Jira is no longer just a place to track work. With Agentic AI, it is evolving into a platform where intelligent agents can coordinate tasks, automate decisions, and help teams move faster. But deploying AI agents in production requires more than powerful models. It requires trust.

Join us for a practical session that explores both sides of the equation: how Agentic AI is transforming Jira workflows, and what it takes to build AI agents that organizations can confidently rely on. Through real-world examples and production insights, you'll learn how AI agents can automate meaningful work while remaining transparent, reliable, and accountable.

What You'll Learn

• How agentic AI is changing core Jira workflows, from ticket triage to reporting to team rituals.

• Real-world use cases where AI agents can coordinate work across engineering and IT teams

• Why trust, traceability, and explainability are essential for production-ready AI agents
• Designing effective human-in-the-loop checkpoints for reliable automation
• When to use deterministic workflows instead of autonomous agents
• Best practices and production lessons for deploying trusted AI agents in Jira
• A live demonstration of Agentic AI powering real Jira workflows

Perfect For

• Engineering Managers and IT Leaders exploring Agentic AI adoption

• Jira Administrators looking to build intelligent, trustworthy workflows
• Product, Operations, and Support teams interested in AI-driven automation
• AI practitioners and technology leaders building enterprise AI systems
• Anyone looking to understand how to deploy AI agents safely and effectively in production

Speakers:

Rajesh Shanmugam: CEO, Enjo AI Pioneer in AI-powered productivity solutions.Led game-changing Slack integrations for Fortune 500 companies. Discover his blueprint for 10x support team efficiency.

Navaneeth Malingan: Founder & CEO, Nunnari Labs. Navaneeth focuses on Enterprise AI, AI Research, AI Governance, and Responsible AI. He will share practical frameworks for building reliable AI agents with human-in-the-loop design, traceability, and trust, drawing on production patterns used in enterprise AI systems.