AI Agents Are Revolutionizing the Software Development Life Cycle
Artificial intelligence is a game changer for many areas of the technology industry, and software development is one discipline in which AI is dramatically impacting the who, what, and how of the process. Generative AI (GenAI) was the first to make its mark, and now, Agentic AI is dramatically increasing the pace at which the entire software development life cycle (SDLC) evolves. AI agents, also known as “digital workers,” are the heavy lifters in the changing SDLC.
The “2024-25 World Quality Report: Futures in Focus” found that 68% of respondents are actively using or planning to use GenAI, and that this trend also applies to agentic AI. According to Gartner, the number of enterprise software applications utilizing agentic AI is expected to increase from less than 1% in 2024 to 33% by 2028.
Organizations that embrace AI agents open the door to benefits on multiple levels, from planning and building to testing and deployment, and beyond. From a strategic perspective, the use of agents significantly increases the potential for discovering new business markets, driving product innovation and achieving breakthrough results.
On the tactical level, agentic AI brings improved speed, efficiency and quality to the SDLC. All of this, of course, is made possible by the human workforce working hand in hand with the digital workforce.
Generative AI vs. AI Agents: Their Roles and Differences
GenAI and AI agents play complementary roles in the evolving AI landscape. GenAI models — such as GPT, Claude, or Gemini — excel at generating content, including code, text, summaries, and test cases, in response to prompts. They are typically stateless and reactive, meaning they perform a task when asked, but don’t remember or plan beyond a single interaction.
AI agents, on the other hand, represent a more autonomous, context-aware layer built on top of generative models or other AI systems. Agents are designed to interpret goals, reason through tasks, access tools or APIs, manage memory and execute multistep plans — often with minimal human intervention.
For example, while a GenAI model might write a function when asked, an AI agent can proactively monitor a repository, detect a pattern, create a branch, write a patch, run tests, and open a pull request — all iteratively.
In essence, think of GenAI as the mind and the AI agents as the brain and hands. The combination of both enables more powerful use cases, especially in software delivery, where agents can coordinate across the planning, development, testing, and operations stages.
Innovative Planning
While there are many benefits to using AI agents, the cornerstone is in the planning phase of the SDLC. Agents enable the acceleration of innovation by identifying new possibilities and generating innovative solutions. Development teams can explore unconventional approaches and challenge existing paradigms in their efforts. At a more granular level, AI agents are used in gathering, extracting and refining information. They can also be used in initial software modeling and making pattern changes.
Looking ahead, organizations are beginning to envision specialized digital workers dedicated to specific disciplines within the software development life cycle. Imagine a planning agent not only creating initial plans but also collaborating directly with agents from testing, performance and security teams to iteratively refine those plans based on anticipated risks, performance profiles and security concerns.
Building Better
AI agents play a role in the building process in many ways; often, that is in the form of automated code generation. However, there are several other pivotal use cases, such as real-time error detection, reviewing and suggesting fixes to potential issues, documentation creation, programming language translation and even generating entire applications based on user prompts.
These digital workers are expected to evolve into domain experts, forming collaborative teams of agents with distinct roles and responsibilities. For instance, a code-generation agent might work in tandem with a performance-optimization agent to not only write functional code but ensure it runs efficiently across target platforms.
With these digital workers assisting with once manual tasks, the result is increased creativity and productivity.
Autonomous Testing and Agent Collaboration
Software testing is an area where AI agents and automation are further along in revolutionizing the SDLC. Currently, there are three primary methods for conducting tests: human-powered manual testing, automated testing (human-designed, machine-executed), and emerging autonomous testing (which involves minimal human intervention).
The third phase of software testing’s evolution — autonomous testing — is increasingly dependent on agentic AI. An autonomous testing agent continuously plans, generates, triggers and maintains tests to minimize risk. With the rise of code assistant agents like Copilot accelerating code generation, autonomous testing agents are crucial to maintaining quality at scale.
In a forward-looking implementation, autonomous test agents won’t operate in silos. A testing agent might collaborate with a performance engineering agent and a security validation agent to assess a software change holistically. For example, when a developer submits a new feature, the testing agent ensures it behaves as intended, the performance agent evaluates the system impact under load, and the security agent scans for vulnerabilities — all in parallel, with shared context and coordinated response strategies.
There are early-stage implementations of this model today, including predictive analysis for test prioritization and AI-driven anomaly detection. But the next phase will include orchestrated agent workflows that dynamically adapt based on environment, codebase and risk profile.
AI Agents and Security
AI agents are playing a key role in bringing DevSecOps to the forefront. DevSecOps integrates security earlier into the software development life cycle, making vulnerabilities easier and cheaper to mitigate and fix. Automation is a core principle. Security testing, vulnerability scanning and compliance checks are automated within continuous integration/continuous deployment (CI/CD) pipelines.
Digital workers extend this further. Specialized security agents can generate threat models, analyze and prioritize findings, and collaborate with testing and development agents to propose mitigations. These agents operate continuously, adapting as threats evolve.
Deployment and Beyond
The role of AI agents does not end once software has been built and tested. In automated CI/CD workflows, agents can predict optimal deployment times, detect misconfigurations, and trigger automatic rollbacks.
In advanced scenarios, deployment agents collaborate with operational agents to assess system readiness, usage trends and rollback triggers. Maintenance agents can launch pre-emptive diagnostics and apply hotfixes autonomously. Together, these form a collaborative agent team that ensures resilience, performance and compliance.
Agentic AI is especially valuable in predictive maintenance, where agents analyze telemetry data to anticipate issues. IT operations agents can then engage in troubleshooting, root-cause analysis, and solution deployment in near real-time.
Orchestrating Digital Worker Teams for Business-Centric Goals
The next evolution in software delivery isn’t just about replacing manual tasks with automation — it’s about mobilizing expert digital workers to collaborate toward a common business goal. Whether launching a new feature, enhancing user experience or eliminating a critical security risk, the future of AI in software engineering lies in orchestrating cross-functional teams of AI agents that reflect the same diversity of skill, focus and coordination as human teams.
Imagine a scenario where a product owner requests the delivery of a new capability. Instantly, a planning agent begins assembling a collaborative task force: a requirements agent consults historical feedback and compliance data, a UI/UX agent tailors the design to known customer personas, a developer agent generates code aligned to enterprise patterns, while test, performance and security agents simulate edge cases and enforce governance — all sharing state, context and objectives.
But true value comes when these agents aren’t just general-purpose tools. They are specialized digital workers, configured with knowledge of the organization’s internal processes, tech stack, market regulations and even geographical constraints. A healthcare-focused security agent will flag risks differently than one trained in financial services. A European deployment agent will enforce GDPR readiness, whereas its U.S. counterpart may prioritize HIPAA or SOC 2 compliance.
This vision turns agent orchestration into a competitive advantage. Digital workers aren’t just fast — they’re domain-aware, policy-compliant and goal-oriented. They embody enterprise intelligence, turning every software change into a business-aligned, risk-informed and user-centered opportunity.
The Human-AI Collaboration Future
What happens to human workers in the SDLC? The Salesforce “State of IT” survey, released in February, found that 96% of respondents believe AI agents will have a positive impact on the developer experience, and 92% said agentic AI will have a positive impact on their careers.
The future lies in collaborative ecosystems, where specialized digital workers perform focused, repetitive tasks and human experts guide strategy, creativity and governance. Teams of agents will act like digital colleagues — each with domain knowledge, APIs to communicate with one another, and rules to govern their collaboration.
This isn’t science fiction, but the next step in how software will be built, tested, secured, deployed and evolved.