By Pindi Sahota · Last updated: 2026-06-07
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How to Build Your First Claude AI Agent (2026 Guide)
Last updated: 2026-06-07
Building a Claude AI agent is simpler than it sounds, and the distinction between a basic automation and a true agent comes down to one thing: does Claude take an action, observe the result, and decide what to do next? If yes — it's an agent. This guide explains what makes something an agent (not just a chatbot or a single-prompt workflow), walks through two paths for building one — no-code with Make.com and code with Python — and gives you three practical agent examples you can build and deploy today, even if you've never written an automation before.
What Makes Something a Claude AI Agent?
The word "agent" gets overused, but it has a precise meaning in the context of AI automation. A Claude AI agent has three properties that a simple prompt-response workflow does not:
1. Tool use: Claude can call external tools or functions — a web search, a database lookup, a calculator, an API call — and incorporate the results into its reasoning.
2. A decision loop: After using a tool and receiving results, Claude decides whether the task is complete or whether it needs to take another action. This loop continues until Claude determines the goal is achieved.
3. Goal-directed behaviour: Claude is given a goal (not just a single prompt) and autonomously determines the steps needed to reach it.
A simple Claude workflow looks like this: input → Claude generates text → output. An agent looks like this: goal → Claude thinks about what to do → Claude calls a tool → Claude reviews result → Claude decides to call another tool or declares done → output.
The agentic loop is what gives Claude agents their power — they can handle multi-step tasks that no single prompt could complete.
No-Code Path: Build a Claude Agent with Make.com
Make.com's visual scenario builder can implement agentic loops without writing code. The approach uses Make.com's Router and Repeater modules to create the decision loop, with the Claude module as the reasoning engine.
Step 1: Define Your Agent's Goal and Tools
Before building in Make.com, write out plainly what your agent should do and what actions it has available. For example:
Goal: Qualify incoming sales leads Tools available: (1) Look up company info in Clearbit, (2) Check if lead already exists in HubSpot, (3) Create or update HubSpot record, (4) Send Slack notification to sales team
Step 2: Set Up the Trigger
Create a new Make.com scenario. Add a trigger — for a lead qualification agent, this would be Typeform > Watch Responses or Webhooks > Custom Webhook if your lead form posts there.
Step 3: Add the Claude Module as the Decision Maker
Add an Anthropic module after the trigger. Write a system prompt that describes the agent's role and available tools:
` You are a lead qualification agent. When you receive a lead, you must:
- Decide if the company size (from data provided) meets our criteria (50+ employees)
- Check if the email domain is from a business (not gmail.com, hotmail.com etc)
- Score the lead 1-10 based on fit
Respond with a JSON object: {"score": number, "qualified": boolean, "reason": string} `
Pass the lead data from the trigger into Claude's message.
Step 4: Add a Router to Branch Based on Claude's Decision
Claude returns a JSON score. Add a JSON > Parse JSON module to extract the values, then add a Router module. Configure two routes:
- Route A (if
qualified = true): Create HubSpot contact → Send Slack alert to sales team - Route B (if
qualified = false): Add to a low-priority spreadsheet for later review
Step 5: Test and Iterate
Run the scenario with test data. Inspect Claude's JSON output. Adjust your system prompt if Claude's scoring logic doesn't match your expectations. This prompt tuning step is where most of your time goes when building agents — the logic lives in the prompt, not the workflow structure.
Code Path: Build a Claude Agent with Python
For agents that need more complex decision loops, maintain conversation history, or call many different tools in sequence, Python gives you full control using the Anthropic SDK.
Basic Agentic Loop Structure
`python import anthropic import json
client = anthropic.Anthropic()
Define tools the agent can use
tools = [ { "name": "searchcrm", "description": "Search the CRM for a contact by email address", "inputschema": { "type": "object", "properties": { "email": {"type": "string", "description": "Email address to look up"} }, "required": ["email"] } }, { "name": "createcrmrecord", "description": "Create a new contact record in the CRM", "input_schema": { "type": "object", "properties": { "name": {"type": "string"}, "email": {"type": "string"}, "company": {"type": "string"} }, "required": ["name", "email"] } } ]
def runagent(usergoal: str): messages = [{"role": "user", "content": user_goal}]
while True: response = client.messages.create( model="claude-sonnet-4-5", max_tokens=1024, tools=tools, messages=messages )
# If Claude is done, return final response if response.stopreason == "endturn": return response.content[0].text
# If Claude wants to use a tool, execute it and loop back if response.stopreason == "tooluse": toolresults = [] for block in response.content: if block.type == "tooluse": result = executetool(block.name, block.input) toolresults.append({ "type": "toolresult", "tooluse_id": block.id, "content": json.dumps(result) })
# Add Claude's response and tool results to message history messages.append({"role": "assistant", "content": response.content}) messages.append({"role": "user", "content": tool_results}) # Loop continues — Claude will decide what to do next
def executetool(name: str, inputs: dict): # Your actual tool implementations go here if name == "searchcrm": return searchyourcrm(inputs["email"]) if name == "createcrmrecord": return createcrmcontact(inputs) `
The key pattern is the while True loop: Claude responds, you check if it called a tool, you execute the tool, you add the result back to the message history, and Claude decides the next step. The loop only exits when Claude's stopreason is endturn.
3 Claude AI Agent Examples
Agent 1: Lead Research and Qualification Agent
What it does: When a new lead fills in your contact form, this agent looks up their company on LinkedIn (via a scraping API), checks their company size, reviews their message for buying signals, scores the lead 1-10, and writes a personalised intro email draft for the sales rep — all before a human looks at it.
Build path: Make.com (no-code) — 4-6 modules. Trigger on form submission, Claude module for reasoning and scoring, Router for qualified/unqualified branching, HubSpot and Gmail actions.
Time to build: 2-3 hours for a non-technical user following a template.
Agent 2: Customer Support Resolution Agent
What it does: A customer submits a support ticket. The agent searches your knowledge base for relevant articles, attempts to draft a resolution, checks if the issue matches any known bugs in your tracker, and either sends an automated response (if confident) or escalates to a human agent with a summary and suggested response.
Build path: Python + Anthropic SDK — requires code for the multi-step decision logic. Tools include: knowledgebasesearch, bugtrackerlookup, sendemail, escalateto_human.
Time to build: 1-2 days for a developer starting from scratch.
Agent 3: Content Repurposing Agent
What it does: Given a new blog post URL, the agent reads the content, generates a Twitter/X thread, a LinkedIn post, an email newsletter intro, and three short-form video script ideas — then routes each piece to the appropriate scheduling tool (Buffer, Mailchimp, etc.).
Build path: Make.com — trigger on new Google Doc or RSS item, Claude module for each content format, parallel action routes to different platforms.
Time to build: 1-2 hours for a user familiar with Make.com.
Agent vs Chatbot vs Simple Automation — What's the Difference?
Understanding where agents fit helps you pick the right tool for each task.
| Type | Claude role | Decision loop | Best for |
|---|---|---|---|
| Simple automation | Generates text once | No | Writing, summarising, classifying single items |
| Chatbot | Responds conversationally | No (responds to each message) | Customer Q&A, support chat |
| Agent | Plans, acts, observes, decides | Yes | Multi-step tasks requiring judgment |
Don't over-engineer. If a task can be completed with a single Claude call, use a simple automation. Agents add value when the task genuinely requires multiple steps, tool calls, or decisions that depend on intermediate results.