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In this episode of Conversations With Dez, tech commentator and media publisher Dez Blanchfield sits down with Sam Harley, Principal Solutions Architect for ANZ at Elastic, to discuss the realities of moving into the "agentic era" of artificial intelligence. As organizations transition from AI that merely describes to AI that actively acts, the infrastructure and data engineering requirements under the hood have fundamentally shifted. Sam breaks down the spectrum of autonomy, moving past the marketing hype to outline what AI agents truly need to operate safely inside live production environments. The conversation dives deep into the unglamorous engineering of retrieval layers, the balance between native architectures and open standards like the Model Context Protocol (MCP), and how to manage the real-world operational hurdles of LLMOps. They also look ahead to Elastic's exciting presence as Gold Sponsors at the 2026 BeerOps and ExecOps events in Sydney and Melbourne, highlighting why the best AI architecture insights come from swapping war stories with the people on call at 2:00 AM. Key Takeaways from This Episode: • The Agentic Shift: We are moving from humans executing via prompts to AI models that can reason over goals, invoke tools, and observe results, moving the human up to a supervisory role. • The Production Triad: To make safe, autonomous decisions, an AI agent needs context that is relevant, fresh, and trustworthy—otherwise, it just executes bad actions faster and burns through token budgets. • Native vs. Interoperable: Collapsing the seam between agents and data natively within Elastic reduces latency, cost, and security gaps, while open standards like Model Context Protocol (MCP) should be used to govern the outer boundaries. • The Rule of Autonomy: Let the model decide what data to look at, correlate, and summarize, but keep all consequential actions (like pushing firewall rules or restarting services) strictly deterministic, permissioned, and auditable. • No "Big Bang" Rip-and-Replace: The most successful enterprise migration stories start with a single, well-bounded, high-pain workflow where data already lives, proving value before expanding iteratively. • The Reality of LLMOps: Operating AI in production requires watching four critical pillars: token cost tracking, multi-step execution latency, catching "confidently wrong" hallucinations, and establishing end-to-end trace visibility. • Measuring Value Over Volume: True success isn't cost-per-query; it’s measured through business outcomes like human approval/override rates, cost-per-resolved-incident, and Mean Time to Resolution (MTTR) deltas against pre-agent baselines. Resources & Links Mentioned in This Episode: • Elastic Website: https://www.elastic.co • Sam Harley on LinkedIn: Connect with Sam: https://www.linkedin.com/in/samharley/ • Elastic on LinkedIn: https://www.linkedin.com/company/elastic-co • BeerOps & ExecOps Events - Please Register Now: https://execsandtechs.com/ About the Guest: Sam Harley is the Principal Solutions Architect for Australia and New Zealand at Elastic. He works directly with security, observability, and search engineering teams at strategic enterprise accounts to help them extract maximum scale and value from their data estates, while implementing secure, native frameworks to deploy production-ready AI capabilities safely.

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