Emerging agentic AI trends reshaping software development

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 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, innovation, and the creative aspects of software development.

However, 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.

AI agents will complement, not replace, other AI tools

The 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.

GitLab research 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.

AI 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.

Looking 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.

Enterprise-grade, secure AI agents will be mandatory

As 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.

In 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.

With 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.

Learn more about security guardrails for AI agents in DevSecOps, from compliance controls and infrastructure protection to user access management.

Agents will be critical for addressing technical debt at scale

Many organizations struggle with legacy codebases that constrain innovation and consume maintenance resources. GitLab research 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.

Agentic 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.

The 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.

Human-AI partnership will be key

As 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.

Effective partnerships include:

• Clear delineation of AI versus human responsibilities
• Transparent processes for reviewing AI-generated work
• Training programs that help teams adapt to new workflows
• Metrics that capture the combined impact of human-AI collaboration

Successful 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.

Preparing for an agentic future

Embracing 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.