From SDLC to ADLC: Are We Entering a New Era of Software Development?

Software development is evolving rapidly with the growth of Artificial Intelligence (AI).

For many years, developers have followed the Software Development Life Cycle (SDLC) to build applications. It includes requirement analysis, design, development, testing, deployment and maintenance.

However, AI agents are changing how we build software.

Unlike traditional applications that mainly follow predefined instructions, AI agents can understand goals, use tools, make decisions and perform multi-step tasks.

This evolution brings attention to a new concept: Agent Development Life Cycle (ADLC).

But what exactly is ADLC, and how is it different from SDLC?

1. What is SDLC?

Software Development Life Cycle (SDLC) is a structured process used to develop, test, deploy and maintain software applications.

Its main stages are:

• Requirement Analysis
• System Design
• Development
• Testing
• Deployment
• Maintenance

For example, when developing an e-commerce application, developers define requirements, design the interface, write code, test features and deploy the application.

2. What is Agent Development Life Cycle (ADLC)?

Agent Development Life Cycle (ADLC) is an approach to designing, developing, testing, deploying and monitoring AI agents.

AI agents are systems that can understand objectives, reason through tasks, interact with external tools and perform actions with varying levels of autonomy.

ADLC introduces additional considerations such as:

• Agent reasoning and decision-making.
• Tool and API integration.
• Context and memory management.
• Multi-agent collaboration.
• Behaviour evaluation.
• Safety and human supervision.
• Continuous monitoring.

The goal is not simply to build an AI application. It is to build an AI agent that performs tasks reliably and safely.

3. Four Major Stages of ADLC

Stage 1: Build

This stage focuses on designing and developing the AI agent.

Activities include:

• Defining the agent's objectives.
• Selecting an appropriate AI model or LLM.
• Connecting tools and APIs.
• Designing prompts and instructions.
• Implementing memory and context management.
• Defining the agent's workflow.

Example: Building a customer support agent that can understand customer questions and retrieve order information through an API.

Stage 2: Test

Testing an AI agent is different from testing traditional software.

An AI agent may produce different responses for similar inputs. Therefore, its behaviour needs continuous evaluation.

Activities include:

• Testing response accuracy.
• Evaluating reasoning and task completion.
• Checking tool usage.
• Testing unexpected inputs.
• Identifying hallucinations.
• Evaluating security and reliability.

Example: Testing whether a customer support agent retrieves the correct order details instead of generating incorrect information.

Stage 3: Deploy

After successful testing, the AI agent is deployed into a production environment.

Activities include:

• Deploying the agent using cloud infrastructure.
• Integrating it with applications.
• Configuring APIs and permissions.
• Setting access controls.
• Implementing human approval for sensitive actions.
• Managing scalability and performance.

Example: Deploying a customer support agent on a company's website to assist customers.

Stage 4: Monitor

Deployment is not the final stage.

AI agents need continuous monitoring because their behaviour can change depending on inputs, model updates, tools and external data.

Activities include:

• Monitoring agent performance.
• Tracking task completion rates.
• Identifying incorrect decisions.
• Monitoring response time and cost.
• Detecting security issues.
• Improving prompts, tools and workflows.

Example: Monitoring how frequently a customer support agent successfully resolves customer queries without human intervention.

4. SDLC vs ADLC

Important: ADLC does not replace SDLC. It extends software engineering practices to address the additional challenges of AI agents.

5. Real-World Applications of ADLC

ADLC concepts can be applied across different industries.

• Healthcare: AI agents that assist with appointment scheduling and medical information retrieval.
• Banking: Agents that assist with transaction queries and financial workflows.
• Education: Personalized learning assistants.
• Software Engineering: AI coding agents that help developers write, review and test code.
• Customer Support: Agents that understand customer problems and perform support workflows.
• Enterprise Automation: Agents that coordinate tasks across multiple business applications.

These applications require appropriate permissions, testing and human oversight.

6. Challenges in ADLC

Although AI agents provide new capabilities, developing reliable agents introduces challenges.

1. Hallucinations: Agents may generate incorrect information.
2. Security: Agents may expose sensitive information or misuse tools.
3. Reliability: Agents may fail during multi-step tasks.
4. Evaluation: Measuring agent performance is more complex than checking traditional software outputs.
5. Cost: Repeated model calls and tool usage can increase operational expenses.
6. Human Supervision: Sensitive or high-impact decisions may require human approval.

7. What Is the Future of Software Development?

The future of software engineering will likely combine traditional software development with AI-driven and agent-based development practices.

Developers will increasingly work with:

• Large Language Models (LLMs).
• AI coding agents.
• Multi-agent systems.
• Retrieval-Augmented Generation (RAG).
• Agent evaluation frameworks.
• AI observability and monitoring.
• Human-in-the-loop workflows.

Understanding both SDLC and ADLC can help developers prepare for this evolving engineering landscape.

Final Takeaway

SDLC has provided the foundation for modern software engineering. With the growth of autonomous and agent-based AI systems, development practices are expanding to address reasoning, tool usage, evaluation, safety and continuous monitoring.

Agent Development Life Cycle represents an important direction in this evolution.

The question is no longer just how we build software, but also how we build, evaluate and manage intelligent agents responsibly.

What do you think? Will Agent Development Life Cycle become an important standard in future software engineering?