What is an AI agent? A short definition
An AI agent is a software system that uses a large language model (LLM) to plan, choose and call tools, and adjust each step based on the results it gets back, in order to complete a goal on a user's behalf. The core distinction: a chatbot mainly answers, while an AI agent acts, and the model, not a prewritten script, decides the path.
Major vendors phrase it differently but agree on the substance. IBM defines an AI agent as a system or program capable of autonomously performing tasks on behalf of a user or another system. Google Cloud describes AI agents as software systems that use AI to pursue goals and complete tasks on behalf of users.
Anthropic draws the clearest architectural line, which is useful for engineers: workflows are systems where LLMs and tools are orchestrated through predefined code paths, while agents are systems where LLMs dynamically direct their own processes and tool usage. This article uses that distinction as the axis for comparing AI agents with chatbots, RPA and automated workflows.
The five core components of an AI agent
Whatever platform it is built on, an AI agent that is usable in a business usually has five building blocks. Remove one and you end up with either a chatbot or automation that is hard to control.
1. Model (the reasoning engine)
The LLM reads the request, understands context and decides the next step. IBM notes that a plain LLM only answers from its training data; agentic capability comes from combining the model with tools to fetch up-to-date information and create subtasks on its own. Not every task needs the most capable model: simple, high-volume work can run on a lighter one.
2. Tools (the hands)
Tools are the functions, APIs or connectors the agent is allowed to call: look up a CRM record, read a document, open a ticket, draft an email, run a query. Google Cloud's documentation names the Model Context Protocol (MCP) as a standardized protocol for agents to communicate with external tools. IBM stresses that the ability to call tools does not by itself make an LLM an agent; the agent must decide which tools to use, when, and how to combine them.
3. Memory
Short-term memory holds the context of the running task (which steps are done, what they returned). Long-term memory stores preferences, history or retrievable knowledge, often through RAG or a vector database. Memory stops the agent from repeating work, but it is also where personal data and context-poisoning risks need control.
4. Planning and the feedback loop
The agent breaks the goal into steps, executes, observes the result and adjusts. Anthropic emphasizes that agents need ground truth from the environment at each step, such as tool call results or code execution, to assess progress, and can pause for human feedback at checkpoints.
5. Guardrails and oversight
Guardrails include least-privilege access per tool, limits on steps and budget, code-based validation of outputs, human approval for consequential actions, and an action log for traceability. IBM recommends giving users access to a log of agent actions, including the external tools used. Many introductions skip this block, yet it decides whether an agent ever reaches real operations.
How an AI agent works: a simple loop
Mechanically, most AI agents today are a loop: receive a goal, reason about the next step, call a tool, read the result, then decide to continue, ask a human or stop. Anthropic describes autonomous agents as typically just LLMs using tools based on environmental feedback in a loop.
Illustrative example: a salesperson asks, "Prepare a brief on customer X before this afternoon's meeting." The agent (1) finds the customer in the CRM, (2) reads recent emails and the latest quote, (3) notices an open support ticket and checks the ticketing system, (4) drafts a brief with sources, (5) sends it to the salesperson for review. Nobody prewrote the branch "if there is an open ticket, look it up"; the model decided. That is the difference from a workflow.
Levels of AI agent autonomy
Autonomy is not an on/off switch. Researchers at the University of Washington (Feng, McDonald and Zhang, 2025) propose five levels of autonomy defined by the user's role: operator, collaborator, consultant, approver and observer. The key point of the framework is that autonomy is a design decision, separate from model capability. A highly capable agent can still be designed with low autonomy if it must consult the user before each action.
For a business, the practical reading is: start low (a human approves every action that writes data), then raise autonomy per task type once you have quality data. See how to design approval points in Human-in-the-loop.
| Level | User role | In practice |
|---|---|---|
| L1 | Operator | The user drives; the agent assists on each request. |
| L2 | Collaborator | User and agent plan and execute together, handing control back and forth. |
| L3 | Consultant | The agent leads and asks the user for expertise or preferences. |
| L4 | Approver | The agent works alone and needs the user only for blockers, credentials or sign-off on consequential actions. |
| L5 | Observer | The agent is fully autonomous; the user monitors and can trigger an emergency stop. |
How AI agents differ from chatbots, RPA and automated workflows
These four terms are often used interchangeably. The quickest test: who decides the next step, and does the system act on other systems?
RPA, as AWS describes it, is software technology that automates repetitive, rule-based back-office workflows such as filling in forms, searching for information or sorting invoices, using software robots that act like humans in digital systems. Automated workflows (including workflows that call an LLM) follow a predefined path. Chatbots, or AI assistants, are reactive according to IBM: they act on request and typically need ongoing user direction, whereas agents can continue working independently after an initial prompt.
| Criterion | Chatbot / AI assistant | RPA | Automated workflow | AI agent |
|---|---|---|---|---|
| Who decides the next step | The user, turn by turn | Fixed script/rules | Predefined code path (may include LLM calls) | The model, within granted scope |
| Best-fit input | Natural-language questions | Structured data, stable UIs | Events, structured data | Ambiguous goals, unstructured data |
| Acts on other systems | Limited or no | Yes, via the user interface | Yes, via APIs/integrations | Yes, via tools/APIs the agent selects |
| Handles the unexpected | Answers, does not resolve | Usually stops or errors | Falls to a default/error branch | Can try another route or ask a human |
| Predictability | Medium | High | High | Lower; needs monitoring and evaluation |
| Cost and latency per run | Low | Low | Low to medium | Higher (many model calls) |
| Best for | Q&A, lookup | Repetitive clicks on legacy systems | Clear, stable processes | Multi-step tasks whose decision tree is hard to draw upfront |
Single-agent vs multi-agent: when do you need more than one?
A single agent completes a task end to end with one toolset. A multi-agent system uses several specialized agents; IBM describes it as multiple AI agents working collectively to perform tasks. Architectures can be vertical (a "conductor" agent delegates to others) or horizontal (peers); IBM notes vertical setups suit sequential workflows but are prone to bottlenecks, while horizontal ones can be slower.
Practical advice: start with a single-purpose agent that does one thing well, and split into multiple agents only for clear reasons such as context that is too long, the need for separated access rights, or an independent agent to verify another's work. We analyse orchestration patterns in depth in agent orchestration research.
Real limitations: hallucination, cost and security
Understanding what an AI agent is also means understanding what it cannot yet do. The three risk groups below need a plan before deployment, not after an incident.
Hallucination compounds across steps
Academic surveys describe LLMs as prone to generating plausible yet nonfactual content. In a chatbot, a wrong answer is one error; in an agent, one wrong step can trigger a chain of wrong actions on real systems. The survey by Huang et al. also notes that RAG reduces but does not eliminate hallucination. Mitigations: ground the agent in real tool results, validate outputs with code, and put approval points before irreversible actions.
Cost and latency
Anthropic notes that agentic systems often trade latency and cost for better task performance, and recommends finding the simplest solution first; for many applications, a single optimized LLM call with retrieval is enough. Google Cloud also notes that developing and running sophisticated agents can be computationally expensive. Set step limits, track cost per successful task, and compare against the current way of working.
Security: prompt injection and excessive permissions
OWASP ranks prompt injection as risk LLM01 and states that, given the stochastic nature of models, it is unclear whether fool-proof prevention exists. Agents raise the stakes because they read external content (emails, web pages, files) that may carry indirect malicious instructions, and they have permission to act. OWASP recommends privilege control, least privilege and requiring user approval for privileged operations. Details in prompt injection and AI agent security.
When should a business use an AI agent (and when not)?
Anthropic observes that agents add the most value for tasks that need both conversation and action, have clear success criteria, enable feedback loops and integrate meaningful human oversight. Conversely, if you can map the entire decision tree, a workflow or RPA is usually cheaper and easier to control.
Use the checklist below as a quick screen. If most answers are "yes", the task is worth piloting with an AI agent. A starting roadmap is in AI agents for Vietnamese businesses, and how to map a process before automating it is in map an AI workflow.
| Question | If yes | If no |
|---|---|---|
| Do the steps vary case by case and resist upfront mapping? | Agents have an edge | Use a workflow/RPA |
| Is the input mostly text, emails or unstructured documents? | Agents have an edge | Traditional automation is enough |
| Are there clear criteria to check whether the result is right? | You can measure and improve | Hard to deploy safely |
| Can errors be caught and reversed before they cause harm? | Autonomy can be raised gradually | Keep a human approving every step |
| Is each task valuable enough to justify many model calls? | Worth piloting | Consider a single LLM call |
| Do data and systems already have APIs or clear access? | Ready to integrate | Standardize data first |
How EYC approaches AI agents
At EYC we treat AI agents as one component of a human-led system: people set goals, permissions and approval points; agents execute the repeatable, verifiable part of the work. EYC is building toward an operating model in which AI agents perform most repeatable work under human direction, and we apply the same principles when advising clients.
If you are considering a pilot, see EYC's AI agents and automation or applied AI R&D services, and read about the AI agent operating model.
Frequently asked questions
What is an AI agent in simple terms?
An AI agent is software that uses an AI model to decide for itself which steps and tools to use to complete a goal you give it, rather than only answering questions. It repeats a cycle of planning, acting, checking the result and adjusting until the task is done or a human needs to decide.
How is an AI agent different from a chatbot?
A chatbot responds to each question and needs ongoing direction from the user. An AI agent takes a goal, breaks it into steps, calls tools such as a CRM, email or database to act, and keeps working after the initial request. A chatbot can serve as the interface to an agent.
Will AI agents replace RPA?
Not entirely. RPA suits repetitive, rule-based actions on stable interfaces, with low cost and predictable results. AI agents suit tasks with unstructured data and many exceptions. Many real systems combine both: the agent handles the reasoning, while RPA or a workflow performs the fixed actions.
Are agentic AI and AI agents the same thing?
They are closely related. An AI agent usually refers to a specific agent system, while agentic AI is the umbrella term for AI systems able to act autonomously, which may contain one or many agents. Anthropic uses "agentic systems" to cover both workflows and agents.
Should a small business use AI agents?
It can, if it starts with a narrow task, clear success criteria and human approval for important actions. Before building an agent, try simpler options such as a single LLM call or a workflow; use an agent only when the steps are genuinely hard to define upfront and the value justifies the cost.
Are AI agents safe?
Safety depends on design. The main risks are hallucination, prompt injection and overly broad access. Recommended controls include least-privilege access per tool, human approval for consequential actions, step limits, code-based output validation and an action log for traceability.