Jaykumar Ambadas Maheshkar

Agentic AI in Cloud Engineering

Abstract:

Agentic Artificial Intelligence (Agentic AI) is rapidly transforming the cloud engineering landscape by enabling autonomous, goal-driven systems that can perceive, decide, and act across complex cloud environments with minimal human intervention. Agentic AI systems are made up of intelligent agents that can reason, plan, work together, and learn from feedback on how they are doing. This is different from traditional AI models, which only work in isolation or respond to inputs that have already been set. Cloud engineers are leveraging these capabilities to manage highly distributed, dynamic, and scalable infrastructures.
This abstract explores how agentic AI is applied across key cloud engineering domains, including infrastructure provisioning, DevOps, Site Reliability Engineering (SRE), security, and cost optimization. Agentic AI agents can autonomously design cloud architectures, select optimal services, generate infrastructure-as-code, and execute deployments while adapting to real-time constraints such as performance, compliance, and budget. In DevOps and SRE practices, agentic agents monitor telemetry data, detect anomalies, perform root cause analysis, and initiate self-healing actions such as scaling, rollback, or failover without manual escalation.
Security and governance are also enhanced through agentic AI by continuously assessing configurations, identifying misconfigurations or threats, enforcing policies, and orchestrating remediation workflows across multi-cloud environments. Furthermore, agentic AI enables intelligent FinOps by predicting usage patterns, optimizing resource allocation, and balancing cost with performance objectives.
The adoption of agentic AI in cloud engineering results in reduced operational overhead, faster incident resolution, improved system resilience, and higher engineering productivity. As cloud ecosystems grow more complex, agentic AI represents a paradigm shift from reactive automation to proactive, autonomous cloud operations, paving the way toward self-managing, self-optimizing, and resilient cloud-native systems.

Profile:

Jaykumar A. Maheshkar is a respected cloud and AI engineering leader with over 16 years of experience helping organizations modernize and scale their technology.
Throughout his career, he has built, maintained, and improved mission-critical systems on AWS, Azure, and Google Cloud, delivering faster performance, stronger security, and real business results. With a master’s degree in computer science and certifications in the fields of GenAI, data science, and cloud engineering, Jaykumar combines strong technical expertise with a practical, customer-focused mindset. He enjoys using AI/ML and automation to solve complex problems and make technology work smarter for everyone. Along the way, he continues to mentor others and stay hands-on with today’s demanding technologies to keep driving innovation forward.