Why and How to Use Agentic AI in Software Development Lifecycle

Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI. The reasons are obvious, since agentic AI can autonomously analyze data, generate actions, and execute multi-step tasks without constant human supervision.

With the growing demand for agentic AI, these tools can be used as smart assistants throughout the app development process itself.

In fact, support from agentic AI can increase task completion accuracy by 7.7% across industries.

This is especially relevant to software development, where even minor mistakes can cause disruptions or costly bugs.

In this article, we will discuss what the agentic SDLC means in practice: how agentic AI in software development supports engineers at each stage of SDLC, what it takes to structure an agentic development process, and where human oversight remains essential.

The term agentic AI encompasses AI-powered systems that can autonomously work toward achieving goals. Agentic AI solutions can independently analyze the environment or inputs, develop autonomous reasoning, take actions on their own, and proactively adapt to changing conditions.

Agentic AI often uses the capabilities of generative AI and LLMs and expands them with additional layers of reasoning and feedback loops. The image below illustrates a typical agentic AI architecture.

With its capabilities for autonomous reasoning, agentic AI can outperform generative AI solutions in many aspects. Unlike generative AI, agentic AI solutions can handle complex, multi-step workflows with little oversight. In some industries, such as banking, agentic AI has already been shown to boost productivity by up to 60%. This technology also has great potential as an assistant throughout the software development lifecycle.

The table below summarizes the key differences between generative and agentic AI.

The agentic SDLC (agentic software development lifecycle) is a delivery model in which AI agents carry out defined work across requirements, design, coding, testing, deployment, and maintenance, operating inside governed workflows with human review gates. The distinction from AI-assisted development matters.

In AI-assisted work, requirements live in individual prompts, output is reviewed without defined acceptance criteria, and useful solutions stay in private chat sessions. In an agentic SDLC, requirements are captured in version-controlled specifications, every change passes automated tests and review gates, and accepted solutions become shared context that the whole team reuses. DORA’s 2026 ROI of AI-assisted software development report supports this framing: AI acts as an amplifier, and returns depend on the underlying organizational system.

Adoption is rarely a single jump. In our AI adoption maturity model, organizations progress through five levels, from traditional delivery through AI-assisted and AI-integrated stages to AI-native and AI-autonomous operation, and each level changes workflows, governance, and team roles.

From Leobit’s experience, agentic autonomous AI systems are especially relevant as assistants in coding or software testing. However, its autonomous reasoning and automation can be relevant across all SDLC stages. Let’s explore how AI agents can assist developers and teams throughout the process. The following agent roles are emerging for each stage: planning agents that draft and validate requirements, coding agents that implement scoped tasks, review agents that check changes against standards, and testing agents that keep suites current.

Agentic AI can assist in collecting and processing information from multiple sources. This ability makes it a useful tool during the requirements gathering and discovery phase. In particular, it can:

• Extract functional and non-functional requirements
• Analyze and validate the feasibility of technical requirements
• Convert various file types into a unified data format for consistency
• Compare key project documents to identify inconsistencies and ambiguities

UI/UX designers can use agentic AI to create initial prototypes and model different user scenarios. In addition, agentic AI can assist solution architects while planning software architecture design. It can plan how components will interact, forecasting scalability or performance issues, and generating architecture diagrams aligned with best practices.

Autonomous coding is one of the most popular use cases for AI tools. In particular, writing new code was the most common use case for artificial intelligence tools in 2024, with 71% of AI users relying on them for coding assistance.

Agentic AI can autonomously generate basic code snippets or code modules. In addition, code editing tools powered by agentic AI, such as Claude Code and Cursor, can provide detailed notes that explain the logic behind specific sections of a codebase. This helps developers quickly analyze and understand how the existing code works. Such a feature is especially valuable for onboarding new software engineers on the project. For multi-agent setups on the Microsoft stack, frameworks such as Microsoft Agent Framework and Semantic Kernel, together with MCP for tool access, give agents a governed way to work with your codebase and infrastructure.

Beyond writing code, agentic AI can refactor existing codebases. AI-powered tools can assist software engineers in maintaining consistent coding styles and optimizing workflows through real-time feedback and context-aware suggestions.

AI-assisted coding solutions can also be used for DevOps workflows, such as building and managing CI/CD pipelines. In particular, agentic artificial intelligence can analyze the system’s existing codebase and infrastructure features to provide suggestions for CI/CD development and deployment. Such solutions can also automatically generate YAML or JSON configurations for CI/CD tools like GitHub Actions, GitLab CI, Jenkins, or Azure DevOps. Still, human involvement is required for writing the prompts for AI, as well as monitoring its work and outputs.

Around 62% of users apply artificial intelligence to write test cases, while 56% use it for code reviews. Agentic AI provides comprehensive support to these and other QA workflows, ensuring automated end-to-end testing.
Some critical capabilities of agentic AI for QA and testing include:

• Continuous monitoring of code changes through integrations with CI/CD pipelines
• Automated, dynamic updates of test suites to adapt to new features
• Identification of high-risk areas of code, which helps QA specialists prioritize test coverage
• Correlation of test results with performance metrics, which enables QA teams to pinpoint the root causes of failures
• Automated generation of detailed bug reports
• Automated suggestions for fixes or refactoring workflows.

By using agentic AI for software testing and QA, specialists can accelerate routine workflows, improve feedback loops, and optimize overall testing efficiency.

Agentic AI strengthens CI/CD pipelines by continuously monitoring code changes, dependencies, and configuration drift across different software versions. Such solutions make integrations with third-party services smoother by detecting integration conflicts and regressions.

Additionally, development teams can use agentic AI to simulate system behavior under varying loads. To minimize system downtime, development teams can configure agentic AI to implement automated rollback when failures or anomalies are detected.

Around 66% of users apply AI for modifying existing code, while over 55% of users rely on it to maintain legacy codebases.

In addition, agentic AI can be applied by support and maintenance teams for system monitoring. Such solutions can detect anomalies and provide smart recommendations for system optimization.

Support teams can also delegate routine workflows like patching or basic performance tuning to agentic AI solutions. With their ability to improve over time, AI agents can soon offer proactive recommendations and support continuous software optimization cycles.

A working agentic SDLC framework rests on four elements. Skipping any of them could put your project on track to join the over 40% of agentic AI projects Gartner expects to be canceled by the end of 2027, with inadequate risk controls among the leading reasons. Here are some factors that distinguish a successfully-structured agentic SDLC from the one that fails in real-world conditions.

1. Shared project context. Agents are only as good as the context they work from. Capture requirements, architecture decisions, and engineering conventions in version-controlled specifications and memory files, so every agent and every engineer works from the same source of truth.
2. Defined workflows and orchestration. Decide which agent handles which task at each stage, how agents hand work to each other, and which tools each agent may call. Protocols such as MCP standardize how agents access repositories, documentation, and external systems.
3. Verification gates on every change. Automated tests, AI evaluations, code review, and CI/CD gates should apply to every agent-produced change, the same way they apply to human-written code. This is where the confidence gap closes: trust comes from verification, and review capacity becomes the limiting factor to plan for.
4. Human oversight boundaries and knowledge capture. Configure how much autonomy agents get at each stage, keep engineers approving production changes, and record every AI-assisted change in an audit trail. Accepted solutions should flow back into the shared context, so the system improves with every sprint.

This is the operating model behind our own delivery: Leobit runs an AI-native software development lifecycle with governed workflows and human review, and our agentic AI development services apply the same system to client projects.

By applying agentic AI in the software development lifecycle, you can achieve measurable improvements in speed, quality, and long-term software efficiency. Here are the main benefits that the use of AI in SDLC can bring you.

In the 2025 DORA State of AI-assisted Software Development survey, over 80% of AI tool users reported that artificial intelligence improved their productivity. In software development, agentic AI can automate routine tasks, such as writing repetitive and basic code patterns, writing test cases, and software deployment.

By speeding up each SDLC stage, agentic AI allows teams to iterate faster to respond to market changes with agility.

Over 59% of software engineers using AI solutions report that such tools have positively impacted code quality, according to the 2025 DORA survey. Agentic AI solutions often involve multiple specialized agents that handle distinct yet interconnected tasks like monitoring, test case generation, code review, performance tuning, etc.

Teams can apply agentic AI tools while developing a solution to minimize post-release fixes. By ensuring continuous system validation and testing, such tools can also be used to ensure software quality after the release.

By automating specific workflows in the software development lifecycle, agentic AI can reduce the manual effort required for coding, testing, and maintenance. This, in turn, makes software development projects more cost-effective. As a result, organizations already using agentic AI report cost savings of 30–50% in advanced industries, according to McKinsey’s 2025 research.

When developing a software solution, you can use agentic AI to maintain optimal software performance with minimal supervision, which translates into reduced total cost of ownership (TCO).

Agentic AI systems boost innovation by enhancing experimentation and rapid development of proofs of concepts (PoCs) and prototypes. Software development teams can use their code generation and testing capabilities to quickly build and deploy an early product version, enabling rapid idea validation and functionality testing. For example, we at Leobit used agentic AI tools for suggesting code patterns while developing our PoCs, exploring the capabilities of services such as Azure AI Vision and Azure AI Document Intelligence.

The short answer is no. Agentic AI offers tremendous potential to enhance the software development process. However, such solutions cannot replace human expertise completely. Human judgment and context awareness remain essential for decision-making, handling creative tasks, and ensuring strategic oversight.

In addition, there are concerns regarding the quality of AI outputs. While overall trust in AI is growing, it’s still far from absolute.

Let’s explore the major concerns and limitations of using agentic AI independently in more detail.

Agentic AI solutions may reflect biases in their training data or deliver poor outputs due to data hallucinations. This problem is currently the most significant obstacle to AI adoption, according to 45% of business executives participating in IBM’s study on the ingenuity of generative AI. Such biases can lead to inconsistent recommendations or flawed decision-making throughout different stages of SDLC. Therefore, software engineers should validate the outputs of agentic AI and use feedback loops to minimize such risks.

One of the main limitations of agentic AI systems is that, despite their powerful reasoning capabilities, they often lack human creativity and contextual awareness. Such solutions base their decisions on historical data, which means their creativity is constrained by the patterns present in such data.

Agentic AI typically struggles to produce truly novel solutions or innovative approaches outside of what it has already learned. As a result, human involvement remains essential for making critical architectural or design decisions, especially when creativity or handling edge cases is required in software development.

Around 40% of business executives cite concerns over data privacy and confidentiality as substantial barriers to AI adoption. Advanced agentic AI systems can be enhanced with custom security configurations and advanced workflows for security monitoring and permission management.

However, to ensure secure operations of such solutions, software engineers should still maintain oversight of permissions and external data dependencies. Human involvement remains crucial for managing access, running in-depth safety audits, enforcing secure development practices, and handling other safety tasks that require contextual judgment and freedom from AI bias.

Agentic AI systems may work inefficiently with legacy software. The point is that older systems often lack modern APIs or standardized documentation. As a result, even the most up-to-date agentic AI tools may struggle to understand dependencies or automate updates. Human oversight becomes vital for guiding the integration process. We also suggest modernizing your software infrastructure before integrating AI systems to prevent compatibility issues and broken system connections that could impact the quality of agentic AI outputs.

As already mentioned, outputs of agentic AI must be evaluated at every SDLC stage. Meanwhile, human involvement is equally essential to maintaining the accuracy and reliability of the same AI system.

To do that, you need to apply continuous tuning, monitoring, and validation practices. Such tasks cannot be fully automated by AI because it can reinforce existing biases, amplify hallucinations, or create feedback loops where models validate their own flawed outputs.

Human involvement is vital for the following workflows:

• Supervising data quality
• Managing feature drift
• Fine-tuning and prompt-tuning of models
• Updating model datasets
• Verifying performance metrics like precision, recall, latency, and resource usage.

In addition, engineering teams bring a deeper understanding of context when troubleshooting issues such as model degradation.

The practical consequence for an agentic SDLC: as agents produce more of the output, engineering attention shifts to the review layer. Teams that plan review capacity, define acceptance criteria, and keep human checkpoints at production boundaries get the speed benefits without inheriting a quality debt.

Given these factors, businesses should view agentic AI as a collaborative assistant in software development rather than a replacement for human specialists. Agentic AI can be used to automate repetitive tasks, generate insights, and support brainstorming. Meanwhile, human judgment should remain essential for complex tasks or those requiring creativity.

Leobit is a custom software and AI development company that runs an AI-native software development lifecycle in daily delivery: AI is systematically embedded across requirements, engineering, testing, project management and other SDLC workflows, with experienced engineers staying in control. Across real client projects, this approach can sufficiently accelerate the product’s development, compared to traditional or ad hoc AI-assisted development. The same internal platform runs on roughly 20 AI agents supporting our own operations.

As a Microsoft Solutions Partner for Digital and App Innovation and Data & AI, we apply this experience in several ways. If you want your software built inside an agentic development process, our dedicated AI engineering teams work within Leobit’s AI-native SDLC. If you need to develop your own agentic AI tooling, we design and build it. And if you’re unsure where to start, an optimal agentic AI business case is the first thing our consultants help you define. One example of agentic delivery in practice: the intelligent shipping orchestration platform, where multi-agent AI systems automate end-to-end shipping operations for a logistics company.

The use of agentic AI in SDLC holds great potential for increased speed, software quality, cost-effectiveness, and innovation. While primarily used in coding and testing, such solutions can support all stages of the software development lifecycle, from planning to continuous support and maintenance. The key point is to view artificial intelligence as an assistant, not a replacement for human specialists, due to AI adoption challenges, such as:

• AI model bias
• Creativity concerns
• Security concerns
• Legacy system integration issues
• Support and maintenance challenges.

To minimize the impact of such issues and benefit from agentic SDLC, rely on Leobit.

Contact us and let’s find out how you can benefit from our strong AI development expertise.