03 · What You Need to Know
Understanding AI Agents, Chatbots, and Agentic Research Systems
What is an AI chatbot?
An AI chatbot is a software system designed to interact with users through conversational exchanges.
Traditional chatbots often relied on predefined rules, decision trees, or intent classification.
Modern generative AI chatbots commonly use large language models to produce responses based on user inputs and available conversation context.
They may answer questions, explain concepts, draft text, summarize documents, or assist with problem solving.
However, conversational interaction alone does not establish that a system independently pursues goals or performs actions beyond generating responses.
A chatbot may remain primarily reactive, responding to each user request without maintaining an autonomous task-execution process.
What is an AI agent?
An AI agent is generally understood as a system that perceives or receives information about an environment and selects actions intended to advance a goal.
In contemporary language-model applications, an agent may use an LLM to interpret instructions, select tools, evaluate intermediate results, and determine subsequent actions.
For example, an AI research agent might search a database, inspect retrieved abstracts, identify missing information, and request additional documents.
Some agents operate through predefined workflows, while others have greater discretion in selecting actions.
The degree of autonomy depends on the system's architecture, permissions, and operating constraints.
What is the central difference between a chatbot and an agent?
AI chatbot
A system primarily organized around conversational interaction, generating responses to user messages and potentially offering limited tool-supported assistance.
AI agent
A system organized around pursuing objectives through selected actions, potentially involving tools, environmental feedback, multi-step execution, and controlled autonomy.
A chatbot may explain how to search for literature. An agent may carry out the search using authorized tools.
However, a conversational interface can also serve as the control mechanism for an agent.
Researchers should therefore avoid treating chatbots and agents as mutually exclusive product categories.
What characteristics commonly distinguish AI agents?
Agentic systems often exhibit several operational characteristics, although no single feature is sufficient to establish a universal definition.
| Characteristic |
Chatbot-oriented system |
Agent-oriented system |
| Primary interaction |
Responds to conversational requests |
Pursues defined objectives through actions |
| Task execution |
Often provides instructions or generated content |
May execute multi-step procedures |
| Tool use |
Optional and sometimes limited |
Often central to task execution |
| State management |
May use conversation history |
May maintain task state and intermediate results |
| Decision-making |
Primarily selects responses |
May select subsequent actions and tools |
| Human involvement |
Often requires user initiation of each task |
May continue within delegated permissions |
| Environmental effects |
May produce informational outputs |
May modify files, databases, or external systems |
These differences describe common design patterns rather than rigid categories.
A chatbot with substantial tool access and multi-step execution may reasonably be described as agentic.
Does an AI agent need a large language model?
No. The concept of intelligent agents predates contemporary large language models.
Classical artificial intelligence includes agents that use rules, planning algorithms, reinforcement learning, or other computational approaches to select actions.
Large language models have expanded the range of tasks that agents can interpret and coordinate through natural language.
However, an agent does not become an agent solely because it contains an LLM.
The relevant feature is its role in selecting or executing actions within an environment.
Does tool use automatically make a chatbot an agent?
Not necessarily.
A chatbot may call a calculator or retrieve a document in response to a direct user request without independently managing a broader task.
Such behavior demonstrates tool use but may involve limited agentic autonomy.
Conversely, a system that selects among tools, evaluates intermediate outcomes, and decides which action to take next exhibits a more agent-oriented workflow.
Researchers should examine the system's actual control structure rather than classify it solely by whether it has access to tools.
What is agentic AI?
Agentic AI is a broad term describing AI systems designed to exhibit goal-directed behavior through some degree of autonomous action selection and execution.
In contemporary applications, the term often refers to language-model-based systems capable of coordinating multi-step tasks.
However, the terminology is not fully standardized.
Some vendors use agentic AI to describe systems with extensive autonomy, while others apply the label to relatively constrained workflow automation.
Researchers should therefore define what agentic means in the particular study.
Operational characteristics are more informative than marketing terminology.
What does autonomy mean for an AI agent?
Autonomy concerns the extent to which a system can select or execute actions without requiring immediate human direction for every step.
Autonomy is better understood as a continuum than a binary property.
An agent may be permitted to search documents independently but require approval before modifying research records.
Another system may execute code automatically within a restricted environment.
A more autonomous system may coordinate several tools and continue working until a specified objective is satisfied or a stopping condition is reached.
Researchers should document the actions the system can perform independently and those requiring human authorization.
What is a typical AI agent architecture?
Many language-model-based agents combine an objective, a model or decision component, available tools, state information, and an execution controller.
A simplified workflow may involve receiving a goal, selecting an action, executing a tool, observing the result, and deciding what to do next.
Step 1: Receive the objectiveThe user specifies a task, such as preparing a preliminary evidence table from selected research articles.
Step 2: Interpret the taskThe system identifies the required information, available resources, and permitted actions.
Step 3: Select an actionThe agent chooses a tool or procedure, such as retrieving an article or extracting metadata.
Step 4: Observe the resultThe system receives information from the executed action and updates its task state.
Step 5: Continue or reviseThe agent determines whether another action is necessary or whether an error requires correction.
Step 6: Stop or request approvalThe system completes the authorized task, reaches a stopping condition, or requests human intervention.
Actual implementations may differ substantially. Some follow deterministic workflows, while others use more flexible action-selection procedures.
The existence of a multi-step process does not guarantee that the selected actions are correct or safe.
How do AI agents use memory?
Agents may maintain information about previous actions, task progress, retrieved evidence, or user instructions.
This information can help prevent unnecessary repetition and support continuity across multiple steps.
However, memory can also introduce errors if outdated, irrelevant, or incorrect information influences later actions.
Researchers should distinguish short-term task state from persistent memory across sessions.
They should also document whether memory is controlled, editable, or accessible to users.
In research applications, persistent memory may raise confidentiality and data-protection concerns when it contains unpublished findings or participant information.
Can AI agents use external tools?
Yes. Tool access is a common feature of contemporary agentic systems.
An agent may search scholarly databases, read files, execute statistical code, query APIs, or modify authorized documents.
These capabilities can extend what the underlying language model can accomplish.
However, they also introduce dependencies on external systems and create opportunities for unintended actions.
Researchers should distinguish the model's generated instructions from the actual operations performed by tools.
The principles of evaluating AI tool access remain relevant when studying agentic systems.
What makes an AI research agent different from an ordinary research chatbot?
An ordinary research chatbot may help formulate questions, explain methods, or suggest literature search strategies.
An AI research agent may be designed to execute parts of those workflows through connected tools.
For example, it might retrieve article metadata, identify duplicates, populate a screening table, or run a predefined analysis script.
However, performing these actions does not automatically establish that the agent can conduct valid research independently.
Research requires methodological judgment, evidence verification, ethical compliance, and accountability.
Agentic execution should therefore be distinguished from scientific authority.
Can AI agents perform research tasks autonomously?
Some agents can perform selected research tasks with limited human intervention.
Examples may include document retrieval, metadata extraction, file organization, and execution of approved analytical procedures.
However, the reliability of these tasks depends on the system's design, available tools, permissions, and evaluation conditions.
More complex activities, such as interpreting ambiguous findings or making consequential methodological decisions, may require substantial human oversight.
The question of autonomous AI research tasks should therefore be examined at the level of specific capabilities rather than assumed from the agent label.
What is the difference between an AI agent and workflow automation?
Traditional workflow automation follows predefined procedures, such as executing a fixed sequence of operations when a specified condition is met.
An AI agent may exercise greater discretion in interpreting goals, selecting actions, or adapting to intermediate results.
However, the distinction is not always sharp.
Some agentic systems operate within highly constrained workflows, while conventional automation can incorporate conditional branching and complex decision rules.
Researchers should examine the actual control logic and degree of action-selection flexibility rather than assume that agentic systems are necessarily more sophisticated.
What is a multi-agent system?
A multi-agent system involves multiple agents that interact or coordinate to perform tasks.
For example, one agent might retrieve research articles, another might extract study characteristics, and a third might evaluate whether extracted information satisfies predefined criteria.
Such arrangements can distribute tasks across specialized components.
However, multiple agents do not automatically produce more accurate results.
Errors may propagate between agents, coordination can introduce additional complexity, and several agents may reproduce similar biases.
Researchers should evaluate the complete system rather than assume that division of labor guarantees reliability.
What are the principal risks of agentic AI?
Agentic systems may create risks beyond those associated with generating incorrect text.
An agent with file-writing permissions could modify research records. An agent with external communication access could send information to unintended recipients.
An agent executing code could alter datasets or produce outputs based on incorrect assumptions.
These risks depend on the system's permissions and operating environment.
Researchers should distinguish informational errors from actions that change external systems.
Risk management may require restricted permissions, logging, approval checkpoints, and procedures for responding to unintended actions.
Why are human approval checkpoints important?
Human approval checkpoints allow designated individuals to review proposed actions before execution.
For example, an agent may be allowed to prepare a revised dataset but prohibited from overwriting the original without approval.
Another agent may draft an email to research participants but require human authorization before sending it.
Approval checkpoints can reduce the risk of consequential actions, although they do not automatically guarantee effective oversight.
Reviewers need sufficient information, authority, and opportunity to assess the proposed action.
The relevance of human approval checkpoints for research agents increases when actions are difficult to reverse.
Who is responsible when an AI agent makes a mistake?
Responsibility does not automatically transfer to the AI system because it selected or executed an action.
Human researchers, institutions, developers, and service providers may have different responsibilities depending on their roles and applicable rules.
For example, a researcher authorizing an agent to modify a dataset remains responsible for following relevant research-integrity procedures.
However, determining legal liability or contractual responsibility may require examination of applicable laws, agreements, and institutional policies.
Researchers should avoid treating an AI agent as an accountable research author or independent professional merely because it performs multi-step tasks.
How should researchers evaluate AI agents?
Agent evaluation should examine more than the quality of the final conversational response.
Relevant outcomes may include task completion, action correctness, tool-use reliability, resource consumption, error recovery, and compliance with permitted actions.
Researchers should also consider whether the agent's actions can be reconstructed from available logs.
For consequential workflows, evaluation may include whether human approval was obtained before restricted operations.
The study should distinguish model-generated plans from actions actually executed.
A system that describes a correct research workflow has not necessarily demonstrated the ability to perform that workflow reliably.
How should agent autonomy be reported?
Researchers should describe the agent's objectives, available tools, action permissions, memory configuration, stopping rules, and human oversight arrangements.
They should identify which decisions were made automatically and which required human intervention.
Where feasible, relevant tool calls and execution outcomes should be preserved.
Claims about autonomy should be limited to the tested environment and permissions.
An agent operating independently in a simulated environment may not be suitable for unrestricted deployment in real research systems.
Clear operational reporting is more informative than describing a system simply as autonomous or intelligent.
