AI agents: what they are, how they work and why they will change the way we work

AI agents are not more advanced chatbots. They are autonomous systems capable of planning, executing and verifying complex tasks using external tools. We explain the architecture, real-world use cases, risks and governance framework you need to deploy them safely

2024 was the year of chatbots. 2025 is the year of agents. The difference is fundamental: a chatbot answers questions. An AI agent executes tasks.

An AI agent can research a topic, compare options, book a flight, generate a report, email it and update a CRM. All without an intermediate human intervention. It does not follow a predefined script: it plans the necessary steps, uses external tools (APIs, databases and web browsers) and adapts its strategy on the fly if something fails.

This radically changes the possibilities of AI in business. But it also radically changes the risks. A chatbot that hallucinates produces an incorrect answer. An agent that hallucinates executes an incorrect action: it sends the wrong email, modifies data in a system or makes an erroneous operational decision.

In this article we explain exactly what an AI agent is, how its architecture works, the most promising business use cases and the specific governance framework you need to deploy them safely.

What an AI agent is (and what it is not)

An AI agent is a system that uses a language model (LLM) as its ‘brain’ to plan and execute sequences of actions aimed at achieving a goal. The difference from a chatbot is threefold:

• Autonomy: The agent decides which steps to take. It does not need you to specify every action. You give it a goal (‘research the three best CRM options for an SME with 50 employees’) and it plans how to achieve it.
• Tool use: The agent can use external tools: search the internet, query databases, call APIs, read and write files, send emails and interact with web applications. The chatbot only generates text.
• Reasoning loop: The agent reasons about its own actions. After each step, it evaluates whether the result brings it closer to the goal. If not, it adjusts its strategy. This think-act-observe loop (ReAct) is the core of agent architecture.

A chatbot generates answers. An agent generates results.

How an agent works: the architecture inside

The basic architecture of an AI agent has four components:

1. LLM (brain). The language model that reasons, plans and decides the next action. It can be GPT-4, Claude, Gemini or an open-source model. The quality of the LLM’s reasoning determines the quality of the agent.
2. Tools. The agent’s external capabilities: web search, database access, third-party APIs, file system, email, calendar and CRM. Each tool has a description that the LLM uses to decide when and how to use it.
3. Memory. The agent needs to remember what it has done, what it has found and what remains to be done. Memory can be short-term (the current conversation context) or long-term (a database with information that persists between sessions).
4. Orchestrator. The component that manages the loop: it receives the goal, asks the LLM to plan, executes tools, observes the results and decides whether to continue, adjust or finish. Frameworks such as LangChain, CrewAI, AutoGen and the Claude Agent SDK provide this orchestration.

The typical flow of an agent is: receive goal → break it into subtasks → for each subtask: select tool → execute → observe result → decide next step → repeat until complete → deliver final result.

Important: An agent’s reliability depends as much on the quality of the LLM as on the quality of the tool descriptions. An agent with access to poorly described tools will make incorrect decisions about which one to use, regardless of how good the model is.

Real-world business use cases

• Sales research assistant: The agent receives a company name, searches public information (web, registries, news and LinkedIn), generates a sales briefing with key data (industry, size, challenges and competitors) and delivers it in a format ready for the sales team.
• Technical support automation: The agent receives a support ticket, diagnoses the problem by querying the knowledge base and system logs, performs basic corrective actions (restart service, clear cache) and escalates to a human only if it cannot resolve the issue.
• Complex order management: In B2B ecommerce, the agent processes an order received by email, checks availability in the ERP, calculates prices with volume discounts, generates the proposal and sends it to the customer for confirmation.
• Periodic report generation: The agent accesses data sources (Google Analytics, CRM and ERP), extracts relevant metrics, generates a report with analysis and trends and distributes it by email to stakeholders every Monday.
• Compliance assistant: The agent monitors regulatory changes (AI Act, GDPR), assesses whether they affect the company’s AI systems, generates an impact summary and alerts the legal team if action is required.

What these cases have in common is that they combine reasoning (understanding what to do), action (using tools to do it) and judgment (evaluating whether the result is correct). That is what distinguishes an agent from a simple automated pipeline.

The specific risks of AI agents

Agents amplify both the capabilities and the risks of AI. These are the distinct risks you need to manage:

• Incorrect action with real consequences: A hallucinating chatbot produces a bad answer. A hallucinating agent can send the wrong email, modify production data or execute an erroneous transaction. The impact shifts from informational to operational.
• Error cascade: If an agent makes an error at an early step and does not detect it, later steps build on that error. The final result may be completely wrong without it being obvious where the failure occurred.
• Excessive access: An agent needs permissions to use tools: read databases, send emails and modify systems. If permissions are excessive, an agent failure can have a much wider impact radius.
• Decision opacity: An agent’s reasoning can be difficult to audit. Why did it choose that tool? Why did it interpret the result that way? Detailed logging of every step is critical.
• Amplified prompt injection: If an agent browses the web or reads emails, malicious content may try to manipulate its actions. Prompt injection against a chatbot is annoying. Against an agent with access to systems, it is dangerous.

Governance framework for AI agents

Agents require specific controls that go beyond the governance of a chatbot or predictive model:

1. Principle of least privilege. The agent should access only the tools and data it needs for its task. Nothing more. If the agent generates reports, it does not need permission to send emails. If it processes orders, it does not need access to HR data.
2. Human approval for critical actions (HITL). Define which actions the agent can execute autonomously and which require human approval. General rule: any action that sends information outside the company, modifies production data or has an economic impact must pass through a human.
3. Logging every step. Record every agent decision: which tool it selected, what input it passed, what output it received and what it decided to do next. This is essential for auditing and diagnosing failures.
4. Execution limits. Set limits: maximum number of steps, maximum execution time and maximum cost per query. An agent without limits can enter an infinite loop or consume resources uncontrollably.
5. Sandboxing and test mode. Before giving access to real systems, run the agent in a sandbox environment where it can use tools without real-world impact. Validate its behavior in controlled scenarios before moving it to production.
6. Specific traffic light. In the GO/FIX/KILL framework: Green if the agent completes its tasks correctly in more than 95% of cases. Amber if the error rate rises above 5% or out-of-scope actions are detected. Red if the agent executes an unauthorized action or one with unforeseen impact.

The key principle: An agent’s autonomy should be proportional to your ability to supervise it. Start with agents that do a few things well and require human approval. Increase autonomy only when you have evidence that they work correctly.

How to start: your first agent in 4 steps

You do not need complex infrastructure to test AI agents. You can start with a controlled pilot:

1. Identify a repetitive task with defined steps. The best candidate for your first agent is a task currently performed repetitively by a human, one that follows predictable steps and has a verifiable output. Examples: generating the weekly sales summary, preparing a briefing for a new customer and processing standard requests.
2. Define the necessary tools. List the data sources and systems the agent will need to query. Less is more: start with 2–3 tools. An agent with access to too many tools takes longer to choose the right one and is more likely to make a mistake.
3. Configure supervision. For your first agent, enable human approval for every external action. Let the agent propose the action and you confirm it. This allows you to observe its reasoning and detect errors before they have an impact.
4. Measure and decide. Run the agent on 50–100 real tasks with supervision. Measure success rate, execution time versus a human, detected errors and required human interventions. If the numbers are good, progressively reduce supervision. If not, adjust the prompt, tools or limits.

Tip: Your first agent should be internal (not customer-facing) and low risk. An agent that generates internal reports is much less risky than one that emails customers. Once you trust the governance model, expand its scope.

Where agents are heading: 2025–2026 trends

• Multimodal agents: Agents that process not only text but also images, audio and video. A support agent that ‘sees’ a screenshot of the error and diagnoses the problem.
• Computer-use agents: Computer-use agents can interact with graphical interfaces as a human would: navigate web applications, fill in forms and extract data from systems without an API. This opens automation up to legacy systems.
• Agent swarms (multi-agent): Systems where multiple specialized agents collaborate: one researches, another drafts and another reviews. Coordination between agents is an active research field with frameworks such as CrewAI and AutoGen.
• Agents with persistent memory: Agents that learn from previous interactions and improve their performance over time. They remember user preferences, past errors and solutions that worked.

AI agents are evolving rapidly. What currently requires manual configuration and careful prompt engineering will be more accessible and reliable in 12–18 months. But the governance principles (least privilege, HITL, logging and limits) will remain the same.

Conclusion: agents are the next leap

AI agents represent the transition from AI as an assistance tool to AI as an execution tool. They move from ‘answering questions’ to ‘doing things’. This makes them one of the most promising technologies for business productivity, but also one of those requiring the most governance.

The key is not to deploy agents as quickly as possible. It is to deploy them with the right level of autonomy, the necessary controls and the ability to supervise what they do.

If you want to explore which processes in your company could benefit from AI agents, design the architecture or define the governance framework to deploy them safely, Impulsa3 can support you through the process.

impulsa3.com · Digital Transformation & AI for SMEs and ecommerce · servicios@impulsa3.com

Responsible implementation requires combining these capabilities with RAG, AI hallucinations, human oversight in AI, an incident plan for AI systems and AI ROI.