[{"data":1,"prerenderedAt":1097},["ShallowReactive",2],{"/en-us/the-source/ai/agentic-ai-unlocking-developer-potential-at-scale/":3,"footer-en-us":57,"the-source-banner-en-us":363,"the-source-navigation-en-us":375,"the-source-newsletter-en-us":403,"article-site-categories-en-us":414,"agentic-ai-unlocking-developer-potential-at-scale-article-hero-category-en-us":416,"agentic-ai-unlocking-developer-potential-at-scale-the-source-gated-asset-en-us":440,"agentic-ai-unlocking-developer-potential-at-scale-category-en-us":455,"agentic-ai-unlocking-developer-potential-at-scale-the-source-resources-en-us":467,"agentic-ai-unlocking-developer-potential-at-scale-article-hero-author-en-us":507},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"config":8,"seo":15,"content":19,"type":49,"slug":50,"category":5,"_id":51,"_type":52,"title":7,"_source":53,"_file":54,"_stem":55,"_extension":56},"/en-us/the-source/ai/agentic-ai-unlocking-developer-potential-at-scale","ai",false,"",{"layout":9,"template":10,"articleType":11,"author":12,"featured":13,"gatedAsset":14},"the-source","TheSourceArticle","Regular","emilio-salvador",true,"source-lp-enterprise-guide-to-agentic-ai",{"title":16,"description":17,"ogImage":18},"Agentic AI: Unlocking developer potential at scale","Explore how agentic AI is transforming software development, moving beyond code completion to create AI partners that proactively tackle complex tasks.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463876/kiw4eb54r8xtzztvbozf.jpg",{"title":16,"date":20,"description":17,"timeToRead":21,"heroImage":18,"keyTakeaways":22,"articleBody":26,"faq":27},"2025-04-08","6 min read",[23,24,25],"AI agents can slash development time from weeks to hours by autonomously handling complex tasks like codebase modernization, while maintaining configurable human oversight for critical systems.","Unlike basic code assistants, AI agents can work with other agents to accomplish different tasks, freeing developers to focus on innovation and high-value problem-solving.","Specialized AI agents powered by different models can excel at specific tasks like security and testing, delivering better results than one-size-fits-all solutions.","AI has already changed how developers work. [GitLab research](https://about.gitlab.com/developer-survey/2024/ai/) found that 39% of DevSecOps professionals reported using AI for software development in 2024, up 16 percent from the previous year. AI-powered code assistants are now common tools that help teams write code faster, understand codebases, and create documentation. But now we’re seeing a big shift: the emergence of AI agents that work as active partners, not just passive helpers.\n\nThis change from reactive assistants to proactive agents is reshaping how developers build software. Agentic AI is making software creation easier for more people, driving a boom in innovation as more builders can create software that reaches billions of users. However, leaders will need to seek out agentic AI solutions with strong security and compliance guardrails to get the most out of this new wave of innovation without introducing unnecessary risk.\n\n## AI agents vs. AI assistants: What’s the difference?\nThe main difference between AI assistants and agents is how they behave. Code assistants are reactive, waiting for developers to ask questions or request tasks. While helpful for faster coding and understanding code, these assistants are passive in the development process.\n\nAI agents act more like team members. They exhibit reasoning, planning, and maintain context over different tasks, coupled with a certain degree of autonomy to make decisions, interact with other agents, and adapt to changing circumstances. With the shift to agents, AI becomes a true partner in building software.\n\nUnlike assistants that just help write code while teams handle everything else, AI agents can actively orchestrate complex processes, from security checks to compliance reviews. For example, a code review agent can automatically check code, find problems, and offer fixes. Where an assistant needs human input at each step, an agent can move between tasks based on project goals. Unlike simple assistants who can't remember past interactions or learn from mistakes, agents can also learn and adapt over time.\n\n## The spectrum of autonomy\nOne of the most powerful aspects of AI agents is their configurability and level of interaction. While some agents can be highly interactive, others can execute complex tasks in the background with limited to no human interaction. Teams can therefore set different levels of human oversight based on the agent’s work and the task’s importance.\n\nFor simple tasks like summarizing code or drafting documentation, teams might let an agent work independently, only notifying a human team member when the task is finished. For critical tasks involving key business logic or sensitive data, teams can set up approval checkpoints or closely monitor the agent’s work.\n\nThis flexibility helps balance the speed of automation with the need for human control. It’s not all-or-nothing - teams can fine-tune the level of autonomy for different types of tasks and stages of the development lifecycle.\n\n## The power of specialization\nToday’s AI code assistants usually use a single large language model. But the future will bring many specialized agents, each powered by different models built for specific tasks.\n\nWe’re beginning to see the emergence of specialized agents for tasks such as:\n- Code modernization (converting codebases to newer language versions)\n- Security vulnerability detection and remediation\n- Test generation and execution\n- Performance optimization\n- Documentation generation\n- Root cause analysis for pipeline failures\n\nEach task works best with a model built specifically for that job. This specialization allows each agent to excel at its particular task rather than trying to be a jack-of-all-trades.\n\nWhat’s emerging is an ecosystem of specialized agents working together, each powered by different language models optimized for specific tasks. This multi-model approach promises to deliver better results than trying to handle all development tasks with a single, general-purpose model.\n\n## The real-world impact of AI agents\nTasks that once took weeks can now be done in hours with AI agents. For example, updating a large Java codebase to a newer version - work that might take a team weeks - can now be handled much faster by agents.\n\nMore importantly, AI agents help developers reach their highest potential. By handling routine tasks, agents free developers to focus on what they do best: solving complex problems and creating new solutions. This isn’t about replacing developers with AI, but boosting their abilities and letting them focus on higher-level thinking, innovation, and the creative work that needs human insight.\n\nWith AI agents, developers can work at a scale never before possible for individuals or teams. This shifts work from reactive, prompt-based tasks to proactive workflows that link all parts of software creation, helping with coding, planning, design, testing, deployment, and maintenance.\n\n## What to consider when adopting AI agents\nTo prepare for rapid growth in software development and code, companies need to plan ahead. Before adding AI agents to your process, focus on these key areas:\n\n1. **Think about how to boost real productivity, not just add new tools and processes for teams to learn**. By adopting [agentic AI workflows as part of a DevSecOps platform](https://about.gitlab.com/blog/gitlab-duo-workflow-enterprise-visibility-and-control-for-agentic-ai/), you can help developers spend more time creating value for customers without contributing to [AI sprawl](https://about.gitlab.com/the-source/ai/overcome-ai-sprawl-with-a-value-stream-management-approach/). The platform’s built-in reports and dashboards will also help you [measure success](https://about.gitlab.com/the-source/ai/4-steps-for-measuring-the-impact-of-ai/) so you know your team is on the right track.\n2. **Seek out solutions that will work for your whole team**. The best AI agents make everyone more efficient, not just a select few developers.\n3. **Prioritize security and compliance**. As more and more production-ready code is generated by AI, a comprehensive DevSecOps platform is essential for secure software development at scale. If you work in a regulated industry, make sure your AI agent solution meets strict security and data privacy rules. Check if it can work offline or in [air-gapped systems](https://about.gitlab.com/the-source/ai/transforming-government-it-ai-for-air-gapped-environments/) if you need that level of security.\n4. **Look for solutions with enterprise control through human oversight**. AI agents should offer clear approval workflows and configurable guardrails that keep humans in the loop. This balance gives you the speed of automation while maintaining proper governance, which is essential for critical systems and strategic decisions.\n\nCompanies that use an end-to-end DevSecOps platform with automated security scanning, compliance guardrails, and standard workflows will be more equipped to harness the benefits of AI agents without adding unnecessary risk. Those without a platform will struggle to manage the complexity and risks of agentic AI while still delivering a safe and reliable customer experience.\n\n## Looking ahead\nWe’re just at the start of the AI agent revolution in software development. As these tools mature, we’ll see even better teamwork between human developers and AI agents, with agents becoming stronger partners in building software.\n\nLooking towards the future, there is significant potential for convergence between code assistants and AI agents. Code assistants will likely evolve to incorporate more advanced AI agent capabilities, such as increased autonomy in handling coding tasks, proactive problem-solving within the development workflow, and deeper integration with other development tools and processes. Future iterations might see code assistants taking on more complex coding tasks beyond simple generation, such as autonomously debugging, testing, and even deploying code based on high-level requirements, effectively becoming more autonomous “code agents.”\n\nSoftware has changed the world over the past five decades, but only a small fraction of people have the skills to build it. Yet these few developers reach billions through smartphones and the internet. Imagine a world where more people can build, secure, and deliver production-ready software. Agentic AI will make that happen.\n\nThe shift from passive assistants to active development partners is a big step forward in software development. As these specialized agents evolve, software development will be faster, more reliable, and more rewarding for developers working with these new AI partners.",[28,31,34,37,40,43,46],{"header":29,"content":30},"What is agentic AI in software development?","Agentic AI refers to autonomous AI agents that can reason, plan, and take initiative across tasks, unlike reactive code assistants that require human prompts. These agents act more like team members, performing complex tasks with minimal oversight and enabling proactive workflows throughout the software development lifecycle.",{"header":32,"content":33},"How do AI agents differ from traditional code assistants?","While code assistants respond to developer prompts, AI agents can independently complete multi-step tasks, coordinate with other agents, and adapt based on project goals. They can handle functions like security scans, test generation, and code reviews without needing manual intervention at every step.",{"header":35,"content":36},"What are the benefits of using AI agents for developers?","AI agents reduce manual workload by automating time-consuming tasks like updating codebases, running compliance checks, and generating documentation. This allows developers to focus on higher-value work such as innovation, problem-solving, and strategic development, ultimately accelerating delivery without compromising quality.",{"header":38,"content":39},"Can AI agents be customized for different levels of human oversight?","Yes. Teams can configure agent autonomy based on task criticality. For routine tasks, agents may operate independently, while for high-risk or business-critical operations, human approval checkpoints can be integrated to maintain governance and compliance.",{"header":41,"content":42},"Are specialized AI agents more effective than general-purpose models?","Specialized AI agents, each trained for a specific function, such as security, testing, or root cause analysis, typically outperform general-purpose models for their targeted tasks. This modular, multi-agent approach improves accuracy and efficiency by leveraging the strengths of domain-optimized models.",{"header":44,"content":45},"What should companies consider when adopting agentic AI?","Organizations should ensure that AI agents align with their security, compliance, and governance requirements. They should be integrated into an end-to-end DevSecOps platform to avoid AI sprawl, maintain control through human oversight, and support enterprise-wide adoption with consistent workflows.",{"header":47,"content":48},"How will agentic AI shape the future of software development?","Agentic AI will democratize software creation by enabling more people to build and manage production-grade software. 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newsletter.",{"config":409},{"formId":410,"formName":302,"hideRequiredLabel":13},1077,"content:shared:en-us:the-source:newsletter.yml","shared/en-us/the-source/newsletter.yml","shared/en-us/the-source/newsletter",{"categoryNames":415},{"ai":388,"platform":396,"security":392},{"_path":417,"_dir":9,"_draft":6,"_partial":6,"_locale":7,"type":418,"config":419,"seo":420,"content":423,"slug":5,"_id":437,"_type":52,"title":7,"_source":53,"_file":438,"_stem":439,"_extension":56},"/en-us/the-source/ai","category",{"layout":9},{"title":388,"description":421,"ogImage":422},"Explore expert insights on how AI is transforming software development, and how organizations can get the most out of their AI 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