdated.
In contrast, an AI agent integrates continuous learning mechanisms, adjusting its behavior based on the results of its actions.
This is achieved through feedback loops, access to dynamic knowledge bases, and constant interaction with APIs that enrich the contextual model. This allows policies, processes, and content to be adjusted in near real time and under supervision.
Context management
Chatbots handle limited context. Whether a short history or only the current session with a user, their memory capacity is reduced, preventing them from using past information to adjust current actions or anticipate possible future scenarios.
AI agents, for their part, retain long-term memory, allowing them to manage dynamic and extended contexts. Thus, they store symbolic representations of previous tasks and coordinate multiple subtasks simultaneously.
For example, an AI agent can remember the type of files you use most for a specific process. With this information, the agent will immediately open that format to save a step in your workflow and give you a boost to make better use of your time.
Task complexity
Chatbots usually handle reactive tasks: they answer questions or execute short commands. None of these tasks require a high level of coordination, reasoning, or prior preparation.
The opposite is true for AI agents. They perform proactive tasks that require reasoning and coordination. We are talking, for example, about designing a training schedule. A task of this type requires:
- Establishing the list of topics to be shared.
- Reserving blocks on the calendar.
- Automatically notifying participants.
Main frameworks for developing AI agents
As business demand for synthetic agents grew, the ecosystem of specialized frameworks began to expand, offering various environments to develop agents with specific purposes.
These tools constitute the essential infrastructure to build, orchestrate, and deploy functional and secure agents, as they offer prebuilt modules for common functionalities, saving you valuable development time.
AutoGPT
This is one of the first projects to demonstrate the extended autonomy of a GPT-based model. The framework allows the agent to define objectives and execute subprocesses through iterative reasoning.
The decentralized approach encourages the creation of self-directed agents, useful for research, financial analysis, or iterative software development.
LangChain
LangChain has established itself as the de facto standard for building chains of reasoning and interaction between language models, APIs, and databases.
The framework offers abstractions for memory handling, information retrieval, integration with external agents, and connection with tools. It is ideal for organizations interested in combining conversational AI with concrete execution of business tasks.
Microsoft Semantic Kernel
It is an environment developed by Microsoft and is characterized by a modular framework that blends Large Language Models (LLMs) with traditional programming functions.
Semantic Kernel has a planning architecture that gives the agent the ability to develop action plans and execute them in controlled environments, awaiting the corresponding validation or adjustment from a supervisor.
As a Microsoft product, Semantic Kernel has high compatibility with .NET and Azure environments, which facilitates its integration into ongoing enterprise workflows.
AutoGen
With the scheme proposed by AutoGen, multiple agents communicate with each other and solve problems jointly, simulating the flow of specialized virtual teams and establishing a collaborative paradigm ignored by other frameworks.
This architecture is particularly powerful for research or distributed data analysis scenarios.
Real use cases
The adoption of AI agents is accelerating across multiple sectors.
A PWC survey released in December 2025 indicated that 88% of company executives had increased their investment in AI that year, mainly due to the productivity-focused capabilities that this type of agent offers.
As pioneers understood, it is more than automating tasks. It is about redefining how companies operate and make informed decisions. With this mindset, different areas have integrated them to transform their processes and raise their level of service.
Human resources
Thanks to their strong foundation in natural language processing, agents can very easily explain internal policies, talent compensation, and benefit programs, which impacts people’s satisfaction.
In recruitment, they analyze résumés according to adaptive criteria, schedule interviews, and follow up on onboarding. They also monitor metrics to recommend retention actions when signs of possible burnout appear.
Sales
This is one of the areas that has benefited the most from this type of innovation. AI agents integrate with CRM and other data systems to improve lead prioritization, generate tailored proposals, and execute strategic follow-ups.
By learning from conversion patterns, they can adjust strategies in real time and increase the efficiency of sales teams, boosting the chances of closing high-value deals.
Salesforce confirms the effect of intelligence in sales by stating that 81% of the teams that use it experience an increase in their revenue and greater interest from their organizations in betting on these solutions.
Marketing
AI-based agents generate content for campaigns, from ad variations to email subject lines and social media copy. They also segment target audiences based on shared traits and patterns to create relevant content.
They also analyze campaign performance across different channels and recommend real-time optimizations, adjusting messaging, effort, or investment based on user behavior data.
Thus, a hybrid scheme is formed with the best of both worlds. Averi confirms this by indicating that AI-made campaigns convert 32% more, while clarifying that human content receives almost 6 times more traffic than AI-generated content.
Technical support
A well-trained AI agent resolves a large part of first-level incidents. By doing this, it drives budget optimization while giving you greater scalability, a crucial aspect for managing demand. An agent can:
- Guide users through diagnostic processes.
- Perform automated actions such as credential resets.
- Open well-documented tickets for a human agent.
- Guide basic software and hardware processes.
According to data from Zuper , resolution speed improves by 83% when artificial intelligence enters the equation. In addition, response accuracy reaches 94% compared to 71% for responses without artificial intelligence.
In parallel, AI can function as an internal knowledge agent, indexing documentation, incident histories, and system changes to help your technical team quickly find relevant solutions.
