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AI agents decide their next action through a controlled feedback loop: they interpret a goal and its context, choose from the actions an application exposes, call a tool or produce an answer, inspect the result, and then decide whether to continue, change course, ask for help, or stop. There is no single universal planner inside every agent. The application’s instructions, tool definitions, routing logic, permissions, and stopping limits shape what the model can do.
What an AI agent is—and what it is not
“Agent” describes an architecture, not one standardized mechanism. Different products may use different prompts, memory systems, planners, tool routers, model calls, and approval rules while still being called agents.
Anthropic draws a practical line between a workflow and an agent. In a workflow, application code orchestrates a language model through predefined paths. In an agent, the language model dynamically directs its process and tool use. The distinction is about who determines the next step: explicit program logic or a model making choices within boundaries set by the application. See Anthropic’s engineering guidance.
A workflow is often the better fit when the task has a known sequence, such as extracting fields, validating them, and writing a record. An agent is useful when the number, order, or type of steps depends on intermediate results—for example, investigating an unfamiliar incident where each observation determines the next diagnostic action. Anthropic also cautions that greater autonomy can increase cost and compound errors, so adaptability is not automatically an improvement.
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The next-action loop
A useful mental model is a five-stage loop. It is a synthesis of documented agent and API behavior, not a formal algorithm that every product implements identically. The model may perform several internal reasoning steps, but the application normally observes only messages, tool calls, results, and final responses.
| Stage | What happens | Typical output |
|---|---|---|
| Interpret | Identify the user’s goal, relevant context, constraints, and success criteria. | An internal task representation or a decision to request clarification. |
| Choose | Select a response, a tool call, or another available step. | A natural-language answer or structured tool-call request. |
| Act | Invoke the selected tool with arguments that match its schema, or return the response. | A tool execution, API request, code run, or user-facing message. |
| Observe | Read the returned result, error, status, or other environmental evidence. | Evidence about what happened and whether the goal is closer to completion. |
| Update | Revise the next step, continue, ask for review, request clarification, or finish. | The next loop iteration or a stop decision. |
Interpret: turn the request into an actionable objective
The agent first separates the desired outcome from the surrounding conversation. It may need to identify constraints such as a required format, a deadline, a permitted data source, or an instruction not to modify production systems. If the request is ambiguous or lacks an essential parameter, asking a question can be the correct next action rather than guessing.
System and developer instructions influence this interpretation. They can define priorities, safety restrictions, output formats, escalation rules, and which kinds of actions require confirmation. Context can include earlier messages, retrieved documents, account state, or results from previous tools.
Choose: select only from exposed actions
The model does not have an unrestricted menu of capabilities. The application supplies tools, descriptions, parameter schemas, and sometimes routing rules that determine what can be selected. A calendar tool might expose operations such as finding availability and creating an event; it does not automatically grant permission to send arbitrary network requests.
When no tool is needed, the choice may be a direct answer. When a tool is needed, the model proposes a structured call. An application can reject the call, validate its arguments, require approval, or route it to a different service before execution.
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Act: make a bounded call or respond
Tool arguments are normally structured data validated against a developer-defined schema. The tool may query a database, execute code, retrieve a document, call an external service, or perform a side effect such as updating a record. A robust application treats the call as untrusted input: it validates types and permissions, handles timeouts, and records what was attempted.
For a purely informational request, acting may mean returning a response immediately. For a multi-step task, the response is often another tool call, so the visible conversation alternates between model decisions and application results.
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An agent should not assume that a requested action succeeded. It needs the actual result: returned data, an error message, an HTTP status, a test output, or a code-execution result. Anthropic 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.” This principle is described in Building Effective AI Agents, published December 19, 2024.
Observation can reveal that the plan worked, that a different action is required, or that the task is impossible with the available permissions. Feeding errors back into the loop allows the agent to correct a parameter, choose another tool, or explain the limitation instead of hallucinating a successful outcome.
Update: continue, revise, escalate, or finish
After observing a result, the agent reassesses the goal. It may perform another search, use a second tool, revise an earlier assumption, ask the user for missing information, pause for human review, or provide the final response. The application should preserve enough state for this decision while limiting irrelevant history and sensitive data.
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This loop does not imply that the model exposes a complete, stable plan before execution. Plans can be partial and revised as new evidence arrives. Some systems make one decision per turn; others have code that enforces a sequence around each model call.
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Tool selection is partly a model decision and partly an API-policy decision. OpenAI’s API reference documents tool-choice modes including none, auto, and required, along with developer-defined function tools and their descriptions and parameter schemas. The reference is available at the tool-choice/API documentation; verify current behavior before applying implementation details because API labels and capabilities can change.
| Mode | Meaning for the current turn | Typical use |
|---|---|---|
none |
Tool calls are disabled. | Force a written explanation, confirmation, or final answer. |
auto |
The model may answer directly or call one or more available tools. | Normal agent turns where either a response or an action could satisfy the request. |
required |
The model must make a tool call. | Require a lookup, validation, or other machine-checked operation before continuing. |
These settings do not decide which tool is semantically best by themselves. Tool names, descriptions, parameter schemas, authorization checks, and application routing all affect the available choice. Narrow, unambiguous schemas reduce accidental side effects; descriptions should state prerequisites, units, failure behavior, and whether an operation changes data.
Planning versus a fixed workflow
The central design decision is how much control to place in application code and how much to delegate to the model. The following comparison uses Anthropic’s guidance as implementation advice, not as a claim that one architecture wins in every benchmark.
| Decision axis | Fixed workflow | More autonomous agent |
|---|---|---|
| Task steps | Predefined sequence and branching rules. | Steps are selected dynamically from intermediate results. |
| Predictability | High when inputs fit known cases. | Useful when the path is difficult to predict. |
| Control and observability | Orchestration is explicit in code and easier to audit. | More decisions occur at runtime and require detailed traces. |
| Adaptability | Unexpected results need new code paths. | Can select a different tool or revise a plan during execution. |
| Cost and error exposure | Usually bounded by the programmed path. | Additional calls can raise cost and compound mistakes. |
| Oversight | Approvals can be placed at known points. | Use explicit review checkpoints for uncertain or high-impact actions. |
A practical rule is to start with the smallest workflow that reliably handles the task. Introduce agentic choice where variability is real and valuable, then add tests, permissions, logging, and limits around that choice. “Autonomous” should describe a measured capability, not a goal in itself.
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When should an AI agent stop?
Stopping is another controlled decision, not merely the absence of a next tool. An agent can stop when it has met a defined completion condition, when it cannot make meaningful progress, when a safety rule blocks the next action, or when an operational limit is reached.
Completion criteria
Define what counts as done before deployment. Examples include a verified record ID, a report containing required sections, a test suite passing, or an answer that cites the permitted source. A vague instruction such as “keep investigating” makes completion difficult to evaluate and encourages unnecessary calls.
Iteration limits
Anthropic names a maximum number of iterations as an example stopping condition. The source does not establish a universal optimal number. Set a limit appropriate to the task, count both successful and failed calls, and return a useful status when the limit is reached rather than silently claiming success.
Human review
Pause for a person before irreversible, expensive, sensitive, or high-impact actions. Review can occur before a tool call, after a draft, or when the agent expresses uncertainty. The reviewer should see the proposed action, relevant evidence, and expected side effect.
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Stop or escalate when authentication fails, required data is missing, results conflict, a tool repeatedly errors, or the next action exceeds the agent’s permissions. Retrying without changing the cause can create duplicate charges, repeated updates, or runaway loops.
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Multi-agent handoffs
A system may use several specialized agents instead of one general agent. LangChain describes a handoff pattern in which the active agent changes dynamically through tool calls based on conversation context. Its architecture overview is at Choosing the right multi-agent architecture.
For example, a triage agent could transfer control to a billing specialist, which could then hand off to a technical specialist. The handoff tool should carry a concise task state, permissions, unresolved questions, and the evidence needed by the receiving agent. Without that context, each transfer can lose assumptions or repeat work.
Handoffs are an orchestration option, not a universal best practice. They add routing decisions, interfaces between agents, and more places for errors to occur. Use them when specialization or independent permissions provide a clear benefit; otherwise, a single agent or a fixed workflow may be easier to test and operate.
Designing a reliable next-action controller
- Write the objective and completion test. State the desired outcome, acceptable evidence, and conditions that mean the task is complete.
- Inventory available actions. Document each tool’s purpose, parameters, permissions, side effects, timeout behavior, and error format.
- Choose the autonomy level. Keep deterministic steps in code; expose model choice only where intermediate results genuinely change the path.
- Constrain tool selection. Use the appropriate tool-choice mode, allow-list tools by task or user role, and validate every argument server-side.
- Capture ground truth. Return structured success and failure results so the next decision is based on what happened, not on an assumed outcome.
- Define stop and escalation rules. Include completion checks, iteration or time limits, retry policies, and human approval for consequential actions.
- Trace the loop. Log the goal, selected tool, validated arguments, result, latency, errors, and stop reason while protecting confidential data.
- Test unexpected paths. Exercise empty results, malformed arguments, permission failures, contradictory data, unavailable tools, and repeated errors.
These are engineering recommendations. The cited sources describe the architectural patterns and examples, but they do not provide a universal completion test, iteration threshold, or comparative success rate.
Common failure modes and fixes
- The agent claims an action succeeded. Require a tool result or verifiable state change before reporting success.
- It chooses an inappropriate tool. Improve descriptions and schemas, narrow the allow-list, or route the task through deterministic code.
- It loops on the same error. Track retries, vary the recovery strategy, and stop after a bounded number of attempts.
- It takes an unsafe side effect. Separate read and write tools, enforce authorization outside the model, and require confirmation for irreversible operations.
- A handoff loses context. Pass a structured summary with evidence, unresolved issues, and the receiving agent’s exact responsibility.
- The agent stops too early. Make completion criteria observable and distinguish “no result found” from “the task is complete.”
- The workflow is too expensive. Move predictable steps into code, reduce unnecessary model turns, and reserve autonomous exploration for cases that need it.
Bottom line
An AI agent decides what to do next by repeatedly interpreting its goal, choosing among application-provided actions, acting, observing real results, and updating its course. Instructions, tool schemas, routing, permissions, and stopping controls define the practical boundaries. Use a fixed workflow when the path is known; use agentic planning when results make the path genuinely unpredictable; and treat every autonomous loop as software that needs completion criteria, limits, evidence, and oversight.
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