What Are Agentic Loops? The Core Pattern Behind Every AI Agent

Every AI agent — from a simple chatbot that retries failed tasks to a fully autonomous coding assistant — runs on the same core pattern. It is called an agentic loop.

Understanding agentic loops is the difference between building an AI tool that follows a script and building one that actually solves problems. We run agentic loops in production at Like One, and this guide breaks down exactly how they work.

The Agentic Loop in 30 Seconds

An agentic loop is a cycle where an AI model:

1. Observes — reads the current state (user input, tool results, environment data)
2. Thinks — reasons about what to do next
3. Acts — calls a tool, writes code, sends a message, or takes some action
4. Evaluates — checks whether the action succeeded or failed
5. Loops — returns to step 1 with updated context

That is it. Every AI agent framework — LangChain, CrewAI, Claude Code, AutoGPT — implements some version of this loop. The differences are in how they handle each step, not in the fundamental pattern.

Why Loops Beat Linear Prompts

A regular LLM call is one-shot: you send a prompt, you get a response, you are done. This works for simple tasks like summarizing text or answering questions.

But real work is not one-shot. Real work involves:

• Trying something and finding out it does not work
• Gathering information from multiple sources before deciding
• Breaking a big task into smaller steps
• Adapting when conditions change mid-task

An agentic loop handles all of these because the AI gets to react to the results of its own actions. It is not locked into a predetermined path — it can course-correct, retry, and explore alternatives.

The Anatomy of a Real Agentic Loop

Here is a simplified version of what an agentic loop looks like in practice:

while not done:
    # 1. Observe: gather current state
    context = get_conversation_history()
    tool_results = get_latest_tool_output()
    
    # 2. Think: ask the model what to do
    response = llm.generate(
        system_prompt + context + tool_results
    )
    
    # 3. Act: execute the model's chosen action
    if response.has_tool_call:
        result = execute_tool(response.tool_call)
        add_to_context(result)
    
    # 4. Evaluate: did we finish?
    if response.is_final_answer:
        done = True
    
    # 5. Loop: continue with updated context

The key insight is that the model decides when it is done. It is not running a fixed number of iterations — it loops until it believes the task is complete. This is what makes it "agentic" rather than just "automated."

The Three Types of Agentic Loops

1. ReAct (Reasoning + Acting)

The most common pattern. The model alternates between reasoning about what to do and taking actions. Each reasoning step is visible, making the agent's decision process transparent and debuggable.

Best for: Research tasks, multi-step problem solving, anything where you need to see the agent's thinking.

2. Plan-Execute

The model creates a full plan upfront, then executes each step sequentially. If a step fails, it can replan from the current state.

Best for: Well-defined tasks with clear steps, like deploying software or processing a batch of documents.

3. Reflexion

The model attempts a task, evaluates its own output, critiques the result, and tries again with the critique as additional context. This self-improvement loop produces higher-quality outputs on tasks where the model can judge its own work.

Best for: Writing, code generation, any task where output quality can be self-assessed.

What Makes a Loop Good or Bad

Good loops have:

• Clear exit conditions. The agent knows when the task is done. Without this, loops run forever and burn tokens.
• Rich tool access. The more tools an agent has, the more actions it can take. A loop with only a search tool is limited. A loop with search, code execution, file editing, and API access can handle complex tasks.
• Context management. Each iteration adds to the context window. Good loops summarize or prune old context so the model does not lose track of the current state.
• Error recovery. When a tool call fails, the model should diagnose the error and try a different approach — not retry the same failing action.

Bad loops have:

• No iteration limits. Without a maximum loop count, a confused agent can burn hundreds of dollars in API calls chasing a dead end.
• Blind retries. Retrying the same failed action without changing the approach is the most common agent failure mode.
• Context overflow. Each iteration adds tokens. Without context management, the model eventually hits its context limit and starts losing information.
• Vague objectives. "Do something useful" is not an objective. "Find the three most relevant papers on CRISPR delivery mechanisms and summarize their methods" is.

Agentic Loops in the Real World

Claude Code

Anthropic's CLI tool is a pure agentic loop. You give it a task, and it loops through: read files, reason about changes, edit code, run tests, check results, fix failures. It continues until the task is complete or it hits a blocker it cannot resolve.

What makes Claude Code's loop effective is its tool set — file reading, file editing, bash execution, and web search — combined with a massive context window that lets it hold an entire codebase in memory while iterating.

Our Production System

At Like One, our AI twin runs an agentic loop that:

1. Boots by reading persistent brain state (880+ entries in a SQLite database)
2. Identifies the next task from a prioritized queue
3. Executes using 15+ tools (email, browser automation, code editing, deployment)
4. Writes results back to brain state
5. Picks up the next task — or creates new tasks based on results

This loop runs continuously across sessions. The brain state persists, so each new session resumes where the last one stopped. The agent never starts from scratch.

Building Your First Agentic Loop

You do not need a framework. The simplest useful agentic loop is a while loop with an LLM call and a tool executor:

import anthropic

client = anthropic.Anthropic()
messages = [{role: user, content: task}]
tools = [search_tool, calculator_tool, file_reader_tool]

for i in range(max_iterations):
    response = client.messages.create(
        model=claude-sonnet-4-6,
        messages=messages,
        tools=tools
    )
    
    # Check if agent wants to use a tool
    if response.stop_reason == tool_use:
        tool_result = execute_tool(response)
        messages.append({role: assistant, content: response.content})
        messages.append({role: user, content: tool_result})
    else:
        # Agent gave a final answer
        print(response.content[0].text)
        break

Start with this. Add complexity only when you hit a specific limitation. Most agent failures come from over-engineering the loop before understanding the basics.

Agentic Iterations: How Loops Process Each Step

Each pass through an agentic loop is called an iteration. Understanding iterations is key to building efficient agents because every iteration costs tokens, time, and money.

A single agentic iteration follows this sequence:

1. Context assembly — The loop gathers the current state: conversation history, tool results from previous iterations, and the original goal.
2. LLM reasoning — The model analyzes the context and decides what to do next. This is where the intelligence lives.
3. Action execution — The agent calls a tool, writes code, searches the web, or performs whatever action the model selected.
4. Result evaluation — The loop checks whether the action moved closer to the goal. If yes, continue or finish. If no, try a different approach.

Most tasks complete in 3 to 8 iterations. Simple lookups might finish in 1-2 iterations. Complex multi-step research or code generation can require 15-30 iterations. Setting a maximum iteration count (typically 10-25) prevents runaway loops that burn through your API budget.

The quality of your agent depends heavily on iteration efficiency — how much progress each iteration makes toward the goal. Poorly designed loops waste iterations on redundant actions, while well-designed ones make meaningful progress with every pass.

Common Mistakes

1. Too Many Tools at Once

Giving an agent 50 tools sounds powerful but actually degrades performance. The model spends more tokens reasoning about which tool to use and makes worse choices. Start with 3-5 essential tools and add more only when the agent demonstrates it needs them.

2. No Human-in-the-Loop Escape Hatch

Every production agentic loop needs a way to pause and ask a human when the agent is stuck. Without this, agents either give up too early or burn resources trying to solve unsolvable problems.

3. Ignoring Token Costs

Each loop iteration sends the entire conversation history to the model. Ten iterations with a 50K-token context means 500K tokens consumed. Track your costs per loop and set budget limits.

4. Not Persisting State

If your agent's loop crashes on iteration 15 of 20, can it resume from where it stopped? If the answer is no, you need persistent state. Write intermediate results to disk or database after each iteration.

Debugging Agentic Loops

When an agentic loop fails, it usually fails silently — the agent gives a confident-sounding wrong answer instead of crashing. Here is how to catch that.

Log Every Iteration

The single most useful debugging technique is logging the full state at each loop iteration: what the model saw (observe), what it decided (think), what it did (act), and what happened (evaluate). Without this, you are debugging blind.

import logging

logger = logging.getLogger("agent_loop")

for i in range(max_iterations):
    logger.info(f"--- Iteration {i+1} ---")
    logger.info(f"Context tokens: {count_tokens(messages)}")
    
    response = client.messages.create(
        model="claude-sonnet-4-6",
        messages=messages,
        tools=tools
    )
    
    logger.info(f"Stop reason: {response.stop_reason}")
    if response.stop_reason == "tool_use":
        tool_call = extract_tool_call(response)
        logger.info(f"Tool: {tool_call.name}({tool_call.input})")
        result = execute_tool(tool_call)
        logger.info(f"Result: {str(result)[:500]}")

Watch for Degenerate Loops

The most common failure mode is the agent repeating the same action expecting different results. Track the last 3-5 tool calls. If the agent calls the same tool with the same arguments twice in a row, inject a system message telling it to try a different approach — or break the loop and escalate.

Token Budget Alarms

Set a token budget per loop execution. When the agent hits 80% of the budget, inject a message: "You are running low on context. Summarize your progress and provide your best answer now." This prevents the agent from burning through tokens without producing output.

Trace Visualization

For production systems, build a simple trace viewer that shows each iteration as a node in a graph: observation → decision → action → result. Tools like LangSmith, Braintrust, and Humanloop provide this out of the box. For custom loops, a JSON log file rendered with a simple HTML page works fine.

Agentic Loops vs. RAG Pipelines

A common question: when should you use an agentic loop versus a RAG (retrieval-augmented generation) pipeline?

They solve different problems:

• RAG pipelines retrieve relevant documents, inject them into a prompt, and generate a response. The flow is linear: query → retrieve → generate. There is no loop, no self-correction, no multi-step reasoning.
• Agentic loops can include RAG as one of many tools. The agent might retrieve documents, realize the results are not relevant, refine the search query, retrieve again, cross-reference with another source, and then generate a response.

The rule of thumb: if the task can be solved with a single retrieval + generation step, use RAG. If the task requires judgment, iteration, or multi-step reasoning, use an agentic loop. In practice, the most powerful systems use both — RAG as a tool inside an agentic loop.

Cost Optimization for Agentic Loops

Agentic loops are expensive by default. Every iteration sends the full conversation history to the model. Here are the strategies that actually reduce costs without degrading quality:

1. Use Tiered Models

Not every iteration needs your most powerful model. Use a fast, cheap model (Claude Haiku) for simple tool-routing decisions and reserve the expensive model (Claude Opus) for complex reasoning steps. This can cut costs by 60-80% on loops that are mostly tool calls.

# Simple routing: use Haiku for tool calls, Opus for reasoning
def choose_model(iteration, task_complexity):
    if iteration == 0:  # First pass: understand the task
        return "claude-opus-4-6"
    if needs_complex_reasoning(messages):
        return "claude-opus-4-6"
    return "claude-haiku-4-5-20250414"  # Default: fast and cheap

2. Compress Context Between Iterations

After every 5-10 iterations, ask the model to summarize the conversation so far. Replace the full history with the summary. This keeps the context window small and focused. The tradeoff: you lose some detail. The benefit: your 20-iteration loop costs the same as a 5-iteration loop.

3. Cache Tool Results

If the agent searches for the same thing twice, return the cached result instead of executing the tool again. This is especially valuable for expensive tools like web searches or database queries.

4. Set Hard Limits

Every production loop needs three limits:

• Max iterations: 10-25 for most tasks. If the agent cannot solve it in 25 iterations, it probably cannot solve it at all.
• Max tokens: Set a total token budget. When exceeded, force a final answer.
• Max cost: Calculate the dollar cost per iteration and set a ceiling. For most tasks, $0.50-$2.00 is reasonable. For complex research tasks, $5-10.

When NOT to Use Agentic Loops

Agentic loops are powerful but not always the right tool. Use a simple prompt or chain instead when:

• The task has a fixed number of steps. If you know exactly what needs to happen in what order, a sequential chain is simpler and cheaper than a loop.
• Latency matters more than quality. Each iteration adds 1-5 seconds of latency. For real-time user-facing features, direct prompts or cached responses are better.
• The task has no clear completion signal. Agentic loops need a way to know when they are done. Without one, you get infinite loops or arbitrary cutoffs.
• You are processing batch data. Map-reduce patterns or parallel API calls are more efficient than sequential agent iterations for bulk processing.

The rule of thumb: if a task requires judgment, adaptation, or tool use across multiple steps with uncertain outcomes, use an agentic loop. If the steps are predictable, use a chain or a single prompt.

For making your agentic loops even smarter, see our guide to Claude custom instructions — the right system prompt can dramatically improve loop efficiency and reduce wasted iterations.

The Future: Multi-Agent Loops

The next evolution is loops of loops — multiple agents running their own agentic loops while coordinating with each other. One agent researches, another writes, a third reviews, and an orchestrator manages the workflow.

This is not hypothetical. We run a multi-agent fleet at Like One: one machine handles planning and code, another handles shipping and social, and a cloud watcher handles monitoring. Each runs its own loop, and they share state through a unified brain.

The pattern is the same at every scale: observe, think, act, evaluate, loop. Master it with a single agent first. Then scale.

Keep Reading

• AI Agent Frameworks Compared: Claude Agent SDK vs LangChain vs CrewAI (2026)
• How to Build an AI Agent That Actually Works: A Practical Guide for 2026
• Self-Improving AI Agents: How to Build Systems That Learn From Every Session