Agent-Based Programming: Superpowers for Coders and Non-Coders

Article written by

Yannick NIAMKEY

Software Engineering Consulting Engineer

Discover how agentic systems are transforming software development, blurring the lines between traditional programming and no-code solutions.

Agentic programming is emerging as a revolutionary paradigm. Unlike rigid approaches, autonomous agents can understand, interpret, and execute complex tasks with a flexibility that was previously unimaginable.

What is an Agent? Definition and fundamental concepts

An agent is an autonomous software system capable of:

• Perceiving its environment
• Reasoning on the collected information
• Making decisions independently
• Acting to achieve specific goals

Key characteristics of an agent

Anatomy of an autonomous agent

┌─────────────────────────┐
│ Perception Layer        │
│ - Information gathering │
├─────────────────────────┤
│ Cognitive Layer         │
│ - Analysis              │
│ - Reasoning             │
├─────────────────────────┤
│ Decision Layer          │
│ - Planning              │
│ - Action selection      │
├─────────────────────────┤
│ Execution Layer         │
│ - Implementation        │
│ - Adaptation            │
└─────────────────────────┘

LLMs: The brain of agentic systems

Artificial Intelligence at the heart of autonomy

Large Language Models (LLMs) have become the central engine of agentic systems, radically transforming their reasoning capacity and autonomy. These models are no longer simple language processing tools, but true cognitive systems capable of:

1. Advanced Contextual Understanding
• Interpreting complex nuances
• Understanding underlying intentions
• Generating inferences beyond literal text
2. Multi-step Reasoning
• Breaking down complex problems
• Planning action sequences
• Dynamically adapting strategies

Reasoning capabilities: 2026 status report

Frontier Models vs Human Capabilities

Introduction: A new frontier

Agentic programming is emerging as a revolutionary paradigm, bridging the gap between traditional development and the world of no-code. Unlike existing rigid approaches, autonomous agents can understand, interpret, and execute complex tasks with a flexibility that was previously unimaginable.

Economic impact and ROI

Agentic systems generate significant gains for businesses:

No-Code and agentic programming: A natural convergence

Agentic systems share several key characteristics with no-code:

• Abstraction: Hiding technical complexity
• Accessibility: Allowing non-programmers to create solutions
• Flexibility: Dynamically adapting to changing needs

Transformation of the Developer into an Architect

Agentic programming offers developers a new strategic role:

• High-Level Design: Moving from line-by-line programming to defining intelligent architectures
• Orchestration: Designing systems where agents collaborate and self-organize
• Acceleration: Implementing complex solutions in a fraction of traditional time
• Innovation: Focusing on business logic rather than technical details

Example: Application development by specialized agents

Process participants

1. Product Owner Agent
• Analyzes business needs
• Defines specifications
• Evaluates proposals
2. UX/UI Agent
• Designs the user experience
• Generates mockups
• Proposes intuitive interactions
3. Developer Agent
• Translates specifications into code
• Chooses appropriate technologies
• Implements functionalities
4. Tester Agent
• Generates test cases
• Verifies compliance
• Identifies anomalies

Collaborative Workflow

Product Owner → UX/UI → Developer → Tester
      ↑                     ↓
      └─────── Feedback ────┘

Tools for developers and creators

Universal approach: LLM via HTTP

Key point: LLMs are at the center of all agentic solutions and are accessible via standard HTTP APIs. This makes agentic programming language-independent and generalizable to all development environments.

Advantages of the HTTP approach:

• Universality: Compatible with all languages (Python, C#, JavaScript, Java, Go, Rust…)
• Flexibility: No dependency on a specific framework
• Portability: Easy to migrate between languages
• Accessibility: Even languages with few AI libraries can create agents

Generic example of LLM call via HTTP:

POST https://api.openai.com/v1/chat/completions
Content-Type: application/json
Authorization: Bearer YOUR_API_KEY

{
  "model": "gpt-4",
  "messages": [
    {
      "role": "system",
      "content": "You are an assistant specialized in agentic programming."
    },
    {
      "role": "user",
      "content": "Explain what an autonomous agent is."
    }
  ],
  "temperature": 0.7
}

Programming Frameworks

Note: Python remains the most widespread language in the AI ecosystem due to its wealth of libraries, but the concepts apply universally via the HTTP approach.

Python Frameworks

1. LangChain (Python)
• Creation of custom agents
• Multi-tool integration
• Maximum flexibility
• Market: +150% adoption in 2025, 2M+ active developers
2. CrewAI
• Multi-agent systems
• Collaboration between specialized agents
• Sequential or hierarchical workflow
• Market: 200% growth Q3 2025, used by 500+ companies

C# Frameworks

1. Microsoft Agent Framework (C#)
• Official Microsoft framework for .NET
• Native integration with Azure OpenAI
• Support for multi-languages C#, F#, VB.NET
• Market: Launched in 2025, 500K+ .NET developer adopters
• Advantages: Seamless integration with the Microsoft ecosystem (Azure, Visual Studio, GitHub)
2. Semantic Kernel (C#)
• Developed by Microsoft Research
• Modular and extensible architecture
• Support for multi-providers (OpenAI, Azure, Hugging Face)
• Market: 300K+ NuGet downloads, used in 200+ companies
• Advantages: Ideal for existing .NET enterprise applications

Example with Microsoft Agent Framework in C#:

using Microsoft.Agents;
using Microsoft.Agents.OpenAI;

// 1. LLM client configuration
var llmClient = new OpenAIClient("YOUR_API_KEY");

// 2. Agent definition
var agent = new AgentBuilder()
    .WithName("Assistant")
    .WithSystemPrompt("You are an expert programming assistant.")
    .WithLLM(llmClient)
    .WithTools(new CalculatorTool())
    .Build();

// 3. Agent execution
var response = await agent.ProcessAsync(
    "Calculate 15 * 23 and explain the result."
);

Console.WriteLine(response.Content);

Example with Semantic Kernel in C#:

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Connectors.OpenAI;

// 1. Kernel initialization
var kernel = Kernel.CreateBuilder()
    .AddOpenAIChatCompletion("gpt-4", "YOUR_API_KEY")
    .Build();

// 2. Adding plugins (tools)
kernel.ImportPluginFromFunctions("Calculator",
    new[]
    {
        kernel.CreateFunctionFromMethod(
            (double x, double y) => x * y,
            "Multiply",
            "Multiplies two numbers"
        )
    });

// 3. Execution with chaining
var result = await kernel.InvokePromptAsync(
    "Calculate 15 * 23 using the calculator."
);

Console.WriteLine(result);

No-Code Solutions

1. OpenCode
• Visual assembly of workflows
• Integration of pre-configured agents
• Accessibility for non-programmers
• Market: 50K+ users, raised €15M in 2025
2. Roo-Code
• Automatic software generation
• Contextual adaptation
• Learning from user needs
• Market: 100K+ generated projects, partnerships with 200+ companies
3. Claude Code
• AI-assisted code generation
• Understanding intentions
• Contextual suggestions
• Market: Integrated into 1500+ IDEs, 5M+ active users

Solving Complex Problems

Concrete Example: Project Management Application

Scenario: An SME wants a custom project tracking tool.

Agentic Process:

1. Product Owner agent analyzes specific needs
2. UX/UI agent designs a suitable interface
3. Developer agent implements features
4. Tester agent validates and optimizes

Result: A tailor-made solution in a fraction of traditional time.

Advanced Workflow: Multi-Agent System with Error Handling

from crewai import Agent, Task, Crew, Process
from langchain_openai import ChatOpenAI

# LLM Configuration
llm = ChatOpenAI(model="gpt-4", temperature=0.7)

# Specialized agents with error handling
product_owner = Agent(
    role="Product Owner",
    goal="Analyze needs and define specifications",
    backstory="Business analysis expert with 10 years of experience",
    llm=llm,
    verbose=True,
    allow_delegation=True,
    max_iter=3  # Limits attempts to avoid infinite loops
)

ux_designer = Agent(
    role="UX/UI Designer",
    goal="Create intuitive and accessible interfaces",
    backstory="UX Designer passionate about accessibility",
    llm=llm,
    verbose=True,
    allow_delegation=False
)

developer = Agent(
    role="Full Stack Developer",
    goal="Implement features with clean code",
    backstory="Senior developer expert in React and Python",
    llm=llm,
    verbose=True,
    allow_delegation=True,
    tools=[CodeGeneratorTool(), DatabaseTool()]  # External tools
)

tester = Agent(
    role="QA Tester",
    goal="Validate quality and identify bugs",
    backstory="QA engineer with expertise in automated testing",
    llm=llm,
    verbose=True,
    allow_delegation=False
)

# Tasks with validation criteria
task1 = Task(
    description="Analyze requirements for the project management app",
    agent=product_owner,
    expected_output="Detailed specifications document"
)

task2 = Task(
    description="Create UI/UX mockups for the application",
    agent=ux_designer,
    expected_output="Interactive mockups and wireframes",
    context=[task1]  # Dependency on previous task
)

task3 = Task(
    description="Implement core functionalities",
    agent=developer,
    expected_output="Functional and documented code",
    context=[task1, task2]
)

task4 = Task(
    description="Test application and generate quality report",
    agent=tester,
    expected_output="Test report with identified bugs and fixes",
    context=[task3]
)

# Crew with process management
crew = Crew(
    agents=[product_owner, ux_designer, developer, tester],
    tasks=[task1, task2, task3, task4],
    process=Process.sequential,  # Sequential execution
    verbose=True,
    memory=True  # Enables memory between tasks
)

# Execution with error handling
try:
    result = crew.kickoff()
    print("✓ Workflow completed successfully")
except Exception as e:
    print(f"✗ Workflow error: {e}")
    # Recovery and retry logic

Technical Mechanisms of Agents

Internal Architecture of an Agent

1. Strategic Prompt Engineering
• System prompts: Defines role and behavior rules
• Few-shot learning: Examples to guide responses
• Chain-of-thought: Step-by-step reasoning
• Self-reflection: The agent analyzes and corrects its own errors
2. Memory Mechanisms
• Short-term memory: Context of the current conversation (tokens)
• Long-term memory: Persistent storage (vector databases)
• Episodic memory: Retrieval of relevant past experiences
3. Chaining Strategies
• Sequential: Tasks executed one after the other
• Hierarchical: Manager agent coordinates sub-agents
• Recursive: The agent calls itself to solve sub-problems

Reliability and Testability

Current Challenges:

• LLM Hallucinations (incorrect information)
• Inconsistency in responses
• Difficulty in guaranteeing determinism

Validation Strategies:

# Validation example with unit tests
import pytest
from langchain.agents import AgentExecutor

def test_agent_response_consistency():
    """Test that agent provides consistent responses"""
    agent = create_test_agent()
    responses = []
    
    # Run the same question 5 times
    for _ in range(5):
        result = agent.invoke({"input": "What is an agent?"})
        responses.append(result["output"])
    
    # Verify that responses are semantically similar
    assert all("autonomous" in r for r in responses), "Inconsistency detected"

def test_agent_error_handling():
    """Test that agent handles errors gracefully"""
    agent = create_test_agent()
    
    # Impossible question
    result = agent.invoke({"input": "Calculate the square root of -1"})
    assert "error" in result["output"].lower() or "impossible" in result["output"].lower()

Production Best Practices:

• Limit the number of iterations (max_iter)
• Implement guardrails
• Log all interactions for auditing
• Use low-temperature models for production
• Systematically test before deployment

Key Benefits

• Speed: Accelerated development
• Personalization: Precise adaptation
• Accessibility: Reduction of technical barriers
• Innovation: Emergence of creative solutions

Augmentation vs Replacement

Agentic systems based on LLMs are evolving rapidly but retain crucial limitations:

• Human Expressiveness: Only humans can formulate deep and complex needs
• Ethical Judgment: Moral assessment remains an exclusively human domain
• Creative Intuition: The ability to generate radically new concepts

Human-Agent Collaboration

Agentic systems are becoming cognitive partners:

• Amplify human capabilities
• Manage operational complexity
• Free human creativity for high-level tasks

Conclusion

Agentic programming is not a replacement, but an evolution. It offers a bridge between traditional code and no-code solutions, allowing everyone to become a technology creator.

The future of development is collaborative, intelligent, and accessible.

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