Optimizing application architectures for AI: From monoliths to intelligent agents (2 of 2 blogs series)

Optimizing application architectures for AI: From monoliths to intelligent agents (2 of 2 blogs series) Why does current customer support fall short? The new approach: a workflow of specialized agents How do we architect for AI agents? 1. Input moderation 2. Information extraction 3. Context enrichment 4. Classification and routing 5. Action execution with MCP 6. Natural language response Why not just one big model? Why governance matters A real example: from request to answer What this means for enterprise architectures Get started with AI Inference About the authors Luis I. Cortés Bernard Tison More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share No one enjoys menu-based customer service. Pressing “1 for billing” or “2 for support” feels outdated, and repeating the same problem to different agents is frustrating. Even when we try the “operator” trick, we’re just hoping for someone to pick up and understand us right away. But what we really want is simple: a fast and accurate resolution, without friction. If that’s how you feel, you are not alone.