Rajesh Royal

Posted on Feb 4

Claude Agent SDK: Build Agents That Work Like Claude Code

#tutorial #claudecode #productivity #beginners

The same agent loop, tools, and context management that power Claude Code — now in your hands

From: x.com/adocomplete

I saved the best for last.

For 30 days, we've explored Claude Code's capabilities: bash integration, file manipulation, Git workflows, planning modes, sandbox security, custom commands, and IDE-level intelligence. You've seen what's possible when AI deeply integrates with development workflows.

But here's the question that's been simmering underneath: What if you could build your own?

What if the same agent loop, the same tool framework, the same context management that makes Claude Code possible... was available as an SDK?

It is. And it fits in 10 lines of code.

The Problem

The gap between "AI demo" and "AI product" is enormous. You've probably experienced it:

Demo: "Look, GPT writes code when I ask it to!"
Reality: Building a reliable agent that can navigate a codebase, execute commands safely, maintain context across long interactions, and recover from errors... is months of engineering.

Most of that engineering isn't about the AI itself — it's about the infrastructure around it:

How do you manage tool execution safely?
How do you maintain context as the agent works?
How do you handle when things go wrong?
How do you give the agent the right information at the right time?

These are solved problems. Claude Code solved them. But until now, those solutions were locked inside the product.

The Solution: Claude Agent SDK

The Claude Agent SDK extracts Claude Code's battle-tested infrastructure into a framework you can use to build your own agents.

How to Use It

Here's a minimal agent in 10 lines:

from claude_agent import Agent, tools

agent = Agent(
    tools=[tools.bash, tools.file_read, tools.file_write],
    system_prompt="You are a helpful coding assistant."
)

response = agent.run("Create a Python script that fetches weather data")
print(response)

That's it. You now have an agent that can:

Execute bash commands
Read files from the filesystem
Write and modify files
Maintain conversation context
Handle errors gracefully

The Full Power

As your needs grow, the SDK scales with you:

from claude_agent import Agent, tools, safety, context

# Define custom tools
@tools.define
def deploy_to_staging(branch: str) -> str:
    """Deploy a branch to the staging environment."""
    # Your deployment logic
    return f"Deployed {branch} to staging"

# Configure safety boundaries
sandbox = safety.Sandbox(
    allowed_commands=["npm", "git", "python"],
    allowed_paths=["./src", "./tests"],
    blocked_paths=[".env", "credentials/*"]
)

# Build sophisticated context management
ctx = context.ProjectContext(
    include_patterns=["**/*.py", "**/*.ts"],
    exclude_patterns=["node_modules", "*.lock"],
    max_tokens=50000
)

# Create your agent
agent = Agent(
    tools=[
        tools.bash,
        tools.file_read, 
        tools.file_write,
        tools.grep,
        deploy_to_staging  # Your custom tool
    ],
    sandbox=sandbox,
    context=ctx,
    system_prompt="""You are a deployment assistant for the Acme platform.
    Help engineers safely deploy code to staging environments."""
)

# Run with full agent capabilities
result = agent.run(
    "Review the changes in the feature/auth branch and deploy to staging if tests pass",
    stream=True  # Stream output as it happens
)

What You Get

The Agent Loop: The same iterative reasoning that makes Claude Code effective — Claude thinks, acts, observes, and continues until the task is complete.

Tool Framework: Define tools with simple decorators. The SDK handles parameter validation, error handling, and context injection.

Safety Infrastructure: Sandbox configurations, permission systems, and execution boundaries — production-ready security.

Context Management: Smart context windowing, file chunking, and relevance ranking to keep the agent informed without overwhelming the model.

Streaming & Observability: See what your agent is thinking and doing in real-time. Debug effectively.

Pro Tips

Start with built-in tools: The SDK includes the same tools Claude Code uses. Start there before building custom ones. They're battle-tested.
Embrace the agent loop: Don't over-specify. Let the agent iterate. The loop is designed for exploration and self-correction.
Design tools around capabilities, not tasks: A good tool is reusable. file_read works for any file. deploy_to_staging is specific but generalizable.
Use sandboxes in production: Even internal tools should run in sandboxes. It's not about trust — it's about accidents.
Build incrementally: Start with a single-purpose agent. Get it working reliably. Then expand.

Real-World Use Case

An infrastructure team built an on-call assistant:

oncall_agent = Agent(
    tools=[
        tools.bash,
        kubernetes_tools,
        datadog_tools,
        slack_tools,
        pagerduty_tools
    ],
    sandbox=ops_sandbox,
    system_prompt="""You are an on-call assistant for platform engineering.
    Help debug production issues, gather diagnostic information,
    and suggest remediations. Always prioritize safety and stability."""
)

When an alert fires at 3 AM:

"Investigate the spike in API latency on the payments service."

The agent:

Queries Datadog for latency metrics
Checks Kubernetes pod status
Pulls recent deployment history
Examines logs for error patterns
Correlates findings
Suggests likely root cause and remediation

The on-call engineer wakes up to a complete diagnostic report, not a raw alert. Time-to-resolution dropped from hours to minutes.

The End Is Just the Beginning

Thirty-one days ago, we started this journey exploring Claude Code's most basic features. Today, we end with its most powerful: the ability to build agents just like it.

But this isn't really an ending. It's a beginning.

2026: The Year We Learn to Build With AI

We're at an inflection point. The first wave of AI tools showed us what AI could do. The second wave showed us how to use AI in our workflows. The third wave — the one we're entering now — is about building with AI.

Not using AI tools. Building AI tools.

The Claude Agent SDK is an invitation. The same patterns that make Claude Code exceptional — the agent loop, the tool framework, the context management — are now building blocks. What you build with them is up to you.

Maybe it's a code review agent that understands your team's conventions. Maybe it's a documentation bot that keeps your docs in sync with your code. Maybe it's an onboarding assistant that helps new engineers navigate your codebase. Maybe it's something nobody has imagined yet.

The primitives are here. The infrastructure is proven. The question is: what will you build?

Thank You

To everyone who followed this series — thank you. Thirty-one days is a commitment. I hope these articles helped you discover capabilities you didn't know existed, workflows you hadn't considered, and possibilities that excite you.

Claude Code is a tool. The Claude Agent SDK is a toolkit. But the real power has always been — and will always be — the developer wielding them.

2026 is going to be interesting. Let's build.

Day 31 of 31. The end of the series. The beginning of what you'll build next.

Series Complete! 🎉

Thank you for joining me on this 31-day journey through Claude Code. If you missed any days, here's a quick reference:

Getting Started: Day 1-5 (Basics, Terminal, Files, Context)
Productivity: Day 6-12 (Workflows, Git, Testing, Debugging)
Advanced Features: Day 13-20 (Memory, MCP, Multi-file, Personas)
Power User: Day 21-25 (Integrations, Customization, Optimization)
Mastery: Day 26-31 (Commands, Sandbox, Usage, Plan Mode, LSP, SDK)

Keep building. Keep learning. The AI-augmented developer isn't the future — it's today.

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