The Ultimate
Claude
Agent Builder Guide

Part 2: API Setup

Placeholder: Step-by-step walkthrough of getting your Anthropic API key, setting it up securely (environment variables, never in code), and making your first successful API call. We'll verify it works before moving on.

Getting your API key

Placeholder: Exact steps with screenshots — create account, go to API keys, generate key, where to store it.

Your first API call

Placeholder: The simplest possible working example in Python and Node.js. We'll explain every line.

# Placeholder: real working Python example will go here
import anthropic
client = anthropic.Anthropic(api_key="your-key-here")
# ... full working example

Testing your setup

Placeholder: How to verify everything is working correctly, what common errors mean, and how to fix them.

Part 3: Building Your First Agent

Placeholder: This is where it gets real. We'll build a complete, working agent from scratch — one that has a defined purpose, a system prompt, and can complete a real task. By the end of this section you'll have something running.

The anatomy of an agent

Placeholder: We'll break down exactly what makes something an "agent" vs a simple API call. System prompt, conversation loop, tool calls, memory — we'll explain each piece and why it matters.

Writing your system prompt

Placeholder: The system prompt is the most important part of your agent. We'll show you the exact structure we use, what to include, what to leave out, and how to test if it's working.

The conversation loop

Placeholder: Code walkthrough of a proper agent conversation loop — how to maintain state, handle responses, and keep the agent on task.

Part 4: Giving Your Agent Memory

Placeholder: By default Claude has no memory between sessions. This section covers every approach to adding memory — from simple conversation history to vector search to external storage — and when to use each one.

Short-term vs long-term memory

Placeholder: Clear explanation of the difference and why it matters for agent design.

Conversation history management

Placeholder: How to manage the context window properly — what to keep, what to summarise, what to store externally.

External memory with vector search

Placeholder: When your agent needs to remember lots of information, we'll show how to set up a simple vector store so it can search its own memory.

Part 5: Tools & Integrations

Placeholder: Tools are what turn Claude from a chatbot into an agent that can actually do things. This section covers Claude's tool use feature in depth — how to define tools, how Claude decides to use them, and how to handle results.

How tool use works

Placeholder: Explanation of the tool use flow — you define available tools, Claude decides when to call them, you execute the call, Claude gets the result.

Essential tools to build first

Placeholder: Web search, file read/write, API calls, email sending. We'll give you working code for each.

Building custom tools

Placeholder: How to wrap any API or function as a tool your Claude agent can call.

Part 6: Building Workflows

Placeholder: Once your agent can use tools, you can build workflows — sequences of actions that run automatically. This section covers workflow design patterns, error handling, and how to make workflows robust.

Simple vs complex workflows

Placeholder: When a simple sequential workflow is enough vs when you need branching logic or parallel execution.

Error handling that actually works

Placeholder: Agents fail. APIs go down. Data is unexpected. We'll show you how to build agents that handle failure gracefully instead of silently breaking.

Part 7: Scheduling & Automation

Placeholder: How to make your agent run automatically on a schedule — without you triggering it manually. Cron jobs, event triggers, webhook-based activation. We'll cover each approach and when to use it.

Running agents on a schedule

Placeholder: Simple cron setup for daily/hourly agent runs. Works on Mac, Linux, and Windows.

Event-triggered agents

Placeholder: Agents that run when something happens — new email, new form submission, API webhook. More powerful than scheduled agents for many use cases.

Part 8: Security

Placeholder: The security section people skip but really shouldn't. API key management, prompt injection attacks, what happens if someone tries to hijack your agent, and how to protect sensitive data your agent has access to.

Protecting your API keys

Placeholder: Environment variables, secret managers, what never to do. One leaked key can cost hundreds of dollars.

Prompt injection defence

Placeholder: If your agent reads user input or external data, it's vulnerable to prompt injection. We'll explain what this is and how to defend against it.

Part 9: Cutting API Costs

Placeholder: The Claude API costs money per token. This section will show you how to cut your costs by 60–80% without sacrificing quality — through smart model routing, caching, prompt compression, and batching.

Model routing

Placeholder: Use Haiku for simple tasks, Sonnet for medium tasks, Opus only when you need it. We'll show you the routing logic.

Prompt caching

Placeholder: Claude's prompt caching feature can reduce costs dramatically if your agent reuses the same system prompt. We'll show you exactly how to set it up.

Part 10: Pro Tips

Placeholder: The things that don't fit neatly into other sections but make a real difference. Lessons from building dozens of Claude agents — what works, what doesn't, and what we wish we knew earlier.

Prompting patterns that work

Placeholder: Specific prompting techniques for agentic tasks — chain of thought, self-reflection, role assignment, output formatting. With examples.

Debugging agents

Placeholder: How to figure out why your agent isn't doing what you want. Logging, tracing, and systematic debugging approaches.

Part 11: Multi-Agent Systems

Placeholder: The advanced section. Multi-agent systems are where Claude really shines — one orchestrator agent directing specialist subagents, each doing one job extremely well. This is how you build something that feels genuinely autonomous.

Orchestrator + subagent patterns

Placeholder: The core pattern: one smart orchestrator that plans and delegates, multiple focused subagents that execute. We'll build a working example.

Agent communication

Placeholder: How agents pass information to each other, share state, and coordinate without stepping on each other's work.

When to use multi-agent vs single agent

Placeholder: Multi-agent isn't always better. We'll give you a clear framework for deciding which architecture fits your use case.

Quick Reference

Placeholder: The cheat sheet version of the entire guide. API call templates, common tool definitions, model routing logic, security checklist, cost optimisation checklist. Everything on one page you can bookmark.