Presented by Outshift by Cisco
Generative AI has significantly transformed business productivity, particularly through agentic AI, which shows great promise for enhancing software development and developer satisfaction. As software increasingly influences every aspect of our lives, from smartphones to smart cities, the methods for developing and securing code are experiencing a profound shift. Agentic AI is not merely augmenting developers’ abilities; it is redefining the entire software development lifecycle.
According to a recent GitHub survey, developers spend a substantial amount of their time tackling necessary yet monotonous tasks, such as bug fixes and waiting for builds. This is precisely where agentic AI can make a difference, reshaping the software development landscape.
“In today’s environment, our goal is to minimize the mundane tasks that developers face, allowing them to focus on innovative solutions,” said Vijoy Pandey, SVP of Outshift. “Currently, we’re seeing assistants that provide suggestions, code snippets, and security recommendations to streamline the software development lifecycle.”
GitHub Copilot, for instance, enables developers to produce up to 60% of their code with AI-driven assistance, marking just the beginning of this transformation.
“It’s a game changer,” said Mike Hanley, GitHub’s chief security officer and SVP of engineering, emphasizing the extensive reach of AI agents across the software development process. “We've witnessed significant productivity gains, with Copilot helping developers produce much of their code effectively.”
Hanley noted that AI tools have been generally available for less than two years, yet adoption has soared, fundamentally altering how developers approach software creation.
Agentic AI: Beyond Traditional Assistants
Agentic AI represents a shift from mere assistance to proactive, independent management of complex workflows tailored to specific domains.
“Think of agentic AI as a team of expert colleagues,” stated Pandey. “Similar to how no single individual oversees all aspects of software development, a network of AI agents will collaborate, learn, and address larger challenges, while humans remain integral to the process.”
Agentic AI empowers DevOps teams by streamlining continuous integration and continuous deployment (CICD) processes, enabling faster go-to-market strategies. Crucially, it also integrates security and privacy from the outset, a significant advantage for enterprise app development amid a shortage of SecOps specialists.
Security by Design
Traditionally, security measures have been implemented late in the software development lifecycle, often as part of post-code reviews. The integration of agentic AI is revolutionizing this approach.
“Developers now have the benefit of receiving secure coding suggestions in real-time via tools like Copilot,” Hanley explained. “This allows for security experiences to be embedded early in the process, effectively reducing vulnerabilities at a much lower cost.”
For instance, the newly available Copilot Autofix offers real-time explanations of potential issues and provides one-click solutions, exemplifying how agents can resolve specific problems more efficiently than human intervention. The integration of bug-detecting agents throughout the workflow ensures that security becomes synonymous with software development, maintaining high productivity levels without the friction of traditional security practices.
“That’s the desired outcome for a developer’s experience,” Hanley remarked. “With software’s critical role in society, AI will transform development by mitigating vulnerabilities right from the start.”
Agentic AI's proactive approach means that once vulnerabilities are identified, they can be addressed immediately, shifting from a reactive to a proactive security model.
Preparing for Agentic Workflows
The concept of agentic workflows is still evolving, largely existing within the realm of AI assistants. To prepare for a future centered on AI agents, organizations should start by embracing existing assistants, deploying them in small projects, and gradually scaling their use. This not only prepares teams for responsible AI practices but also optimizes code repositories for future training of AI agents.
As mundane, repetitive tasks increasingly fall to AI, businesses must adapt. “You can't ignore this evolution,” Hanley warned. “Understanding how to leverage these tools is crucial for maintaining a competitive edge.”
Pandey echoed this sentiment: “Sooner rather than later, time-consuming functions will be handled by AI agents, allowing humans to focus on higher-order problem-solving.”
For those ready to embrace this transformation, the future of software development is brighter than ever. The pertinent question now is not if, but when and how swiftly organizations will adopt these groundbreaking AI agents. Are you prepared to step into this new era of software development?
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