AI Agents Need More Than Good Data | Governance, Guardrails & Data Engineering | Phani Kaligotla

If you work in Data Engineering, AI, Data Strategy, Analytics, Product, or Technology Leadership, this conversation offers a practical look at what changes as AI agents become part of enterprise systems. Subscribe to Off The Backlog for conversations with practitioners building and operating real-world Data and AI systems. Strong AI and data strategy requires defined policies to ensure systems work in parallel effectively. Avoid common design traps by managing your agents properly. Building robust systems requires balancing both spoken and unspoken constraints. When you design for parallel execution, you must decide how to split processes so that every agent operates in harmony. Without clear policy definitions, your system will struggle to coordinate actions across different parts of the infrastructure. This breakdown covers how to structure your agentic architecture to handle simultaneous tasks without conflicts. By focusing on these design patterns, you can build reliable systems that scale across your entire data strategy. Proper policy management is what separates a fragile setup from a resilient production environment. Subscribe for weekly enterprise architecture breakdowns, and comment on which system design challenge you are currently facing. #DataEngineering #AgenticAI #GenerativeAI #DataStrategy #AIGovernance #ArtificialIntelligence #DataArchitecture #DataLeadership #OffTheBacklog