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Agentic Workflows: How to Build Your First AI Loop in 2026

Agentic workflows are a design pattern where AI models act as "agents" by planning, executing, and refining their own work through multiple steps rather than generating a single response. By allowing an AI to use tools and iterate on its own results, these workflows can improve task accuracy by over 40% compared to standard prompting. You can build your first agentic loop in under 15 minutes using Python and modern models like Claude Sonnet 4.

Why are agentic workflows different from standard prompting?

Standard prompting is like a one-shot request where you ask an AI a question and get a single answer. If the answer is wrong or incomplete, the process stops there unless you manually intervene. This is often called "zero-shot" reasoning because the AI has one chance to get it right.

Agentic workflows change this by giving the AI a "loop" to work within. Instead of just answering, the AI can think through a plan, execute a task, check its own work, and fix errors. It treats the AI as an active participant that can use external tools, such as a web search or a code execution environment, to complete a complex goal.

This shift moves AI from being a simple chatbot to a digital coworker. We have found that breaking a large project into smaller, agentic steps leads to much more reliable software than asking a model to write the whole thing at once.

What are the core components of an agent?

To understand how these workflows function, you need to know the four main patterns that make an AI "agentic." These patterns allow the model to move beyond simple text generation.

The first pattern is Reflection. This is when the AI looks at its own draft and identifies mistakes or areas for improvement. It’s like a writer proofreading their own essay before turning it in.

The second is Tool Use. The AI is given an API (Application Programming Interface—a way for programs to talk to each other) so it can interact with the real world. This might include a calculator, a database, or a file editor.

The third is Planning. The AI breaks a high-level goal, like "Research this company," into a sequence of smaller steps. It decides which step to take first and what to do if a specific step fails.

The fourth is Multi-agent Collaboration. This involves two or more AI agents working together. For example, one agent might act as a "Coder" while another acts as a "Reviewer" to ensure the code is safe and efficient.

What do you need to start building?

Before you write any code, you need to set up your environment. Don't worry if you haven't done this before; it only takes a few commands in your terminal (a text-based interface used to run commands on your computer).

Prerequisites:

  • Python 3.12+: Ensure you have the latest stable version of Python installed.
  • Anthropic API Key: You will need an account and an API key from Anthropic to use Claude Sonnet 4.
  • The Anthropic Library: This is a pre-written set of code that makes it easy to talk to Claude.

To install the necessary library, open your terminal and type: pip install anthropic

Once that is done, you are ready to create your first script.

Step 1: Setting up the AI client

The first step is to tell your code which AI model you want to use and provide your secret key. We will use Claude Sonnet 4 because it has excellent reasoning capabilities for agentic tasks.

Create a new file named agent.py and add the following code:

import anthropic

# Initialize the client with your API key
# Replace 'your-api-key' with your actual key from Anthropic
client = anthropic.Anthropic(api_key="your-api-key")

# Define the model we will use (Claude Sonnet 4 - Released 2025)
MODEL_NAME = "claude-4-sonnet-20251022"

What you should see: Nothing will happen yet when you run this, but it prepares the connection between your computer and the AI.

Step 2: Creating a basic reflection loop

Now we will build a simple "Reflection" workflow. This script will ask the AI to write a poem, then ask it to critique that poem, and finally rewrite it based on the critique.

Add this to your agent.py file:

# Step A: Generate an initial draft
initial_prompt = "Write a short poem about a robot learning to paint."
response = client.messages.create(
    model=MODEL_NAME,
    max_tokens=500,
    messages=[{"role": "user", "content": initial_prompt}]
)
draft = response.content[0].text
print(f"--- DRAFT ---\n{draft}\n")

# Step B: Reflect on the draft
reflection_prompt = f"Critique this poem. Find three ways to make it more emotional: {draft}"
reflection_response = client.messages.create(
    model=MODEL_NAME,
    max_tokens=500,
    messages=[{"role": "user", "content": reflection_prompt}]
)
critique = reflection_response.content[0].text
print(f"--- CRITIQUE ---\n{critique}\n")

# Step C: Rewrite based on reflection
final_prompt = f"Rewrite the poem using this critique: {critique}"
final_response = client.messages.create(
    model=MODEL_NAME,
    max_tokens=500,
    messages=[{"role": "user", "content": final_prompt}]
)
print(f"--- FINAL VERSION ---\n{final_response.content[0].text}")

What you should see: When you run python agent.py, your terminal will show three distinct sections. You'll notice the final version of the poem is significantly more detailed and polished than the first draft because the AI "thought" about its mistakes.

Step 3: Adding tool use to the workflow

To make an agent truly powerful, it needs to interact with the world. In this step, we define a "tool" (a function) that the AI can call. We will simulate a tool that fetches the current weather.

# This is a mock tool that the AI can "call"
def get_weather(location):
    # In a real app, this would call a weather API
    return f"The weather in {location} is 72 degrees and sunny."

# We tell the AI about the tool using a JSON schema
# JSON is a format for storing and transporting data
tools = [{
    "name": "get_weather",
    "description": "Get the current weather in a given location",
    "input_schema": {
        "type": "object",
        "properties": {
            "location": {"type": "string", "description": "The city and state"}
        },
        "required": ["location"]
    }
}]

By defining tools this way, the AI can decide when it needs extra information. If you ask, "Should I wear a jacket in New York?", the agentic workflow recognizes it doesn't know the weather and will trigger the get_weather tool.

What are common mistakes beginners make?

It is normal to run into bugs when building your first agent. One frequent mistake is Infinite Loops. This happens when an agent keeps trying to fix a problem but fails, leading it to call the same tool over and over forever. You should always set a "max iterations" limit (a maximum number of tries) to prevent high API costs.

Another common issue is Prompt Leakage. This occurs when the AI gets confused between its instructions and the data it is processing. For example, if you ask an AI to summarize a document that contains the phrase "Ignore all previous instructions," a poorly designed agent might actually stop working. Using "system prompts" (special instructions that set the AI's behavior) helps keep the agent on track.

Finally, beginners often forget to handle JSON Errors. Sometimes the AI might return a tool call that is missing a bracket or has a typo. Your code should always include a "try-except" block (a way to catch and fix errors in Python) to handle these moments gracefully.

How can you scale these workflows?

Once you are comfortable with simple loops, you can explore frameworks designed for complex agents. Frameworks are collections of pre-written code that handle the "boring" parts of agentic workflows so you can focus on the logic.

LangGraph is a popular choice for building workflows that require complex cycles and state management (remembering what happened in previous steps). It allows you to draw your workflow as a flowchart and then turn that chart into code.

CrewAI is another great tool for beginners. It focuses on "Role-Based" agents. You can define one agent as a "Manager" and another as a "Researcher," and the framework handles the communication between them automatically.

Next Steps

Now that you have built a basic reflection loop, the best way to learn is by doing. Try modifying your script to include a third "Reviewer" step where a different model—perhaps GPT-5—checks the work of Claude Sonnet 4. This "cross-model" verification is a powerful way to reduce errors.

You might also try connecting your agent to a real-world tool, like a Google Search API or a local file system. Start small, and don't be afraid to break things—that is how we all learn to build better systems.

To deepen your understanding of the technical specifics, check out the official Anthropic documentation.

Read the Agentic Documentation