Building AI Agents with Claude Code CLI: From Concept to Execution

December 1, 2025 weldonpc

Published: December 1, 2025
Author: CureCancer.info Development Team

The Developer’s Challenge

In our previous posts, we’ve shown you what our AI-powered cancer research system can do and how Claude.ai makes it possible. Today, we’re going behind the scenes to share how we actually built it using Claude Code CLI—a powerful command-line interface that transformed our development process.

What is Claude Code CLI?

Claude Code CLI is Anthropic’s official command-line tool that brings Claude’s capabilities directly into your development workflow. Think of it as having an expert AI pair programmer who can:

Write and edit code across multiple files
Search and analyze entire codebases
Execute commands and run tests
Manage complex, multi-step development tasks
Create specialized agents for specific purposes

For our cancer research platform, this meant we could rapidly prototype, test, and deploy 57 specialized AI agents with unprecedented efficiency.

Why Claude Code CLI Changed Everything

Before Claude Code CLI

Building multi-agent systems traditionally meant:

Writing thousands of lines of boilerplate code
Manually creating agent configurations
Spending weeks on integration testing
Debugging complex interaction patterns
Struggling to maintain consistency across agents

With Claude Code CLI

Our development process became:

Conversational and iterative
Focused on logic and behavior, not syntax
Rapid prototyping with immediate testing
Consistent patterns across all agents
Built-in best practices and error handling

Building Our First Agent: The Hospital Representative

Let me walk you through creating one of our core agents using Claude Code CLI.

Step 1: Starting the Conversation

$ claude
Claude Code CLI v1.0

I need to create a hospital representative agent for our cancer care system.
It should handle patient coordination, resource allocation, and
regulatory compliance.

Claude Code immediately understood the requirements and began scaffolding:

# agents/hospital/hospital_representative.py
class HospitalRepresentativeAgent:
    """
    Hospital Representative Agent
    Responsibilities:
    - Patient care coordination
    - Resource allocation
    - Regulatory compliance
    - Interdepartmental communication
    """

Step 2: Iterative Refinement

The beauty of Claude Code CLI is the conversational development process:

Me: "Add methods for checking bed availability and coordinating admissions"

Claude Code: *creates check_bed_availability() and coordinate_admission() methods*

Me: "Include HIPAA compliance checks"

Claude Code: *adds compliance validation and audit logging*

Me: "Integrate with the agent orchestrator"

Claude Code: *updates imports and adds orchestrator registration*

Each iteration took seconds, not hours.

Step 3: Testing and Validation

Can you create unit tests for the hospital representative agent?

Claude Code generated comprehensive test suites:

def test_bed_availability_check():
    agent = HospitalRepresentativeAgent()
    result = agent.check_bed_availability(
        unit="oncology",
        bed_type="isolation"
    )
    assert result.available_beds >= 0
    assert result.estimated_wait is not None

Scaling to 57 Agents

Once we had the pattern working for one agent, Claude Code CLI helped us scale rapidly:

Creating Agent Templates

Create a template for research oncology agents. We need three specialists:
1. Molecular biology focus
2. Immunotherapy focus
3. Cancer metabolism focus

Claude Code generated:

Base class with shared functionality
Three specialized subclasses
Configuration files for each
Integration tests
Documentation

Total time: Under an hour.

Maintaining Consistency

With 57 agents across 21 categories, consistency is critical. Claude Code CLI helped by:

Standardizing Interfaces

Ensure all agents implement the standard consultation protocol:
- receive_query()
- analyze_data()
- generate_recommendation()
- explain_reasoning()

Enforcing Best Practices

Add logging to all agent methods and ensure HIPAA-compliant
data handling across the entire codebase.

Updating Multiple Files

Update all 57 agents to use the new consensus building protocol

Claude Code CLI modified all agent files simultaneously, maintaining consistency while saving days of manual work.

The Agent Orchestrator: Claude Code’s Masterpiece

The most complex component of our system is the Agent Orchestrator—the conductor that coordinates all 57 agents. Here’s how Claude Code CLI helped build it:

Challenge 1: Query Routing Logic

Me: "Create intelligent routing that analyzes incoming queries and
determines which agents should participate in the consultation"

Claude Code: *implements natural language understanding, specialty matching,
and priority-based agent selection*

Challenge 2: Consensus Synthesis

Me: "Build a consensus algorithm that synthesizes input from multiple
agents, identifies agreements and disagreements, and generates
prioritized recommendations"

Claude Code: *creates sophisticated analysis logic with confidence scoring,
conflict resolution, and evidence weighting*

Challenge 3: Audit Trail Management

Me: "Every interaction should be logged to chat.md with timestamps,
agent identities, and full decision rationale"

Claude Code: *implements comprehensive logging with markdown formatting
and structured data capture*

Real Development Example: The Ethics Team

One of our most complex requirements was creating the ethics consultation team. Here’s how Claude Code CLI handled it:

Create three ethics professor agents:
1. Medical Ethics - clinical ethics and bioethics
2. Research Ethics - IRB compliance and research ethics
3. Technology Ethics - AI ethics and digital health

They need to:
- Analyze ethical dimensions of treatment decisions
- Provide balanced perspectives on complex dilemmas
- Consider cultural and religious factors
- Generate consensus recommendations
- Document minority opinions

Claude Code CLI:

Created the three agent classes with specialized knowledge bases
Implemented ethical analysis frameworks
Built collaborative consensus protocols
Added cultural sensitivity guidelines
Created documentation and test suites
Integrated with the orchestrator

Development time: Approximately 2 hours for all three agents, fully tested and documented.

Best Practices We Learned

1. Be Specific and Iterative

Start with clear requirements, then refine through conversation.

2. Leverage Claude’s Knowledge

Claude Code understands medical domain knowledge, ethical frameworks, and software engineering best practices.

3. Test Early and Often

Ask Claude Code to generate tests as you build, not after.

4. Use the Task Tool

For complex, multi-step operations, use Claude Code’s task management to break work into stages.

5. Review and Validate

Claude Code is incredibly capable, but human review ensures medical accuracy and ethical appropriateness.

The Numbers

Using Claude Code CLI, we achieved:

57 agents across 21 categories built in under 2 weeks
10,000+ lines of production code generated
Comprehensive test coverage (>90%)
Full documentation for all components
Zero security vulnerabilities in initial scan

Traditional development timeline estimate: 6-9 months

Code Quality and Maintainability

Claude Code CLI doesn’t just write code quickly—it writes quality code:

Consistent style across all files
Comprehensive error handling
Clear documentation
Type hints for better IDE support
Modular architecture for easy updates

The Development Workflow

Here’s our typical development session with Claude Code CLI:

# Start Claude Code
$ claude

# Describe what you want to build
> I need to add a new feature to the patient support system...

# Claude analyzes the codebase
[Reading existing files...]
[Understanding architecture...]

# Claude proposes approach
"I'll create a new patient support coordinator that integrates
with the existing mental health and social work agents..."

# Iterative development
> Perfect. Also add integration with insurance verification.
> Can you add unit tests?
> Update the documentation in README.md

# Run tests
> Run the test suite to verify everything works

# Deploy
> Great! Let's commit these changes with an appropriate message

Integration with Our Workflow

Claude Code CLI integrates seamlessly with:

Git for version control
pytest for testing
TensorBoard for model monitoring
DICOM libraries for medical imaging
HIPAA compliance tools for data protection

What We’re Building Next

With Claude Code CLI, we’re now developing:

1. Real-Time EHR Integration

Build connectors for Epic, Cerner, and Meditech EHR systems
with FHIR compliance and real-time data sync

2. Patient-Facing Interface

Create a secure patient portal where users can view their
multi-agent consultation results and ask follow-up questions

3. Outcome Tracking System

Implement longitudinal tracking of treatment outcomes to
continuously improve agent recommendations

Each of these complex features is now within reach thanks to Claude Code CLI’s capabilities.

The Bottom Line

Claude Code CLI transformed our development process from months of traditional coding to weeks of conversational development. More importantly, it allowed us to focus on what matters: creating AI agents that provide genuine value to cancer patients and healthcare providers.

The combination of Claude.ai’s intelligence with Claude Code CLI’s development capabilities is genuinely revolutionary for building complex AI systems.

Try It Yourself

Claude Code CLI is available at anthropic.com/claude-code

Whether you’re building medical AI, business automation, or any complex software system, Claude Code CLI can accelerate your development while maintaining quality and best practices.

Next Time: On December 15th, we’ll dive deep into the technical details of our deep learning models for medical imaging analysis—the AI engine that powers our diagnostic capabilities.

Resources

Claude Code CLI Documentation: docs.anthropic.com/claude-code
Our GitHub Repository: github.com/curecancer-ai (coming soon)
Development Blog: CureCancer.info/blog

About CureCancer.info
Building next-generation AI tools for cancer research and patient care through innovative use of Claude.ai and Claude Code CLI.

Disclaimer: This AI system is designed to assist medical professionals and is not a substitute for professional medical care.

Questions about our development process? Want to collaborate? Reach out at CureCancer.info