The Pragmatic Architect’s Guide to Enterprise AI: Balancing Cost, Memory, Context, and Production Reality
Enterprise Generative AI is transitioning from experimental prototypes to production-scale systems, where architectural design is now more critical than model capability. Success depends on dynamic model routing, efficient memory management, and controlled tool integration to handle real-world complexity and cost. Sustainable AI platforms require engineering discipline in context, latency, and distributed systems for probabilistic workloads.
- ▪Dynamic model routing reduces costs and latency by selecting the optimal model based on prompt complexity and constraints.
- ▪A split memory architecture separates short-term and long-term memory to improve context relevance and reduce token usage.
- ▪Exposing all tool schemas to the model leads to inefficiencies; progressive disclosure and AgentSkills help manage tool complexity.
- ▪Production AI systems must balance cost, memory, context, and latency to operate reliably at enterprise scale.
- ▪Microsoft Azure AI Foundry supports multi-model orchestration and intelligent routing for enterprise AI workloads.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1829954) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Seenivasa Ramadurai Posted on May 17 The Pragmatic Architect’s Guide to Enterprise AI: Balancing Cost, Memory, Context, and Production Reality Introduction Enterprise Generative AI has officially moved beyond the “cool demo” phase. Most organizations can now build a basic chatbot, connect a vector database, and generate answers from static documents.
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