AI has already changed how developers work. GitLab research 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.
This 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.
AI agents vs. AI assistants: What’s the difference?
The 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.
AI 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.
Unlike 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.
The spectrum of autonomy
One 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.
For 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.
This 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.
The power of specialization
Today’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.
We’re beginning to see the emergence of specialized agents for tasks such as:
- Code modernization (converting codebases to newer language versions)
- Security vulnerability detection and remediation
- Test generation and execution
- Performance optimization
- Documentation generation
- Root cause analysis for pipeline failures
Each 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.
What’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.
The real-world impact of AI agents
Tasks 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.
More 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.
With 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.
What to consider when adopting AI agents
To 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:
- 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, you can help developers spend more time creating value for customers without contributing to AI sprawl. The platform’s built-in reports and dashboards will also help you measure success so you know your team is on the right track.
- Seek out solutions that will work for your whole team. The best AI agents make everyone more efficient, not just a select few developers.
- 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 if you need that level of security.
- 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.
Companies 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.
Looking ahead
We’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.
Looking 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.”
Software 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.
The 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.
Next steps
AI guide for enterprise leaders: Building the right approach
Download our guide for enterprise leaders to learn how to prepare your C-suite, executive leadership, and development teams for what AI can do today — and will do in the near future — to accelerate software development.
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Download our guide for enterprise leaders to learn how to prepare your C-suite, executive leadership, and development teams for what AI can do today — and will do in the near future — to accelerate software development.
Frequently asked questions
Key takeaways
- 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.