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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesShort answer: A chatbot is primarily a conversational interface, while an AI agent uses a language model to choose tools and control a multi-step workflow toward a goal. Neither label tells you what the system can actually reach. Connected accounts, runtime design, permissions, approval rules and workspace policy determine whether it can only use your prompt, retrieve information, change records, send messages or run scheduled work.
What is the difference between a chatbot and an AI agent?
A chatbot receives a message and produces a conversational response. The simplest version has no tools: it generates an answer from the text in the conversation and any model knowledge available to it. More capable chatbots can accept files, search a service or call a function, but the conversation remains the main control surface.
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An agent is defined by how it controls work, not by a product name. OpenAI’s A Practical Guide to Building Agents says: “Applications that integrate LLMs but don’t use them to control workflow execution—think simple chatbots, single-turn LLMs, or sentiment classifiers—are not agents.” In an agent, the model helps decide which step to take, which tool to use, whether the result is sufficient and what to do next. It can recognize completion or failure and continue, retry, ask a person or stop.
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The boundary is functional rather than absolute. A conversational product can expose agent workflows, and an agent can be intentionally restricted to read-only tasks. To compare two systems accurately, inspect their configured tools, data connections, execution environment and controls instead of assuming that every chatbot is passive or every agent is fully autonomous.
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What can either system access?
Access comes from the system around the model. The model does not gain access to an account merely because a user asks for it in a prompt. An application must connect a data source or tool, authenticate it and allow the relevant operation. OpenAI’s Agents documentation describes runtimes in which tools gather context or perform actions; those tools may be hosted by the service, controlled by your application or invoked through direct model calls.
Prompt and conversation context
Both a basic chatbot and an agent can see the messages, instructions and files deliberately supplied in the current interaction, subject to the product’s retention and context limits. This is the narrowest access boundary: the system can reason about what you provide but cannot inspect an unrelated mailbox, drive or database unless a connector exposes it.
Uploaded files and indexed data
A product may let you upload documents or search an index prepared by your organization. That is different from unrestricted access to the original repository. File-level permissions, indexing scope, freshness and deletion policies determine what can be retrieved. A chatbot might answer a question about a supplied PDF; an agent might search several approved documents, extract fields and write a result to a case system if those tools are configured.
Connected applications
Connectors can expose email, calendars, customer records, tickets, code repositories, storage or other business systems. The connector’s account and scopes define the boundary. OpenAI’s permission documentation emphasizes that app permissions govern what the product can do after an account is connected; they do not connect the account, override the source system’s access rules or bypass workspace policy.
Runtime and network environment
An agent running in a hosted service may have a different network, filesystem and credential boundary from an agent embedded in your application. NIST’s discussion of tool use notes that capabilities depend on the action environment: a tool may be read-only, state-changing, connected to the open internet or limited to a sanitized environment. Two systems using the same model can therefore have very different practical access.
What a typical chatbot can do
Answer and explain
A chatbot can summarize supplied text, explain a concept, draft content, translate, classify messages and answer questions within its available context. These tasks usually end after one response, although the user can continue the conversation and provide corrections.
Follow a guided conversation
Many chatbots collect information through a fixed dialogue: they ask for an order number, look up a status and present the result. The application, rather than the model, may control the sequence. This can feel interactive without meeting the agent definition, because the model is not selecting and managing the workflow.
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Call tools when the product exposes them
A chatbot can search the web, query a knowledge base or call a calculator if its product includes those functions. Tool calling alone does not make it an agent. The key question is whether the model controls a multi-step process, evaluates intermediate results and decides what to do next, or simply fills one request in a predetermined turn.
Where a chatbot usually stops
Unless deliberately extended, a chatbot will not monitor a process, retry a failed operation, coordinate several systems or make a consequential change on its own. It may draft an email but leave sending it to you. It may suggest a database update but not execute it. Those boundaries are design choices, not guarantees supplied by the word chatbot.
What an AI agent can do
Decompose a goal into steps
An agent starts with an outcome such as preparing a weekly operations report. It can determine that it needs to retrieve data, calculate changes, check for missing values, create a document and notify a team. The exact plan may vary with the request and the tool results.
Select and sequence tools
The agent can choose among approved functions, pass outputs from one step to another and maintain state while the task runs. For example, it might search a ticket system, open the linked incident, compare deployment records and create a draft status update. Each action still depends on the permissions granted to the connected tools.
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Workflow logic can let an agent retry a transient failure, take an alternate path when data is missing, recognize that a goal is complete or pause for a human decision. A well-designed agent records which step failed and avoids repeating a completed write. Poorly constrained logic can instead loop, make an incorrect assumption or act on stale information.
Change external state
With write-enabled tools, an agent may create or edit files, update records, change settings, send messages or initiate transactions. Read access and write access have different consequences. NIST’s tool-use guidance treats the action environment and permission set as part of the capability definition, so “the agent can access the system” is not precise enough; you must specify which operations are allowed.
Chatbots and agents compared by capability
| Dimension | Chatbot | AI agent |
|---|---|---|
| Primary interaction | Conversation and response generation | Goal-directed workflow that may include conversation |
| Workflow control | Usually a single turn or application-defined dialogue | Model can choose steps, tools and stopping conditions |
| Information source | Prompt, uploaded files and any explicitly enabled search or connector | Those sources plus whatever approved tools and data stores the workflow can call |
| State across steps | Conversation history; longer-term state depends on the product | Workflow state, intermediate results and sometimes durable memory, depending on runtime |
| Typical action | Explain, draft, retrieve or recommend | Retrieve, transform and execute a sequence of approved operations |
| Write capability | Often absent or requires an explicit tool and user action | Can be enabled, restricted or approval-gated for each operation |
| Failure handling | User notices an unsatisfactory answer and retries | Workflow can retry, branch, escalate or stop according to its logic |
| Oversight | Review the response before acting | Permission scopes, approvals, constraints, logs and suspension controls are needed |
The table describes common designs, not universal product behavior. A chatbot may have extensive tool access, while an agent may be configured as a read-only researcher.
Permissions define the practical boundary
Account and workspace permissions
The source application remains authoritative. If your account cannot open a document or edit a record, a connected agent should not be able to do so through that account. Workspace administrators can further restrict which apps, domains, files or actions are available.
Read versus write scopes
Separate read permissions from write permissions whenever a connector supports it. A read-only agent can summarize invoices or identify overdue tickets without changing them. A write-enabled agent could alter those records, so it needs narrower scopes, stronger validation and a clear approval policy.
Approval modes
Depending on the app and account, settings can ask before reading or changing data, allow reads automatically while asking before writes, automatically approve selected low-risk actions or permit supported actions without an extra prompt in a specific connection. Availability varies by product and workspace configuration; verify the current setting rather than relying on a generic label.
Dedicated identities and least privilege
OpenAI’s Workspace Agents guidance describes connections using an individual end-user account or an agent-owned shared account. It recommends a service account where possible and limiting access to what the agent needs. A dedicated identity makes ownership, rotation and revocation clearer than using an employee’s broad personal access.
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Connector constraints
Use constraints to narrow otherwise capable tools. Examples documented for workspace agents include limiting email recipients or restricting an agent to a particular document. A prompt instruction such as “only email the finance team” is not a permission boundary by itself; the connector or policy must enforce it.
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How agent runtimes differ
Where the workflow runs determines who owns state, credentials, tool execution and monitoring. OpenAI documents three implementation patterns:
| Runtime | Where it runs | What the developer controls | When it fits |
|---|---|---|---|
| OpenAI-managed Agents API | Managed service | Tools, instructions and task configuration while the service runs long-lived work and saves progress | Teams that want hosted execution and persistence without operating the orchestration layer |
| Agents SDK | Your application | Custom tools, handoffs, workflow logic, state and application-side controls | Products needing bespoke orchestration or integration with existing backend systems |
| Responses API | Direct model calls or your custom orchestration | Each call, tool invocation, state strategy and execution policy | Developers building a narrowly controlled loop rather than adopting a full agent runtime |
These choices are described in the official Agents guide. Product names, availability and features can change, so check the live documentation before selecting an implementation.
What can go wrong when an agent has tools?
Prompt injection in external content
External text can contain instructions aimed at the agent rather than the user’s task. A malicious web page, document or email might tell the agent to reveal secrets or ignore its original objective. The ChatGPT Agent System Card discusses prompt injection as a source of possible data exposure, unintended actions and incorrect answers for that specific system. Treat content retrieved by a tool as untrusted data, not as authority.
Over-broad access
An agent with access to an entire drive, mailbox or production database has more opportunities to expose information or make an incorrect change. Scope connectors to the smallest useful set of files, records, recipients and operations.
Incorrect but authorized actions
Permission checks answer “may this tool perform the operation?” They do not answer “is this particular operation correct?” Validate identifiers, totals, recipients and target environments before a write. Require a person to approve actions that are irreversible, externally visible, financial or legally significant.
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Loops, retries and stale state
Multi-step workflows can repeat an operation after a timeout or act on information that changed while the task was running. Use idempotent operations where possible, record operation IDs, set retry limits and re-check critical state immediately before committing a change.
Monitoring and emergency controls
Keep logs showing the request, tools called, arguments, results, approvals and final status. Provide a way to pause scheduled runs, revoke credentials and disable a connector without deleting the whole workflow. Review logs for unexpected destinations, unusual volume and repeated failures.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Current example: workspace agents
As of October 13, 2026, OpenAI describes workspace agents in ChatGPT as workflows that can use connected apps, code, memory and multiple steps. Organizations can choose tools and data, require approvals for sensitive actions, schedule runs or deploy an agent to Slack, and apply administrative controls. The details of available apps, connection types, constraints, file handling and sharing are in the Workspace Agents Help Center article and the product announcement at Introducing workspace agents in ChatGPT.
This is an example of one vendor’s current offering, not a definition of every agent. A previous Help Center page describing a standalone consumer Agent mode says that mode is no longer available and points readers to newer products: the archived Agent help article. Do not use the older name to infer the capabilities or availability of the current workspace product.
Which should you use?
Choose a chatbot when the outcome is informational
- You need explanations, summaries, drafting, translation or brainstorming.
- A person will review and carry out any external action.
- The task fits one response or a short, predictable dialogue.
- Giving the system access to business applications would add risk without meaningful benefit.
Choose an agent when the work is repeatable and multi-step
- The task requires several approved systems or repeated handoffs.
- Intermediate results determine the next operation.
- You need scheduled execution, persistent state, retries or escalation.
- The time saved justifies the engineering, monitoring and permission overhead.
Use a hybrid design for sensitive workflows
A practical pattern is a conversational front end with a restricted agent behind it. The chatbot collects intent and clarifies ambiguity; the agent performs read-only retrieval and prepares a proposed change; a person approves the final write. This separates natural-language interaction from high-impact execution without pretending that either component is risk-free.
A capability and permission checklist
- Define the goal and completion test. State what success looks like and which outcomes require escalation.
- List every data source. Record whether the system uses prompts, uploads, search indexes, live apps, databases or the open internet.
- Map each tool action. Mark every operation as read-only, reversible write, irreversible write or external communication.
- Assign an identity. Prefer a dedicated service or shared account for production workflows, with ownership and credential rotation documented.
- Apply least privilege. Limit files, records, recipients, environments and API scopes to what the goal requires.
- Set approval gates. Require confirmation for financial, legal, destructive, public-facing or otherwise consequential actions.
- Constrain untrusted content. Ensure retrieved documents cannot silently override system instructions or tool policy.
- Test failure paths. Simulate missing data, expired credentials, duplicate requests, hostile instructions and partial completion.
- Instrument the workflow. Log tool calls, arguments, results, approvals, retries and stop reasons while protecting sensitive log content.
- Plan shutdown and recovery. Document how to revoke access, pause schedules, undo changes and contact the system owner.
Questions to ask before enabling access
- Exactly which account will the system use, and whose permissions will it inherit?
- Can it read private content, and can it write, delete, send or purchase?
- Which operations always require a person, and where is that rule enforced?
- Does the runtime retain state between runs, and how long are logs and memory kept?
- What happens when a tool times out or returns contradictory data?
- Can an administrator see activity and revoke access without waiting for the workflow owner?
- Are current product limits, app availability and regional requirements documented?
Bottom line
Chatbots primarily help through conversation; agents pursue goals by controlling tools and workflow steps. The decisive question is not which label appears in a product menu, but what the configured system can see, which operations its identity is allowed to perform, where it runs, how it handles failure and when a person must approve the next step. Evaluate those concrete boundaries before connecting data or enabling write access.
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