As we close out 2024, the biggest thing since the internet itself has been the transformative force that is Generative AI (GenAI). Over the past two years, these advanced AI models have emerged from research labs and pilot projects to become integral components of how organizations build software, secure infrastructures, manage cloud-native environments and deliver seamless IT services. From code generation to automated security responses, from cost optimization to creative troubleshooting, GenAI’s fingerprints are now everywhere.
In fact beyond tech and code, GenAI promises civilization wide changes that make both politicians and even oligarchs nervous.
But just as we’re getting comfortable with GenAI, a new paradigm is on the horizon for 2025: The rise of “agentic AI.” Whereas today’s GenAI systems excel at producing content or guiding decisions when prompted, agentic AI promises to take autonomy to a new level—proactively making decisions, orchestrating entire workflows and continuously learning from the environments they manage. As we look forward, it’s clear that the combination of GenAI and agentic AI will redefine the landscape of application development (AppDev), security, cloud-native architectures, platform engineering and IT service management (ITSM).
2024: GenAI as a Co-Pilot Across the Tech Spectrum
This year, GenAI took on the role of an invaluable co-pilot for teams across the IT spectrum. Developers quickly embraced these large language models (LLMs) and code generation tools, integrating them directly into their IDEs and CI/CD pipelines. The result was a significant acceleration in development velocity: Repetitive boilerplate code, integration logic and even test cases were generated by AI assistants, freeing developers to focus on innovation and business logic.
Security teams leveraged GenAI to detect anomalies in logs and metrics, highlight subtle misconfigurations in Kubernetes manifests, or identify unusual patterns of access control changes that might indicate a breach. While human expertise remained essential, these AI-driven insights reduced mean time to detect (MTTD) and mean time to respond (MTTR), improving organizations’ defensive posture.
Cloud-native operations—from managing containerized applications at scale to optimizing resource usage—benefited from GenAI-driven recommendations on workload placement, autoscaling policies and network configurations. Observability teams tapped into AI models that correlated massive amounts of telemetry data, surfacing root causes in seconds rather than hours. Similarly, platform engineering teams relied on GenAI to create “golden paths” for developers—pre-approved templates and infrastructure configurations that embodied best practices and security guardrails.
ITSM processes also evolved, with GenAI-driven chatbots and virtual assistants resolving routine support tickets, suggesting remediation steps for common issues and even learning from prior incidents to offer better solutions over time. This year, the goal of “shifting left” and embedding security, reliability and compliance into every step of the development lifecycle was turbocharged by AI’s ability to provide instant feedback and recommendations.
Beyond Chatbots and Code Suggestions: The Shift to Agentic AI in 2025
As we turn to 2025, we’re on the cusp of another leap forward: The rise of agentic AI. If GenAI was the brilliant but passive co-pilot waiting for instructions, agentic AI is the autonomous agent that understands objectives, navigates complexities, and takes action without constant human prompting. This shift has profound implications.
Imagine an AppDev workflow where an agentic AI not only generates code in response to a developer’s request, but also proactively refactors aging codebases overnight, runs test suites and updates documentation as dependencies evolve. In security, agentic AI could continuously monitor an environment for threats, apply relevant patches, rotate keys and adjust firewall rules as risks emerge—without waiting for a human to tell it what to do. It could also collaborate with other AI agents specialized in compliance to ensure that every deployment remains within policy boundaries.
In cloud-native environments, agentic AI could juggle workloads across multiple clusters and clouds, anticipating traffic spikes, negotiating resource availability with other agents, and initiating zero-downtime failovers in response to early signs of a service degradation. For platform engineering, the future might involve a landscape of intelligent agents maintaining internal developer platforms: Spinning up secure environments, retiring outdated services, and automatically surfacing new best practices as they become relevant.
ITSM could see a radical transformation. Instead of human operators triaging tickets, agentic AIs might become digital site reliability engineers (SREs), identifying problems, queuing them for resolution, and even addressing them directly if they fall within known resolution patterns. When a truly novel issue arises, the AI agent could escalate to a human operator, but not before collecting logs, metrics and a synthesized overview of potential root causes.
How Agentic AI Changes the Game
What sets agentic AI apart from today’s GenAI is not just autonomy—it’s continuous learning and improvement in the face of dynamic systems. Cloud-native ecosystems are inherently complex, with ephemeral services, evolving APIs and shifting threat landscapes. Agentic AIs can continuously refine their models based on real-time feedback, adapting governance rules, policies and runbooks as conditions change.
For example, consider the software supply chain. Today, GenAI can help identify vulnerable dependencies when prompted. An agentic AI might continuously scan container registries and package repositories, automatically quarantine suspicious artifacts and suggest secure replacements. This automated vigilance ensures that as soon as a new vulnerability is disclosed, the system begins to respond—much faster than any human team could react at scale.
Challenges and Considerations
Of course, this shift to agentic AI comes with new responsibilities. Trust, explainability and ethics are top of mind. As AI agents assume more control and make autonomous decisions, organizations must ensure that policies and guardrails are clearly defined. Observability, ironically, must also extend to these AI-driven agents themselves—teams will need to understand why a certain decision was made, be able to audit it, and override it if necessary.
Compliance and regulatory considerations will intensify as agentic AIs start making choices that impact data residency, access controls and user privacy. It will be crucial to embed compliance-as-code and policy-as-code frameworks that these AI agents can reference, ensuring that they operate within well-understood boundaries.
2025 and Beyond: A New Era of Intelligent Automation
Looking ahead, the most successful organizations in 2025 will be those that learn to orchestrate human expertise and agentic AI cooperation effectively. Humans will still set strategic goals and provide oversight, while AI agents handle the tactical execution, continuously improving as they gather more data and feedback.
In AppDev, we’ll see agentic AI streamlining large-scale refactoring projects, ensuring code quality and automating test and release cycles end-to-end. In security, AI-driven sentinel agents will not just detect threats—they’ll respond in real-time, deploy countermeasures and collaboratively share knowledge with other agents and teams. Cloud-native infrastructures will benefit from elastic, self-adjusting clusters that optimize for cost, performance and resilience automatically. Platform engineering will evolve into a discipline of defining strategic patterns and letting AI agents maintain them dynamically, while ITSM will become a domain of proactive problem prevention rather than reactive issue resolution.
Conclusion
2024 laid the groundwork as GenAI moved from experimentation to mainstream adoption, assisting teams across AppDev, security, cloud native, platform engineering and ITSM. As we approach 2025, the promise of agentic AI looms large—intelligent entities that don’t just wait for commands but proactively shape the systems they manage.
The coming year will challenge us to balance autonomy with governance, speed with safety, and innovation with oversight. Those who navigate these challenges successfully will unlock a new era of intelligent automation, redefining what it means to build, secure and operate technology at scale.
