What are the stages of an agentic AI loop?

Vocabulary varies by vendor, but most descriptions reduce to the same stages:

• Perceive: the agent receives input, whether a user request, a tool result, or an error from its last action.
• Reason: the model interprets everything in context and decides what to do next.
• Plan: for larger goals, the agent breaks the objective into subtasks. Simple tasks skip this step.
• Act: the agent executes something, such as an API call, a database query, a file edit, or a code run.
• Observe: the agent inspects the outcome and decides whether to continue, adjust, or finish.

In code, the whole pattern is a while loop. The model is called, any requested tools run, their results are appended to the conversation, and the loop continues until the model replies without requesting another tool. Anthropic's engineering team describes agents in nearly these terms: a model using tools based on environmental feedback, in a loop. The idea is simple, and most of the engineering effort goes into everything around it.

How is an agent loop different from a chatbot?

A chatbot handles one input and produces one output, with no mechanism to check whether an action worked or to change course. Ask it to find flights, compare them against loyalty points and book the best one, and it can discuss each step but cannot chain them.

An agent can, because the loop gives it three things a single pass lacks. It can iterate on results, so each tool output informs the next decision. It can recover from failure, so an empty search or an error triggers a new approach instead of a dead end. And it can decompose dependent tasks, where step three only makes sense after step two returns. The model may be identical in both cases. The architecture is what differs.

Not sure which of your workflows genuinely need a loop? JADA supports your agentic AI adoption needs by separating agentic use cases from ones a simple pipeline handles better.

What is the primary function of the reasoning part of an agentic AI loop?

The primary function of the reasoning stage in an agentic AI loop is to decide what happens next. The model evaluates the goal, the accumulated context, and the latest observation, then selects the next action, including which tool to call and with what arguments, or concludes the task is finished and returns a final answer.

Reasoning is the control point of the loop. It converts raw observations into decisions, updates the plan when something unexpected occurs, and judges whether the objective has been met. A weak reasoning step produces the failures people associate with agents: repeated identical tool calls, premature stopping, or wandering off task. This is why prompt design, tool descriptions, and effort settings all attach to this step. It is also the part that benefits most from the ReAct pattern below.

What is a ReAct loop in AI agents?

A ReAct loop interleaves reasoning traces with actions. At each step, the model writes a short thought, takes an action such as a search or tool call, reads the observation, and reasons again. The name combines "reasoning" and "acting."

On the ALFWorld and WebShop benchmarks, ReAct beat imitation and reinforcement learning baselines by absolute success-rate margins of 34% and 10%, using only one or two in-context examples. The result mattered because it showed that letting a model reason between actions, rather than acting blindly or reasoning without acting, measurably improves outcomes. Thinking and acting reinforce each other, so the reasoning trace also gives humans something to audit. 

Nearly every modern agent framework implements some form of this loop, whether it calls it ReAct, a tool-calling loop, or an orchestration layer. The names differ, but the shape does not.

How does the Claude agent loop work?

The Claude agent loop is the execution cycle behind Claude Code and the Claude Agent SDK. Claude receives the prompt, system instructions, tool definitions, and history, then either answers or requests tool calls. The SDK runs those tools, returns the results, and repeats until Claude produces a response with no tool calls.

Anthropic's documentation calls each full round trip a turn. A quick question might take one or two turns. A task like refactoring a module and updating its tests can chain dozens of steps, reading files, editing code, and re-running tests. The loop ends with a result message carrying the output, token usage, cost, and a status that tells you whether the task succeeded or hit a limit.

What makes this loop production-relevant is the set of levers around it:

• Turn and budget caps: max_turns limits tool-use round trips, and max_budget_usd stops the run at a spend threshold. Both default to no limit, so a production agent should set them deliberately.
• Permission modes: Tool calls can require approval, be auto-approved for specific tools, or be blocked outright.
• Hooks: Callbacks run before and after each tool call and when the agent stops, letting you validate inputs, log outputs, or block risky commands.
• Context management: Older history is summarized automatically as the window fills, so durable rules belong in persistent project instructions, not the first prompt.
• Subagents: A subagent works in a fresh context and returns only its final result, keeping the main loop's context lean.
• Parallel execution: Read-only tools can run concurrently, while state-changing tools run one at a time to avoid conflicts.

How does the Codex agent loop work?

The Codex agent loop is the orchestration logic in OpenAI's Codex CLI. Each turn assembles inputs, runs model inference, executes any tool calls the model requests, appends the results, and queries the model again until it emits a final assistant message.

Unrolling the Codex agent loop, the process repeats until the model stops emitting tool calls and produces a message for the user. Because tools can modify the local environment, the real output is often the code written or edited on disk, and the closing message signals that control returns to the person. The post also covers the practical costs of long sessions: context growth, prompt caching, and automatic compaction.

Set side by side, the two loops look almost identical, which supports the point that the pattern has converged.

What are the main types of agent loops?

The basic loop extends in a few directions:

• Single-agent tool loop: One model, one context, tools called in sequence. This is the right starting point for most workflows.
• Plan-and-execute: A planner drafts the full task list first, then executors work through it. The LLMCompiler paper reports up to 3.7x lower latency, up to 6.7x cost savings, and roughly 9% higher accuracy than ReAct on benchmarks with parallelizable function calls.
• Multi-agent orchestration: A lead agent delegates to specialist subagents in parallel. Anthropic reported that a lead-plus-subagents setup on its internal research evaluation.
• Workflow loops: Some frameworks, like Google's ADK, provide loop components that repeat a fixed sequence of steps until a condition is met. These are deterministic constructs, distinct from a model-driven agent loop.

The trade-off is cost. Anthropic's own data shows agents use about 4x the tokens of chat interactions, and multi-agent systems about 15x. Complexity should be earned by measured improvement, not assumed.

Why do agent loops fail in production?

Loops that work in a demo fail in predictable ways once volume, real systems, and real consequences arrive:

• Runaway iteration: A broken tool returns nothing, the agent retries indefinitely, and tokens burn until a rate limit intervenes. Hard turn caps prevent this.
• Cost compounding: Every iteration is a model call, and history is replayed each time. Without per-run budgets, spend scales with ambiguity.
• Context bloat: Large tool outputs accumulate, older instructions get summarized away, and behavior drifts in long sessions.
• Opaque failures: A 15-step run is hard to debug without traces of what the agent reasoned, which tool it called, and what came back.
• Overreach: An agent with broad permissions can take actions nobody intended, which matters most in banking, insurance, healthcare, and the public sector.

How do you stop an agent loop safely?

Layered stopping conditions are the difference between an agent and a liability:

• Maximum turns or iterations, set per task type, not globally.
• Token and cost budgets enforced as hard stops per run.
• No-progress detection, exiting when successive iterations yield nothing new.
• Goal checks, where a separate validation step confirms the objective was met.
• Human approval gates for irreversible actions like payments, deletions, or external messages.

Alongside these, keep structured logs of every reasoning step, tool call, argument, and result. Organizations operating under frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001, or the EU AI Act typically need that audit trail as evidence of oversight.

Building agents for a regulated environment? JADA designs the approval gates, logging, and permissions into the Build phase, then runs them through Manage.

When should you not use an agent loop?

A loop is the wrong tool for a fixed, predictable sequence, where a deterministic pipeline is cheaper, faster, and easier to audit. It is also wrong for single-step tasks, where one model call and one tool call suffice, and for latency-critical paths, because every iteration adds model latency.

The industry direction is to start with the simplest architecture that solves the problem and add a loop only when the number of steps cannot be predicted in advance.

Agent loop design checklist

Before you build or buy, confirm each of these:

• The workflow genuinely requires adaptive, multi-step decisions.
• Every tool has a clear name, purpose, and schema, and a distinct role.
• Turn, budget, and no-progress limits are defined and tested.
• Permissions follow least privilege, with approvals on irreversible actions.
• Traces and logs capture every reasoning step and tool call.
• Context handling is planned for long sessions, including what must survive summarization.
• An owner monitors cost per completed task, not just tokens consumed.

Why JADA is the right partner to build and manage AI agents

JADA is a boutique agentic AI company that designs, builds, and manages custom AI agents. What that means for your loop:

• We set turn limits, budgets, permissions, and approval gates during design, not after an incident.
• We instrument every agent for traceability, so oversight and audit questions have answers.
• We stay accountable after go-live, monitoring cost per completed task and tuning behavior as your workflows change.

If you have a workflow you suspect needs an agent, the fastest next step is a short conversation about scope. Talk to our experts today!

Frequently Asked Questions

What is the difference between an agent loop and a ReAct loop?

‍ReAct is a specific, well-known way of running an agent loop, in which reasoning and acting are interleaved at every step. "Agent loop" is the broader category, covering ReAct, plain tool-calling loops, plan-and-execute designs, and multi-agent variants. Every ReAct loop is an agent loop, but not every agent loop reasons in visible ReAct format.

What is the primary function of the reasoning part of an agentic AI loop?

‍Its job is to decide the next step. The reasoning stage interprets the goal, context, and latest result, then chooses an action or concludes the task is done. It is the control point that turns observations into decisions and revises the plan when reality diverges from it.

How does the Claude agent loop differ from the Codex agent loop?

‍The core cycle is the same: model, tool calls, results fed back, repeat until a final message. The differences are in the surrounding harness. The Claude Agent SDK exposes explicit turn and budget caps, hooks, and permission modes as developer controls. Codex documents its approach to prompt caching and context compaction for long sessions.

How do you stop an agent loop from running forever?

‍Layer several limits: a maximum number of turns, a per-run cost budget, no-progress detection, and a goal-completion check. Add human approval for high-impact actions. Set these limits deliberately, because some SDKs default to no limit.

Do all AI agents need an agent loop?

‍Any system that must take multiple dependent steps and adapt to results does, and that is what makes it an agent. But many business workflows follow a fixed sequence and are better served by a deterministic pipeline. If you cannot predict the number of steps in advance, a loop is justified. If you can, it usually is not.