AI continues to reshape software engineering, and one question is becoming increasingly important: Can we trust AI agents to do more than just assist?
I am excited to share that Stephen Ellis, Cloudera's VP of Engineering will be joining industry leaders at Harness Unscripted 2026 for the panel discussion:
Time vs. Trust: Do You Trust Your AI Agents?
Over the past year, AI has evolved from helping developers write code to taking on increasingly autonomous responsibilities across the software lifecycle. These workloads range from reviewing pull requests and investigating production issues to automating deployments and security workflows. AI agents are therefore beginning to move from suggestions to actions.
As these systems become more capable, the conversation naturally shifts beyond productivity. The real question is no longer how quickly AI can help teams move, but how confidently organizations can allow AI to operate within production environments.
At Cloudera, we see this challenge from two perspectives.
Internally, our engineering teams are continually exploring ways to improve developer productivity by adopting modern engineering practices and tools across the software development lifecycle. As AI becomes an integral part of software engineering - from development and testing to CI/CD and operational workflows -we are learning firsthand that delivering software faster is only part of the equation. What is equally important is establishing the trust and governance for engineering teams to confidently rely on AI in production.
At the same time, we are helping organizations build and deploy AI applications and agents on enterprise data. As enterprises move beyond experimentation, they are increasingly looking for AI that can run where their data lives - whether in private cloud, public cloud, on premises, and sovereign environments - while maintaining governance, security, and compliance. Trust isn't just about choosing the right model; it's about ensuring AI agents have the right context, secure access to governed enterprise data, and the guardrails to operate confidently in production.
While these challenges exist at different layers of the technology stack, they're driven by the same underlying question: what does it take to trust AI in production?
We are looking forward to joining fellow engineering leaders to discuss how organizations are approaching this challenge, what trust frameworks are emerging, and what it will take for AI agents to become reliable participants in modern software delivery and enterprise systems.
If you will be attending Harness Unscripted in San Francisco, we would love for you to join the discussion. We look forward to seeing you there!
