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Chatbots primarily generate replies; AI agents use tools and workflow logic to pursue a goal across multiple steps. That distinction is functional, not a product label. Either system may search, read files, or call an application when those connections are configured. The practical boundary is set by available data, tool permissions, runtime, state, and approval controls.
This guide shows how to compare the system in front of you, what each can access, which actions create real-world effects, and how to limit risk before granting access.
The essential difference: conversation versus controlled workflow
A chatbot is usually organized around a conversation turn: you provide a prompt, it uses the context supplied to it, and it returns an answer. It may maintain a conversation history, retrieve information, or call a tool, but the interaction is commonly centered on producing the next response.
An agent is organized around an objective. It can decide which step to take next, select among available tools, inspect the result, continue or retry, and determine whether the task succeeded or failed. OpenAI’s A Practical Guide to Building Agents defines the key test this way: applications that integrate language models but do not use them to control workflow execution—including simple chatbots and single-turn language-model applications—are not agents.
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That definition does not mean every chatbot is passive or every agent is unrestricted. A conversational product can expose tool calling and behave agentically for a particular request. An agent can be deliberately limited to read-only tools and require a person to approve every change. Describe the configured capabilities rather than assuming the name on the screen tells you what the system can do.
A capability comparison
| Dimension | Chatbot pattern | Agent pattern |
|---|---|---|
| Primary objective | Answer a question, generate content, or guide a conversation | Pursue a goal through a sequence of decisions and actions |
| Workflow control | Usually the user chooses the next turn | The model or orchestration layer selects the next step within configured rules |
| Information source | Prompt, conversation history, supplied files, and any enabled retrieval | Those sources plus tools, connected systems, and task state that the runtime exposes |
| Tool use | May call a search, calculator, code runner, or connector for a response | Can select tools repeatedly, pass results between steps, and use the outcome to decide what to do next |
| Action scope | Often read or generate information; changes depend on enabled tools | Can read data or change records, files, settings, messages, or transactions when write-capable tools are granted |
| Continuity | Conversation history may persist while the chat is open or saved | Designed to retain task state, wait for input or approval, and resume a multi-step job when the runtime supports it |
| Completion | Ends when it sends a response or the user asks another question | Uses a completion or failure condition and can report intermediate results, blocked steps, or a final outcome |
| Controls | Account, workspace, connector, and per-action settings still apply | Those controls plus tool scopes, approval gates, action constraints, monitoring, and suspension mechanisms |
What a chatbot can access
Prompt and conversation context
At the narrowest configuration, a chatbot can access only the text, images, or other content you send and the conversation context the application chooses to retain. It cannot infer permission to a company drive, mailbox, or database merely because you mention one. The application must provide the relevant data to the model.
Uploaded files and retrieval
Many chat interfaces can analyze files that you explicitly upload. Some can search an indexed knowledge base or retrieve documents selected by an application. Those are additional data paths, not an automatic view of every file associated with your account. Check which repositories are indexed, how freshness is handled, and whether retrieval is limited to your identity or workspace.
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A chatbot may call a search service, calculator, code interpreter, or application connector. In that case it can use whatever result the tool returns, subject to the tool’s own authentication and filtering. Tool calling alone does not turn every conversation into an autonomous agent: the key question is whether the language model controls a multi-step workflow or simply invokes one function to answer the current turn.
Connected applications
When a connector is configured, the chatbot may read permitted messages, documents, records, or calendar data. The connector’s account permissions and workspace policy determine what is actually visible. Instructions in a prompt cannot grant access to an application that has not been connected, and they cannot override the source system’s access rules.
What an AI agent can access
Configured tools and runtime capabilities
An agent’s access is the union of the tools made available to its runtime and the permissions those tools use. OpenAI’s Agents documentation describes several runtime arrangements: a managed service can run long-lived tasks and preserve progress; an application can use an Agents SDK to define custom tools and workflows; or developers can call a model directly and build their own orchestration with the Responses API. Each arrangement controls where execution occurs, how state is kept, and how tools are run.
Read access and write access are different
A read-only tool can retrieve a document, list a record, or inspect a status. A write-capable tool can create, edit, delete, send, purchase, publish, or change configuration. NIST’s discussion of tool use emphasizes that these possibilities depend on permissions and the action environment: a tool exposed in a sanitized workspace is not equivalent to one with open-internet access, and a read operation does not carry the same consequence as a state-changing operation. Treat the two classes separately when reviewing an agent.
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An agent may retain a plan, intermediate outputs, tool results, and pending approvals so it can continue later. Whether that state is durable, isolated per user, shared with a team, or discarded after a run is a runtime design choice. Ask where state is stored, who can inspect it, how long it remains, and what happens if a run is interrupted.
Connected accounts and shared identities
Workspace agents can be connected through an individual end-user account or an agent-owned shared account. The Workspace Agents help documentation recommends using a service account where possible and limiting access to what the agent needs. A shared identity can make scheduled work reliable, but it also concentrates authority: its mailbox, files, and application roles must be reviewed as carefully as a human administrator’s account.
What each system can do in practice
Typical chatbot work
- Explain a concept, summarize supplied material, or answer questions from an approved knowledge source.
- Draft, rewrite, translate, classify, or transform text and code.
- Analyze an uploaded spreadsheet or document and return findings.
- Use a permitted search or calculation tool to improve a single response.
- Guide a person through a process while the person performs the actual changes.
These activities can still be consequential if the answer is wrong or the source data is sensitive. The important characteristic is that the chatbot’s output is generally the deliverable; a person or another system decides whether and how to apply it.
Typical agent work
- Collect information from several connected systems, reconcile it, and produce a report.
- Monitor a queue, classify new items, route them, and continue until a defined condition is met.
- Draft a change, request approval, and apply it only after the approval gate passes.
- Run code or queries, inspect results, retry a failed step, and record the outcome.
- Perform scheduled or event-triggered workflows without a person present for every step.
The same examples can be implemented as a guided assistant rather than an autonomous agent. A product comparison is therefore incomplete unless it states whether the system can select tools, loop across steps, retain state, and commit changes.
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How an agent executes a multi-step task
The following sequence is a useful mental model for evaluating an agent, regardless of vendor or interface.
- Receive the goal and constraints. The system gets an objective, relevant context, allowed tools, limits, and a definition of success. A vague goal produces more room for interpretation than a narrowly scoped request.
- Inspect available context. It may retrieve a document, query a record, or read a prior step’s output. The runtime determines whether this inspection is local, connector-based, or internet-facing.
- Select a tool or next action. The model chooses from the tools exposed by the application. Tool descriptions and permission scopes constrain what can be selected; a prompt does not create a new capability.
- Execute within the tool’s identity. The action runs under a user, service account, or agent-owned account. The source application still enforces its own authorization and policy.
- Evaluate the result. The agent can compare the output with the goal, detect an error, ask for missing information, or choose another tool. This feedback loop is what makes the workflow more than a one-shot function call.
- Request approval when required. A policy may pause before sending a message, changing a record, publishing content, or performing another consequential operation. The agent should expose what it plans to do and what data it will use.
- Continue, retry, or stop. A bounded retry can recover from a transient failure; an unbounded loop can waste resources or repeat an unsafe action. Good designs specify limits and a failure path.
- Record the outcome. The runtime can save task state, tool results, approvals, and an audit trail so a person can understand what happened and resume or revoke the workflow.
Permissions decide the real boundary
Connector permissions are not a connection
OpenAI’s app-permission documentation explains that permission settings govern what the service may do after a connected account exists. They do not themselves connect an account, bypass the source application’s access rules, or override a workspace policy. Availability and settings vary by app and account.
Approval modes
Depending on the connector and configuration, a system may ask before reading or changing data, allow reads while asking before writes, automatically approve selected low-risk actions, or permit supported actions without an extra prompt. Treat these as policy choices, not universal chatbot-versus-agent properties. A read approval does not imply write approval, and a prompt asking for confirmation is not a substitute for least-privilege account design.
Scope an agent-owned account
For a shared or service identity, grant only the repositories, folders, records, and operations required for the workflow. Connector constraints can narrow an agent to a particular document, limit email recipients, or restrict other destinations. Test the narrow scope with a harmless read before enabling any write operation.
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Separate user authority from agent authority
An agent that acts through a personal account may inherit permissions that change when the employee changes roles. An agent-owned account gives administrators a stable identity to audit, rotate, suspend, and revoke. Whichever model you use, document who owns the account, who approves new tools, and how access is removed when the workflow is retired.
Safety risks when a system can act
Prompt injection from external content
Content an agent reads can contain instructions aimed at the agent rather than the task. OpenAI’s ChatGPT Agent System Card discusses prompt injection as a source of possible data exposure, unintended actions, or incorrect answers for that specific system. The risk is not limited to malicious web pages; an ordinary document, email, ticket, or comment can carry text that attempts to redirect the workflow.
Data exposure
If a tool can read confidential data and another tool can send messages or upload files, a compromised step may move information across boundaries. Limit the combinations of read and write tools, filter sensitive fields, and keep high-value repositories out of the agent’s scope unless they are essential.
Incorrect or repeated actions
An agent can misunderstand an instruction, act on stale data, select the wrong record, or retry a state-changing operation. Idempotent APIs, dry-run modes, transaction limits, duplicate detection, and explicit confirmation reduce the chance that a transient error becomes a repeated change.
Monitoring and recovery
Log tool calls, inputs, outputs, approvals, identity, timestamps, and failures. Alert on unusual destinations, volume, or action types. Provide a way to pause scheduled runs and revoke connector credentials quickly. A workflow is not controlled if administrators cannot see what it did or stop it.
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Current runtime choices and product examples
OpenAI’s API documentation presents three broad implementation choices. They illustrate deployment trade-offs rather than a universal taxonomy.
| Implementation | Where workflow runs | State and orchestration | When it fits |
|---|---|---|---|
| OpenAI-managed Agents API | Managed by the service | Designed for long-running tasks with saved progress | Teams that want hosted execution and persistence without building the entire runtime |
| Agents SDK | Inside the developer’s application | Application defines custom tools, workflow logic, and state handling | Products that need their own connectors, policies, and user experience |
| Responses API with custom orchestration | Controlled by the calling application | Developers manage loops, state, tool execution, and completion rules | Direct model calls or specialized orchestration with maximum application control |
Features and availability change, so verify the live Agents documentation before designing around a particular endpoint.
Workspace agents in ChatGPT
In an October 13, 2026 announcement, OpenAI described workspace agents that can use connected apps, code, memory, and multiple workflow steps, with scheduled runs or deployment through Slack. Organizations can choose tools and data, require approval for sensitive steps, and apply administrative controls. The announcement and the accompanying Help Center article are examples of one vendor’s current offering, not a definition of every agent. Product names, supported apps, regional availability, and controls can change.
A practical comparison checklist
Use these questions when evaluating a chatbot, copilot, or agent. Ask the product owner for concrete answers rather than relying on labels.
- Goal: Does it answer one request, guide a person, or pursue a multi-step objective?
- Data: What can it see—prompt text, uploads, indexed content, search results, live applications, or task memory?
- Identity: Which user, service account, or agent-owned account performs each tool call?
- Read scope: Which repositories, records, fields, messages, and destinations are visible?
- Write scope: Can it create, edit, delete, send, publish, purchase, or change settings?
- Autonomy: Can it select tools, continue after a result, retry, schedule itself, or wait for an event?
- Approval: Which actions pause for a human, and can administrators change that policy?
- Environment: Does code run in a sandbox, a controlled workspace, or an environment with open internet access?
- State: Where are plans, intermediate data, credentials, and logs stored, and how long are they retained?
- Oversight: Can you inspect tool calls, stop a run, revoke access, and recover from a mistaken change?
Choosing the right pattern
Use a chatbot when the person should remain in control
A chatbot is usually the better fit for explanation, drafting, brainstorming, document analysis, and decision support when a person will review and apply the result. Keep external actions outside the model or require the user to execute them manually. This limits the blast radius while preserving the speed of natural-language interaction.
Use a restricted agent for repeatable coordination
An agent is useful when a task spans several systems or steps and the sequence is sufficiently defined to automate. Start with read-only retrieval, narrow data sources, bounded loops, and a visible report. Add a single write operation only after the read path is reliable and auditable.
Use a human approval gate for consequential changes
Require confirmation before financial transactions, external messages, publication, deletion, permission changes, or any operation that is difficult to reverse. Show the exact target, fields, recipients, and source data in the approval request. Automatic approval may be reasonable for low-risk, reversible actions, but the policy should name those actions explicitly.
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- List the business outcome and the exact steps required to reach it.
- For every step, mark whether it needs no data, read access, or write access.
- Choose the narrowest connector, account, repository, and environment that can satisfy the step.
- Define success, failure, retry limits, timeouts, and the person responsible for intervention.
- Decide which actions require approval and what evidence the approver must see.
- Run tests with synthetic or low-sensitivity data, including malformed input and hostile instructions embedded in documents.
- Review logs and attempted tool calls, then remove any tool or permission the workflow did not need.
- Document suspension, credential rotation, rollback, and incident-notification procedures before scheduling the workflow.
The result of this exercise is more informative than calling a product a chatbot or an agent. Two systems with the same conversational interface can have entirely different access and consequences, while two products with different labels can expose the same read-only workflow.
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