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What changes when a model can take actions?
Text generation is only one part of an application. A chatbot can answer a question, summarize a document, draft an email, or make one bounded retrieval call. The person normally decides what happens next. An agent places the language model inside a controlled execution loop: the model interprets an objective, chooses an available operation, receives the result, decides whether another operation is needed, and either continues or stops.
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That change turns the model from a responder into a decision-maker within the application’s permitted boundaries. A tool might read a customer record, update a ticket, query inventory, send a message, interact with a website, or transfer work to another software component. The important property is not the existence of tools. It is that the model controls the sequence and timing of steps under rules, permissions, and oversight.
OpenAI’s A Practical Guide to Building AI Agents puts the boundary plainly: applications that integrate language models but do not use them to control workflow execution—such as simple chatbots, single-turn model calls, or sentiment classifiers—are not agents. NIST’s glossary describes an agent more broadly as software that interacts with its environment and undertakes self-directed actions toward an externally specified goal. These definitions overlap on the practical point: an agent must be able to advance work, not merely describe what a person could do.
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Chatbot, fixed workflow, and agent: three different designs
“Agent” is not a universally applied product label. Some vendors use it for a long-running autonomous system; others use it for a tightly prescribed implementation with a few model-directed steps. The most useful comparison is architectural: who controls the next step, how much can change at runtime, and what the system is allowed to do.
| System | Who controls the steps? | Best fit | Main trade-off |
|---|---|---|---|
| Chatbot or single model call | The user supplies the next prompt. The model answers or completes a bounded task and may make an individual retrieval call. | Questions, drafting, summarizing, and retrieval where a person remains responsible for advancing the work. | Limited independent follow-through. A tool call by itself does not establish agent behavior. |
| Fixed workflow | Predefined code paths, rules, and branches determine the sequence. | Predictable, repeatable operations where consistent execution matters. | Less adaptable when inputs, conditions, or exceptions change. |
| Agent | The model decides what to do next and selects among permitted tools while pursuing a goal. | Ambiguous tasks, context-dependent decisions, and multi-step work that must adapt to findings. | More complexity, possible latency and cost, and greater consequences when a tool is misused. |
A system can combine all three patterns. A deterministic program may validate an order, an LLM may classify an unstructured note, and an agent may handle only the exceptional cases. Calling the whole application an agent can hide these important differences, so evaluate each part of the workflow.
How an agent loop works
A useful mental model is a repeated observe–decide–act cycle. The exact implementation varies, but a production agent normally needs the following stages.
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- Interpret the situation. The model extracts facts, identifies missing information, and determines whether the request is within scope.
- Choose a next step. It selects a tool, asks a clarifying question, follows a deterministic branch, or decides that no action is required.
- Execute within permissions. The application, not the model alone, enforces authentication, authorization, input validation, rate limits, and approval requirements.
- Observe the result. The tool returns structured data, an error, or a request for additional information.
- Revise and continue. The model uses the result to select another step, correct a failed action, or change its plan.
- Recognize completion or stop safely. It reports the result when the goal is met, halts when it cannot proceed, or transfers control to a person.
OpenAI’s practical guidance treats completion detection, correction, halting on failure, and handoff as agent capabilities. They are design requirements rather than guarantees: a model can still misunderstand a result or claim success prematurely, so the surrounding application must verify important outcomes.
A simple example
Suppose a support representative asks, “Find the customer’s recent delivery issue and resolve it.” A chatbot might summarize the customer’s messages and suggest a reply. A fixed workflow might always look up the order, check a delivery-status field, and offer one predefined remedy. An agent could inspect the account, identify which order is relevant, query the carrier, compare the result with refund policy, draft or send an approved response, update the ticket, and hand the case to a person if the policy does not cover the situation. The agent’s distinguishing behavior is its choice of sequence based on what it discovers.
Why tool access alone does not make a chatbot an agent
Many chat interfaces can call search, retrieval, calculation, or application functions. A single model response that invokes one function and returns the result is still compatible with a chatbot or bounded LLM feature. The decisive questions are:
- Does the model determine which step comes next, or does application code always determine the sequence?
- Can it continue through multiple steps without a new user prompt?
- Does it use each result to revise its next decision?
- Can it recognize that the task is complete, blocked, or unsafe?
- Can it hand the matter back to a human at a defined boundary?
If the user must direct every next operation, the model is assisting a person rather than managing the workflow. If code follows a fixed series of calls and the model only fills one field, the design may be an LLM-enabled workflow rather than an agent. These are not quality judgments; a simpler design is often more predictable.
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When a fixed workflow is better
Anthropic’s engineering guidance distinguishes workflows—LLMs and tools orchestrated through predefined code paths—from agents that dynamically direct their process and tool use. It recommends choosing the simplest solution that fits the task. A fixed workflow is usually preferable when the inputs, decisions, and outputs are known in advance.
- Repeatable sequence: Every request follows the same validated steps.
- Strict compliance: A policy requires an auditable, deterministic path.
- Low tolerance for side effects: An accidental message, deletion, payment, or record change would be costly.
- Stable data: Required fields and system responses have predictable formats.
- Easy exception handling: A small, known set of branches covers unusual cases.
An LLM can still perform one task inside that workflow—for example, extracting fields from an unstructured email—without making the entire process agentic. This hybrid approach often keeps high-impact actions deterministic while using a model where interpretation is genuinely useful.
When an agent is justified
OpenAI’s guidance points to complex decisions, difficult-to-maintain rules, and heavy reliance on unstructured data as situations where agents can help. The common factor is not novelty; it is that a rigid sequence would require too many brittle branches or would fail when conditions change.
- Ambiguous objectives: The request describes an outcome rather than a fixed procedure.
- Changing evidence: The next action depends on information discovered during the task.
- Many possible tools: The system must choose among records, search, calculators, business applications, or specialist services.
- Long-running work: The task may pause, retry, or require a human handoff.
- Unstructured inputs: Emails, documents, conversations, and webpages contain the facts needed to decide what to do.
Agents do not automatically improve accuracy, reduce costs, or save time. Anthropic notes that they can trade latency and cost for task performance, and the cited guidance does not establish a universal performance gain. Compare a proposed agent with a deterministic or hybrid alternative on the actual task.
Compare systems by behavior, not by the label
Before selecting an architecture, score the real workflow against these dimensions.
| Dimension | Questions to ask |
|---|---|
| Workflow control | Who decides the next step: the user, application code, or the model? |
| Adaptability | Must the sequence change when new facts appear, or are the branches known? |
| Tool breadth | Which systems can be read or changed, and can the model choose among them? |
| Permission scope | What data and operations are available, and are they limited to the task? |
| Action impact | Are outcomes reversible, externally visible, financial, legal, or privacy-sensitive? |
| Human review | Which steps require approval, and how does a person pause or take over? |
| Reliability | How are tool results validated, failures retried, and completion confirmed? |
| Latency and cost | How many model and tool calls are acceptable for the value of the task? |
Do not infer autonomy or safety from a marketing name. Inspect the actual loop, permissions, approval controls, logging, and failure behavior of the implementation.
What an agent can do—and what remains outside its authority
With suitable integrations, an agent can retrieve data, update records, send messages, interact with websites, or delegate to another agent. Those capabilities are bounded by the tools exposed to it. A model cannot legitimately perform an operation for which the application has not granted a credential or endpoint, and a tool should enforce authorization independently of the model’s instructions.
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The model also does not become a legal or organizational decision-maker merely because it can call an API. Policies, contractual obligations, and accountability remain with the people and organization operating the system. The application should make the boundary explicit: which actions are suggestions, which are drafts, which are reversible updates, and which require a named approver.
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Build an agent in controlled stages
A practical implementation starts with the task and its risk, not with an agent framework.
- Define the outcome. Write what “done” means, what evidence proves completion, and which conditions require a handoff.
- Map the current process. List inputs, decisions, tools, data stores, side effects, and known exceptions. Mark steps that can remain deterministic.
- Choose the smallest model role. Use a single model call for a bounded interpretation step; add an agent loop only where dynamic planning is needed.
- Design narrow tools. Give each tool one clear purpose, explicit parameters, predictable errors, and the minimum data and write access required.
- Separate planning from execution. Have the model produce a structured action proposal, then let application code validate permissions, schemas, and policy before calling the tool.
- Set stop conditions. Define maximum steps, time limits, retry rules, uncertainty thresholds, and conditions that always require human review.
- Test adversarially. Include malformed inputs, contradictory records, unavailable services, prompt-injection attempts, duplicate requests, and partial failures.
- Deploy with observation. Log the goal, selected tools, validated arguments, results, approvals, errors, and final outcome while protecting sensitive data.
A structured intermediate result can limit uncontrolled text passing between steps. For example, an internal action proposal might contain {"tool":"ticket_update","arguments":{"status":"pending_review"},"reason":"policy exception"}. The application should validate that object against an allow-list and policy before execution; the example is a design pattern, not permission to update any particular system.
Safety risks increase with every external action
Action creates consequences that a text-only response does not. OpenAI’s safety guidance identifies prompt injection as untrusted text or data attempting to override the agent’s instructions. An injected webpage, email, document, or ticket could try to make the system reveal private information, call an unintended tool, or ignore a policy. Even without an attacker, an agent may hallucinate a fact, misunderstand a request, or issue an unauthorized refund or deletion.
Limit access
Expose only the tools and data needed for the task. Separate read and write operations, use least-privilege credentials, constrain records by tenant or case, and make destructive operations unavailable unless they are explicitly required.
Keep untrusted content in the right channel
Do not place user-supplied or retrieved text into privileged developer instructions. Treat external content as data to analyze, not as authority. Clearly identify which instructions are trusted policy and which text is merely an object of inspection.
Constrain data between steps
Use structured outputs, schemas, enumerated values, and length limits instead of passing unrestricted prose from one tool or agent to another. Validate types, identifiers, destinations, and amounts in application code.
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Make policy concrete
State expected behavior and edge cases with examples. Tell the agent what to do when records conflict, a tool times out, a user asks for an out-of-scope operation, or confidence is insufficient. A vague instruction to “be careful” is not an enforceable control.
Require approval where impact warrants it
Human approval is appropriate for actions that expose sensitive data, change access, send external communications, move money, delete information, or create legal or reputational commitments. OpenAI’s safety guidance specifically recommends approvals for MCP tool operations. Approval should show the proposed action and relevant evidence, not merely present an opaque “allow” button.
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Preserve a stop and handoff path
The agent must be able to pause, explain what is missing, and return control when it is blocked or uncertain. Operators need a way to cancel queued work and revoke credentials without waiting for the model to cooperate.
These safeguards reduce risk but do not make an agent infallible. The approval boundary should reflect the action’s reversibility and consequences, not a generic autonomy setting.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reliability: verify the work, not just the explanation
A fluent final message is not proof that an action succeeded. For each important operation, record the request, validated arguments, authorization result, tool response, and any approval. Confirm state changes by reading back the authoritative record when feasible. Distinguish a completed action from a drafted recommendation, a queued operation, a failed attempt, and an unknown outcome after a timeout.
Design retries carefully. A read-only lookup can often be retried; a payment, message, or record update may need an idempotency key or a post-action check to avoid duplicates. Set a maximum number of model-tool turns and a time budget so that a confused loop cannot run indefinitely. Route repeated failures to a person with the evidence needed to continue manually.
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Latency and cost trade-offs
An agent may make several model calls and tool calls where a chatbot would make one response. Each additional turn can add latency, compute cost, network failure risk, and opportunities for an incorrect decision. Long-running tasks may also require state storage, scheduling, and monitoring.
A fixed workflow can be cheaper and faster when its branches cover the real requirements. A hybrid can reserve model calls for ambiguous inputs and keep routine actions deterministic. An agent is easier to justify when the value of adapting to changing evidence exceeds the cost and risk of the extra loop. Compare complete workflows—including human review and exception handling—not just the price of an individual model call.
Human control is part of the architecture
“Human in the loop” is not a single feature. Decide whether people approve before an action, review after a low-risk action, or take over only when the agent reaches a defined exception. Document who can approve, what evidence they see, how long a request can wait, and what happens if no one responds.
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A practical decision checklist
Use these questions before calling a feature an agent:
- Is there a specific outcome that can be checked?
- Does the model need to choose among multiple next steps?
- Will new information change the sequence?
- Can the task be expressed as a small, maintainable set of deterministic branches?
- What is the worst plausible unintended action?
- Which tools and records are strictly necessary?
- Where must a person approve, inspect, or take over?
- How will the system detect completion, failure, duplication, and uncertainty?
- What are the maximum time, step, and cost limits?
- What evidence will show that the design is better than a simpler alternative?
Bottom line
An AI agent is not simply a chatbot with a button that calls a tool. It is a system in which the model can direct a multi-step workflow toward a goal, choose permitted actions, use their results to decide what happens next, and stop or hand control back under defined safeguards. Use a chatbot for bounded assistance, a fixed workflow for predictable execution, and an agent only where ambiguity and changing conditions justify the additional complexity. Restrict permissions, validate every action, protect against untrusted instructions, and make human approval and takeover explicit wherever failure would matter.
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