AI agents usually make their next move through a controlled loop: interpret a goal and its context, choose an available action, execute it, inspect what happened, and then update the next decision. The application—not the model alone—defines the instructions, tools, routing rules, permissions and stopping limits that shape this loop.
There is no single, universal “agent algorithm.” Some systems follow fixed workflows written by developers; others let a language model select and sequence actions dynamically. Understanding that distinction makes it easier to choose the right architecture, prevent runaway tool calls and know when human review belongs in the process.
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What an AI agent is—and is not
“Agent” is an architectural label rather than a universally agreed mechanism. Anthropic distinguishes a workflow from an agent: a workflow uses code to orchestrate a language model through predefined paths, while an agent allows the model to direct its own process and tool use dynamically.
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Agents are not unrestricted software operators. They can select only actions that the surrounding application exposes, and those actions are constrained by descriptions, parameter schemas, permissions, routing code and policy checks. The model also does not provide a guaranteed, complete internal plan that developers can inspect. What developers can observe is the sequence of messages, tool calls, results and final responses that the application records.
The next-action loop
A useful mental model has five stages. Real products may combine stages, repeat them, add validation or skip a tool call when a direct answer is sufficient. Treat this as a design model synthesized from documented agent and API behavior, not as a formal algorithm implemented identically everywhere.
1. Interpret the goal and context
The agent first determines what the user is trying to accomplish, what information is already available and which constraints apply. Context can include the conversation, retrieved records, current application state, account permissions, deadlines and explicit instructions such as “do not send anything without approval.”
Interpretation also includes identifying missing information. If a request says “move my meeting to next week” but names no meeting or time zone, the safest next action may be a clarification question rather than a calendar call. A well-designed application can enforce required fields before any external action is permitted.
2. Choose among permitted actions
After interpreting the request, the model selects from the options the application presents. Those options may include replying in text, calling one function, calling several functions, requesting clarification, handing the task to another agent or ending the run.
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The model’s choice is shaped by tool names and descriptions, parameter schemas, system and developer instructions, conversation history and routing logic. If a payment-refund function is not provided, the agent cannot legitimately invoke it. If a function requires an order identifier, the application should reject or pause an incomplete call rather than guessing one.
3. Act with structured arguments
When a tool is needed, the agent emits a structured call. The application validates the arguments, checks authorization and executes the function. The function might search a database, run code, read a file, call an external service or prepare a proposed change.
Tool execution should be treated as a separate trust boundary. A model-generated argument is a request, not proof that the requested operation is safe. Validation can restrict account scope, allowed values, network destinations, write permissions and rate limits before the tool runs.
4. Observe the result
The tool returns evidence: records found, an error, a permission denial, a calculation, a status change or no matching result. That result becomes new context for the next decision. It may confirm that the task is complete, reveal that the chosen approach failed or show that another step is necessary.
Anthropic’s engineering guidance states: “During execution, it’s crucial for the agents to gain ‘ground truth’ from the environment at each step (such as tool call results or code execution) to assess its progress.” See Building Effective AI Agents, published December 19, 2024.
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5. Update, continue or stop
Using the result, the agent can revise its approach, call another permitted tool, ask the user for information, request human review or provide a final response. A failed search should not be silently treated as proof that no record exists; the agent may need to check a different identifier or explain the limitation.
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This loop can run once or many times. The number of turns is a product decision governed by completion criteria and safety controls, not a universal property of “agents.”
How tool choice is bounded by the application
Tool use is not a general “do anything” capability. Developers register functions and describe their inputs, outputs and intended use. The model can then select from that declared set, subject to the API’s tool-choice setting and the application’s own routing and authorization code.
OpenAI’s API reference documents three broad tool-choice modes. Verify the current behavior in the API reference before implementing against it, because API details can change.
| Mode | What it permits | Typical use |
|---|---|---|
none |
Tool calls are disabled for that turn. | Answering from supplied context or enforcing a read-only response. |
auto |
The model may answer directly or call one or more available tools. | Normal mixed conversations where a tool is useful only when needed. |
required |
A tool call is required rather than a text-only answer. | Flows that must obtain a system result before proceeding. |
These settings do not replace application policy. A “required” call can still be routed to a narrow, read-only function, and a function can still be blocked after the model selects it. Descriptions should state when a tool is appropriate, while schemas should make invalid arguments difficult to produce. For sensitive operations, use a proposal-and-approval pattern: let the agent prepare structured changes, then require a user or service to authorize execution.
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Planning, workflows and autonomy
A fixed workflow is usually preferable when the path is well understood and consistency matters. An autonomous agent is useful when the number or type of steps depends on intermediate results. Anthropic’s guidance recommends matching autonomy to the task and warns that greater autonomy can bring higher cost and compounding errors; those are implementation considerations, not a universal benchmark.
| Decision axis | Fixed workflow | More autonomous agent |
|---|---|---|
| Predictability of steps | Predefined sequence and branches. | Steps adapt to tool results and changing task needs. |
| Control and observability | Orchestration is explicit in application code. | Some sequencing is selected at runtime and requires detailed tracing. |
| Adaptability | Unexpected results usually require a coded branch. | The model can select another permitted action or ask for clarification. |
| Cost and error exposure | Often easier to bound calls and failure paths. | Additional calls can increase cost and compound an early mistake. |
| Human oversight | Review points can be placed at known stages. | Review may be triggered by uncertainty, risk or an iteration limit. |
Many practical systems combine both approaches: deterministic code handles authentication, validation and irreversible writes, while an agent handles an uncertain research or triage segment inside those boundaries.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should an agent stop?
An agent can stop when its completion criteria are satisfied, when a control limit is reached or when a human-review condition requires pausing. Anthropic names a maximum number of iterations and a pause for human feedback as examples of stopping conditions; it does not establish one optimal limit or universal completion test.
Define completion before deployment
Write a testable definition of “done.” For a support task, that might mean locating the correct ticket, recording a factual answer and presenting the response for approval. “The model sounds confident” is not a completion criterion. Completion should be tied to observable state or an explicit user confirmation.
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Set a maximum number of model-tool cycles, a time budget and per-tool rate limits. If the limit is reached, preserve the trace and return a clear explanation or escalate rather than silently continuing. A limit is a safety backstop, not evidence that the task succeeded.
Pause for review when impact is high
Require approval before sending messages, changing permissions, spending money, deleting data or publishing content. The agent can gather facts and prepare a proposed action, but a person or deterministic policy can make the final commitment.
Stop on contradiction or missing evidence
If tools return conflicting records, stale data or repeated authorization failures, stop and surface the uncertainty. Continuing to call tools without new information can increase cost while making the eventual answer less reliable.
What can go wrong in the loop?
- Wrong tool selection: Improve names and descriptions, narrow the available tool set for the task and add routing rules for sensitive operations.
- Invalid or incomplete arguments: Enforce schemas, validate identifiers and return actionable errors that tell the agent what information is missing.
- Misread results: Preserve the raw tool response, label errors distinctly from empty results and require confirmation for ambiguous matches.
- Runaway repetition: Track repeated calls and arguments, impose iteration limits and stop when no new evidence is being produced.
- Unauthorized side effects: Separate read and write tools, check permissions at execution time and use approval gates for irreversible actions.
- Stale or changing state: Recheck critical state immediately before a write and report the time or version of information when it matters.
Observability is part of the control system. Log the instruction context that was allowed to influence a decision, selected tool, validated arguments, tool result, policy checks, retries and final outcome. Redact secrets and personal data according to the application’s security requirements.
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A single agent is not the only orchestration pattern. LangChain describes dynamic handoffs in which the active agent changes through a tool call based on conversation context. For example, a general intake agent could transfer an active conversation to a billing specialist after identifying a billing issue.
A handoff should specify what context is transferred, which agent now owns the task, what tools it may use and how control can return. The cited architecture description does not establish comparative success rates or show that handoffs are better than a single agent. Treat handoffs as an option when separation of expertise, permissions or ownership justifies the added coordination.
A practical design blueprint
- State the goal and completion test. Define the desired outcome, required evidence and conditions that mean the task is finished.
- Separate context from instructions. Identify user data, application state, constraints and policies. Do not rely on an unstated assumption that the model will infer a critical rule.
- Expose the smallest useful tool set. Give each function a precise description and strict parameter schema. Keep high-impact tools unavailable until their prerequisites are met.
- Choose routing behavior. Decide when text-only answers are allowed, when a tool is required and when deterministic code should bypass model selection.
- Validate every call. Check types, ranges, identity, authorization, freshness and side effects before execution.
- Return informative observations. Distinguish success, empty results, transient errors, permanent failures and permission denials so the next decision has reliable evidence.
- Set stopping and escalation rules. Configure iteration, time and rate limits, plus human review triggers for uncertainty or impact.
- Trace and test the whole loop. Test normal paths, missing information, contradictory results, tool outages, prompt injection attempts and repeated failures. Review traces for unnecessary calls and unsafe assumptions.
Illustrative example: a support-ticket agent
Suppose a user asks, “What is the status of my replacement?” The application provides a ticket-search tool, a shipment-status tool and a text-response capability. The agent interprets the request and notices that an order or ticket identifier is missing. It asks for that identifier instead of guessing.
After receiving the identifier, it calls the ticket-search tool. The result shows a replacement was approved but contains no carrier scan. The agent observes that the ticket alone cannot answer the delivery question, so it calls the shipment-status tool with the validated tracking number. If the carrier reports no record, the agent explains that limitation and offers the next supported step. If the user instead asks it to cancel the replacement, a separate write tool and approval policy may require explicit confirmation before any change.
The example illustrates why the result, not the initial plan, determines the next action. It also shows how application-defined tools, schemas and stopping rules constrain what the agent can do.
Quick Recap
Key takeaways
- An agent decides what to do next through an interpret–choose–act–observe–update loop, but products implement that loop differently.
- Tool selection is bounded by developer-supplied tools, schemas, instructions, routing and permissions.
- Tool results provide environmental evidence that should change the next decision when necessary.
- Completion criteria, iteration limits and human-review checkpoints make stopping explicit.
- Use fixed workflows for predictable paths and agentic autonomy where adaptation is genuinely needed; greater autonomy can increase cost and error exposure.
- Multi-agent handoffs are one possible coordination pattern, not a universal requirement.
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