Join Dr. Bratin Saha, Chief Product and Technology Officer at DigitalOcean, as he explores how AI agents are reshaping cloud infrastructure.
Discover the challenges developers and digital-native enterprises face, the innovations driving agentic cloud, and key considerations for deploying agents at scale.
Gain insights from Bratin’s experience leading AI, ML, and data infrastructure at AWS, Nvidia, and Intel, plus lessons from building one of AWS’s fastest-growing multi-billion-dollar businesses.
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How do we validate that AI agents deployed in the cloud can scale to handle sudden surges in concurrent requests?
How might the rise of agentic cloud fundamentally reshape traditional cloud operating models and redefine the role of humans in managing autonomous environments?
What are the most significant technical challenges in integrating and scaling agentic AI systems within existing, potentially legacy, cloud environments?
Agent-to-agent testing: when machines start validating machines, who validates them?
What can individuals in dev or SDET capacity learn to upscale to keep up with the AI Gold Rush?
What are the longer-term implications of agentic cloud on traditional cloud operating models and the role of human oversight in fully autonomous cloud environments?
What metrics will define success in an agent-based cloud? Agent uptime?
In the next 2-3 years, what’s the next step forward for innovation and breakthroughs in agentic AI and its application in building and managing a future-proofed cloud infrastructure?
How do we ensure accountability when agents in the cloud make decisions that affect scalability, availability, or compliance?
From your experience at AWS and now DigitalOcean, what key differences do you see in how startups vs. large enterprises adopt agent-based systems?
99% want to build agents, then do you think we have that much need for agents? Wont too many agents complicate the system/ Is there a standard on how and when to decide building an agent?
Considering the potential for significant disruption, how can orgs effectively navigate the workforce implications of agentic cloud adoption, including talent development and new roles that will emerge from human-agent collaboration?
What happens when Copilot generates a flaky test who’s to blame the tool, the prompt, or the tester?
As agentic AI become more common, what best practices and architectural patterns are emerging for building resilient and adaptable agentic cloud infrastructures, particularly in multi-cloud or hybrid environments?
How do you address biases in training data or unintended consequences of autonomous actions in multi-tenant cloud environments?
What risks do you see in giving agents control over critical cloud infrastructure?
How should organizations prepare their architecture and teams to transition toward agentic cloud models over the next five years?
What impact would Agents make on SRE’s and DevOPs?
Can Copilot help with exploratory testing?
