Architect

← Series hub Next → For the past two years, enterprise AI adoption has been dominated by a singular architectural pattern: API integration with massive, closed-source models (Frontier LLMs). While this API-Centric model allows for rapid prototyping, it becomes a severe liability when scaled to production workloads handling sensitive company data. The Problem with API-Centric Architectures Relying exclusively on commercial APIs (such as GPT-4 or Claude 3.5 Sonnet) introduces three critical bottlenecks for scale-ups and enterprises: ...

While using an AI to write code or answer support tickets is becoming commonplace, the true transformation in enterprise software lies in Agentic Systems. We are moving away from monolithic, single-prompt architectures toward distributed networks of AI Agents that can plan, coordinate, and execute complex workflows autonomously. The Limitation of the “Single Agent” Paradigm Many organizations begin their AI journey by building a “monolithic agent”—stuffing an entire knowledge base and every possible tool into a single LLM’s context window. As the system scales, this approach inevitably collapses: ...