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newsletter.",{"config":418},{"formId":419,"formName":349,"hideRequiredLabel":113},1077,"content:shared:en-us:the-source:newsletter.yml","shared/en-us/the-source/newsletter.yml","shared/en-us/the-source/newsletter",{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"config":424,"seo":425,"content":426,"type":21,"slug":22,"_id":23,"_type":24,"title":11,"_source":25,"_file":26,"_stem":27,"_extension":28},{"layout":9},{"title":11},[427,431],{"componentName":14,"type":14,"componentContent":428},{"name":11,"headshot":429},{"altText":11,"config":430},{"src":18},{"componentName":20,"type":20},{"amanda-rueda":433,"andre-michael-braun":434,"andrew-haschka":435,"ayoub-fandi":436,"bob-stevens":437,"brian-wald":438,"bryan-ross":439,"chandler-gibbons":11,"dave-steer":440,"ddesanto":441,"derek-debellis":442,"emilio-salvador":443,"erika-feldman":444,"george-kichukov":445,"gitlab":446,"grant-hickman":447,"haim-snir":448,"iganbaruch":449,"jason-morgan":450,"jessie-young":451,"jlongo":452,"joel-krooswyk":453,"josh-lemos":454,"julie-griffin":455,"kristina-weis":456,"lee-faus":457,"nathen-harvey":458,"ncregan":459,"rob-smith":460,"rschulman":461,"sabrina-farmer":462,"sandra-gittlen":463,"sharon-gaudin":464,"stephen-walters":465,"taylor-mccaslin":466},"Amanda 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McCaslin",{"ai":397,"platform":405,"security":401},[469],{"_path":470,"_dir":471,"_draft":6,"_partial":6,"_locale":7,"config":472,"seo":475,"content":479,"type":503,"slug":504,"category":471,"_id":505,"_type":24,"title":476,"_source":25,"_file":506,"_stem":507,"_extension":28,"date":480,"description":477,"timeToRead":481,"heroImage":478,"keyTakeaways":482,"articleBody":486,"faq":487},"/en-us/the-source/ai/emerging-agentic-ai-trends-reshaping-software-development","ai",{"layout":9,"template":473,"author":22,"featured":6,"sourceCTA":474,"isHighlighted":6,"authorName":11},"TheSourceArticle","source-lp-enterprise-guide-to-agentic-ai",{"title":476,"description":477,"ogImage":478},"Emerging agentic AI trends reshaping software development","Discover how agentic AI transforms development from isolated coding to intelligent workflows that enhance productivity while maintaining security.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751464050/us6fmbypxtbjxrzxsbqy.png",{"title":476,"date":480,"description":477,"timeToRead":481,"heroImage":478,"keyTakeaways":482,"articleBody":486,"faq":487},"2025-04-14","4 min read",[483,484,485],"Agentic AI systems are evolving from simple code completion tools to autonomous agents that understand entire codebases, orchestrate complex workflows, and take proactive actions across the development lifecycle with minimal human input.","Enterprise adoption of agentic AI requires robust security guardrails, including audit trails, infrastructure protection, and access controls, allowing organizations to gain efficiency without compromising governance or compliance standards.","Successful implementation of agentic AI tools depends on establishing clear human-AI partnership models that maintain human oversight for critical decisions while leveraging automation to address technical debt and modernize legacy codebases at scale.","The future of software development lies in the balance between human innovation and artificial intelligence (AI) capabilities. Simple AI-powered code completion tools are evolving into [sophisticated agentic AI systems](https://about.gitlab.com/the-source/ai/agentic-ai-unlocking-developer-potential-at-scale/) capable of understanding entire codebases and streamlining complex workflows across the development lifecycle. Unlike AI assistants that simply respond to commands, modern autonomous agents can proactively identify problems, suggest solutions, and implement changes with minimal human input - [freeing developers to focus on higher-level problem-solving](https://about.gitlab.com/blog/gitlab-duo-workflow-enterprise-visibility-and-control-for-agentic-ai/), innovation, and the creative aspects of software development.\n\nHowever, as these changes accelerate, business leaders face important strategic choices about implementing agentic AI tools within their organizations while staying ahead of potential risks. Let’s explore a few major agentic AI trends that will reshape how enterprises develop software.\n\n## AI agents will complement, not replace, other AI tools\nThe first wave of AI development tools focused primarily on isolated tasks like code completion. The shift to AI agents isn’t about replacing these valuable tools, but rather complementing them with agentic AI systems that coordinate across the entire development ecosystem.\n\n[GitLab research](https://about.gitlab.com/developer-survey/) found that 42% of respondents use 6-10 tools in their development tech stack, and 20% use over 11 tools. What’s more, organizations using AI for software development were more likely to want to consolidate their toolchain than those not using AI. It’s clear that AI-powered development tools have the potential to boost developer productivity and efficiency, but that can’t happen if AI adds to the context-switching problem.\n\nAI agents offer a solution. They provide a single entry point for everyone building software to write code, check for vulnerabilities, update documentation, and more. AI agents can actively orchestrate the entire software lifecycle, managing complex tasks that previously required a developer to interact with three or four different tools, including AI code assistants and chat interfaces. This represents a significant advancement in how developers interact with AI systems, dramatically reducing the productivity drain caused by [context switching between tasks and tools](https://about.gitlab.com/the-source/ai/devops-leaders-fix-this-productivity-blocker-before-adding-ai/).\n\nLooking ahead, we’ll continue to see the convergence of AI agents and other AI tools as they evolve. AI agents will essentially become an orchestration layer that manages specialized systems, such as AI code assistants and security scanners, throughout the development process. At the same time, code assistants will evolve to incorporate more advanced agent-like capabilities, such as increased autonomy and more proactive problem-solving.\n\n## Enterprise-grade, secure AI agents will be mandatory\nAs agentic AI becomes more integrated into development processes, robust guardrails - comprehensive frameworks of policies, controls, and monitoring mechanisms - are emerging as a business imperative. These guardrails should govern how autonomous agents interact with development environments while maintaining the agility that makes them valuable.\n\nIn regulated industries, comprehensive audit trails must capture both AI-initiated changes and human intervention points, creating clear accountability chains that satisfy compliance requirements. For example, financial institutions and healthcare organizations will need granular access controls to ensure AI-powered agents comply with data protection regulations when handling sensitive customer or patient data.\n\nWith the rise of AI-generated code, determining the origin of specific pieces of code will also become more difficult, creating challenges in maintaining clean intellectual property rights and regulatory compliance. Organizations will need guardrails to track and verify the origin of AI-generated code while ensuring compliance with open source licensing obligations. These safeguards will allow organizations to gain the efficiency benefits of AI agents without compromising governance, security, or compliance standards.\n\n> Learn more about [security guardrails for AI agents in DevSecOps](https://about.gitlab.com/the-source/ai/implementing-effective-guardrails-for-ai-agents/), from compliance controls and infrastructure protection to user access management.\n\n## Agents will be critical for addressing technical debt at scale\nMany organizations struggle with [legacy codebases](https://about.gitlab.com/the-source/security/why-legacy-code-is-a-security-risk-and-how-ai-can-help/) that constrain innovation and consume maintenance resources. [GitLab research](https://about.gitlab.com/developer-survey/2024/ai/) found that 34% of all respondents using AI across the software development lifecycle already use AI to modernize legacy code. This is even higher in the financial services industry (46%). However, even with support from AI, code modernization is a complex process: in addition to updating the code itself, it requires testing, root cause analysis, vulnerability scanning, and documentation updates.\n\nAgentic AI is poised to help organizations make the code modernization process more efficient and reduce technical debt at scale. Agents can automatically refactor code to improve quality and reduce complexity while simultaneously migrating legacy languages like COBOL to modern alternatives. Throughout this modernization process, intelligent agents can also automatically manage all downstream dependencies, such as testing, CI/CD, and documentation.\n\nThe business impact of modernization through AI-powered agents goes far beyond cleaner code. It’s about freeing resources for innovation and accelerating response to market changes, which can provide significant competitive advantages for organizations undergoing digital transformation.\n\n## Human-AI partnership will be key\nAs autonomous agents become more capable, the relationship between humans and AI is also evolving. Successful organizations are developing clear models for human-AI collaboration that maintain appropriate human intervention while maximizing the benefits of automation.\n\nEffective partnerships include:\n- Clear delineation of AI versus human responsibilities\n- Transparent processes for reviewing AI-generated work\n- Training programs that help teams adapt to new workflows\n- [Metrics](https://about.gitlab.com/the-source/ai/4-steps-for-measuring-the-impact-of-ai/) that capture the combined impact of human-AI collaboration\n\nSuccessful agentic AI implementations maintain humans at the center of critical decisions while leveraging automation for speed and consistency. Organizations that establish this balanced partnership approach - acknowledging both the capabilities of autonomous systems and the essential value of human judgment - will likely experience better adoption rates and greater satisfaction with their AI investments.\n\n## Preparing for an agentic future\nEmbracing agentic AI represents a strategic inflection point for software development. Forward-thinking organizations are already considering how to use AI agents to orchestrate complex workflows, tackle technical debt at scale, and establish productive human-AI partnerships. As these technologies mature, companies that thoughtfully integrate autonomous agents into their development ecosystems - maintaining human judgment for critical decisions while automating routine tasks - will dramatically accelerate innovation cycles, enhance code quality, and gain significant competitive advantages in an increasingly AI-powered world.",[488,491,494,497,500],{"header":489,"content":490},"What are the key differences between agentic AI and generative AI?","While both agentic AI and generative AI leverage large language models, their purposes and behaviors differ significantly. Generative AI tools are typically reactive, producing text or code in response to prompts. Agentic AI, on the other hand, is proactive, it understands broader contexts, can maintain memory across tasks, make decisions, and take autonomous actions across the software development lifecycle. Agentic AI acts more like a collaborator than a tool, working in tandem with other systems and agents to orchestrate entire workflows.",{"header":492,"content":493},"How can agentic AI reduce developer context switching?","Agentic AI helps reduce context switching by serving as a central orchestration layer across development tasks. Instead of jumping between multiple tools for coding, testing, documentation, and vulnerability checks, developers can rely on an AI agent to manage and integrate these workflows. This creates a smoother, more focused experience, improving productivity and reducing fatigue.",{"header":495,"content":496},"What security measures are required when using agentic AI in enterprise environments?","Enterprise-grade agentic AI must be deployed with robust guardrails to ensure compliance, data protection, and infrastructure safety. These include audit trails for AI actions, multi-stage reviews, access controls for sensitive data, and safeguards against unauthorized changes to production systems. These measures help maintain trust and accountability as AI agents take on greater responsibility.",{"header":498,"content":499},"Can agentic AI help modernize legacy code?","Yes, agentic AI is well-suited to tackling technical debt. It can automatically refactor legacy code, manage downstream dependencies like testing and CI/CD, and handle migration from outdated languages to modern alternatives. This helps accelerate modernization efforts while freeing developers to focus on innovation.",{"header":501,"content":502},"How should organizations structure human-AI collaboration for best results?","The most effective agentic AI strategies establish clear partnership models between humans and AI agents. These models include defined roles, transparent review processes, and metrics to measure joint performance. Organizations that maintain human oversight for high-risk decisions while leveraging automation for speed and efficiency tend to see higher adoption rates and better outcomes.","article","emerging-agentic-ai-trends-reshaping-software-development","content:en-us:the-source:ai:emerging-agentic-ai-trends-reshaping-software-development.yml","en-us/the-source/ai/emerging-agentic-ai-trends-reshaping-software-development.yml","en-us/the-source/ai/emerging-agentic-ai-trends-reshaping-software-development",1761814442438]