5 Lessons Running AI Agents in Production
Show notes
John Sexton and Aaron Tummon from Genesys join the show to share hard-won lessons from building and operating Cloud Copilot — an agentic AI layer serving 2 million users across 21+ AWS regions. Genesys powers customer experience for brands like Virgin Atlantic, Vodafone, and HSBC, and their copilot helps admins, supervisors, and agents work more efficiently through natural language. We cover the migration from Bedrock Inline Agents to Strands Agents, multi-agent orchestration with agents-as-tools, context management strategies, cost optimization, and the testing discipline required to keep agentic systems stable at scale. The 5 lessons: 1. Pick a framework that scales with you — Bedrock Inline Agents worked for 12–15 tools but became exponentially flakier beyond that. Strands Agents gave sensible defaults and room to grow without pinch points. 2. Separate orchestration from domain logic — Agents-as-tools creates a clean line between the orchestrator and sub-agents. You can pull functionality in and out per persona without destabilizing the system, and domain teams own their sub-agents independently. 3. Manage context aggressively — Long context windows for the orchestrator, stateless sub-agents, summarizing and sliding-window conversation managers, and strict control over what tools return. Every extra token in context degrades quality and increases cost. 4. Make prompt caching non-negotiable — System prompts, tool definitions, and conversation history rarely change between invocations. Enabling prompt caching delivered significant cost reductions with almost no effort. 5. Test relentlessly because prompt drift is invisible — One prompt change is never a breaking change; five accumulated changes are. A dedicated weekly Sentinel role investigates failures, and full test suites run on every single change.With Aaron Tummon, Software Engineer at Genesys ; With John Sexton, Engineering Manager at Genesys