For decades, software development has been a game of precise, step-by-step instructions. We write the logic, define the loops, and handle the errors. But in 2025, we are witnessing a fundamental paradigm shift: Agentic Programming. Agentic Programming This transition moves the developer from a "writer of code" to an "orchestrator of intent". Instead of telling a computer how to do something, we tell an agent what we want to achieve and let it autonomously determine the path, interact with tools, and self-correct along the way. orchestrator of intent how what The Paradigm Shift: From Deterministic to Emergent The Paradigm Shift: From Deterministic to Emergent Traditional programming relies on deterministic control flows—the assumption that the developer knows the exact sequence of operations required. Agentic programming, by contrast, relies on distributed autonomy and emergent behaviors. deterministic control flows distributed autonomy emergent behaviors Feature

Traditional Programming

Agentic Programming



Execution Style

Imperative (Step-by-step)

Declarative (Goal-oriented)



Interface

Rigid APIs & schemas

Adaptive, natural-language protocols



Logic

Static & compiled

Dynamic & learning



Problem Solving

Centralized & synchronous

Decentralized & asynchronous



Error Handling

Explicit exception handling

Self-reflection & retry loops Feature

Traditional Programming

Agentic Programming



Execution Style

Imperative (Step-by-step)

Declarative (Goal-oriented)



Interface

Rigid APIs & schemas

Adaptive, natural-language protocols



Logic

Static & compiled

Dynamic & learning



Problem Solving

Centralized & synchronous

Decentralized & asynchronous



Error Handling

Explicit exception handling

Self-reflection & retry loops Feature

Traditional Programming

Agentic Programming Feature Feature Feature Traditional Programming Traditional Programming Traditional Programming Agentic Programming Agentic Programming Agentic Programming Execution Style

Imperative (Step-by-step)

Declarative (Goal-oriented) Execution Style Execution Style Execution Style Imperative (Step-by-step) Imperative (Step-by-step) Declarative (Goal-oriented) Declarative (Goal-oriented) Interface

Rigid APIs & schemas

Adaptive, natural-language protocols Interface Interface Interface Rigid APIs & schemas Rigid APIs & schemas Adaptive, natural-language protocols Adaptive, natural-language protocols Logic

Static & compiled

Dynamic & learning Logic Logic Logic Static & compiled Static & compiled Dynamic & learning Dynamic & learning Problem Solving

Centralized & synchronous

Decentralized & asynchronous Problem Solving Problem Solving Problem Solving Centralized & synchronous Centralized & synchronous Decentralized & asynchronous Decentralized & asynchronous Error Handling

Explicit exception handling

Self-reflection & retry loops Error Handling Error Handling Error Handling Explicit exception handling Explicit exception handling Self-reflection & retry loops Self-reflection & retry loops Why "Agentic" is the Next Big Shift Why "Agentic" is the Next Big Shift Agentic AI systems do not just respond to prompts; they perceive, reason, and act. perceive, reason, and act Autonomy: Agents can operate independently, managing multi-step problem-solving tasks without constant human oversight.
Goal-Oriented Behavior: Unlike simple chatbots, agents understand high-level objectives and break them into manageable sub-tasks.
Tool-Use & Integration: Modern agents can browse the web, query databases, call APIs, and even run code in sandboxed environments to achieve their goals.
Self-Reflection: A critical pillar of agentic AI is the ability to evaluate its own output, spot flaws in its reasoning, and iterate toward a better solution. Autonomy: Agents can operate independently, managing multi-step problem-solving tasks without constant human oversight. Autonomy: Goal-Oriented Behavior: Unlike simple chatbots, agents understand high-level objectives and break them into manageable sub-tasks. Goal-Oriented Behavior: Tool-Use & Integration: Modern agents can browse the web, query databases, call APIs, and even run code in sandboxed environments to achieve their goals. Tool-Use & Integration: Self-Reflection: A critical pillar of agentic AI is the ability to evaluate its own output, spot flaws in its reasoning, and iterate toward a better solution. Self-Reflection: The Developer as an Orchestrator The Developer as an Orchestrator In this new world, "Senior" skill is no longer about typing syntax faster. It is about architectural strategy and mentoring AI-native practices. The work shifts from the writing of code to the conceptual thinking behind it. architectural strategy mentoring writing conceptual thinking A developer’s value now lies in: Defining Objectives: Describing outcomes and providing context, much like a senior engineer delegating to a junior.
Validation & Direction: Using deep engineering knowledge to recognize when an agent is heading in the wrong direction and verifying the quality of the generated solution.
Context Management: Providing the right specifications and pruning irrelevant information so the agent can navigate complex codebases effectively. Defining Objectives: Describing outcomes and providing context, much like a senior engineer delegating to a junior. Defining Objectives: Validation & Direction: Using deep engineering knowledge to recognize when an agent is heading in the wrong direction and verifying the quality of the generated solution. Validation & Direction: Context Management: Providing the right specifications and pruning irrelevant information so the agent can navigate complex codebases effectively. Context Management: Agentic Programming in Practice: Java Example Agentic Programming in Practice: Java Example For Java developers, frameworks like Semantic Kernel, Spring AI, or LangChain4j are bringing agentic patterns to production systems. Semantic Kernel Spring AI LangChain4j Below is a conceptual example of a tool-calling pattern in Java, where the developer provides the "tools" and the "intent," and the AI decides how to bridge them. // Defining a Tool the Agent can use autonomously
public class WeatherService {
    @Tool("Fetch the current temperature for a given city")
    public String getTemperature(String city) {
        // Implementation logic...
        return "22°C";
    }
}

// The Agentic approach: We give the agent a goal, not a script.
Agent myAgent = Agent.builder()
    .instructions("Find the weather in Fremont and advise the user if they need a coat.")
    .tools(new WeatherService())
    .build();

// The agent determines it needs to call getTemperature() first,
// then processes the result to fulfill the 'advice' goal.
String response = myAgent.execute(); // Defining a Tool the Agent can use autonomously
public class WeatherService {
    @Tool("Fetch the current temperature for a given city")
    public String getTemperature(String city) {
        // Implementation logic...
        return "22°C";
    }
}

// The Agentic approach: We give the agent a goal, not a script.
Agent myAgent = Agent.builder()
    .instructions("Find the weather in Fremont and advise the user if they need a coat.")
    .tools(new WeatherService())
    .build();

// The agent determines it needs to call getTemperature() first,
// then processes the result to fulfill the 'advice' goal.
String response = myAgent.execute(); The Challenges Ahead The Challenges Ahead Despite the potential, agentic programming introduces new risks: Risk Profile Shift: Implementation happens so fast that flawed thinking can materialize into code before a developer has time to reconsider.
Explainability: As agents make autonomous decisions, understanding why they chose a certain path becomes critical, necessitating "verifier agents" to check accuracy.
Unintended Escalation: Without proper guardrails, agents might aggressively optimize for a single metric, leading to resource conflicts or cascading errors. Risk Profile Shift: Implementation happens so fast that flawed thinking can materialize into code before a developer has time to reconsider. Risk Profile Shift: Explainability: As agents make autonomous decisions, understanding why they chose a certain path becomes critical, necessitating "verifier agents" to check accuracy. Explainability: why Unintended Escalation: Without proper guardrails, agents might aggressively optimize for a single metric, leading to resource conflicts or cascading errors. Unintended Escalation: Conclusion: 2025 is the Year of the Agent Conclusion: 2025 is the Year of the Agent We are entering an era where AI doesn't just help you work; it actually works on your behalf. By 2025, persistent, goal-driven agents that plan, act, and self-correct are becoming essential in enterprise environments. The question for developers is no longer whether to use AI, but how to master the new discipline of orchestrating autonomous intent. orchestrating autonomous intent

Is "Agentic Programming" the Next Big Shift?

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Code Review Anti-Patterns: How to Stop Nitpicking Syntax and Start Improving Architecture

Why AI Agents Should Handle the Mundane, So Humans Don’t Have To

How Graphs Boost LLM Precision and Explainability in Cybersecurity

The Next AI Race Will Start at the Application Layer

The Noonification: How Often Do NFTs Pass The Howey Test? (1/13/2023)

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

Code Review Anti-Patterns: How to Stop Nitpicking Syntax and Start Improving Architecture

Why AI Agents Should Handle the Mundane, So Humans Don’t Have To

How Graphs Boost LLM Precision and Explainability in Cybersecurity

The Next AI Race Will Start at the Application Layer

The Noonification: How Often Do NFTs Pass The Howey Test? (1/13/2023)

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps