Platform Engineering
Viewing all posts categorized under Platform Engineering.
Small Language Models (SLMs) vs LLMs: When to Use What in 2026
Small Language Models are changing how enterprises design and deploy AI applications in 2026. While Large Language Models offer broad capabilities and advanced reasoning, SLMs can deliver faster, more cost-effective inference for focused tasks. This guide compares SLMs and LLMs across architecture, performance, infrastructure, security, and total cost of ownership. Learn when to use local models, when to choose a hosted LLM, and how to build hybrid AI systems that route each task to the most appropriate model.
The Agent Factory: How to Build 100+ Specialized AI Agents at Scale
Building one AI agent is an engineering project. Building and operating 100 specialized agents is a platform engineering challenge. This guide explains how to design an Agent Factory that standardizes agent creation, dynamically routes requests, reuses tools and capabilities, enforces security policies, automates testing, and manages the full agent lifecycle. Learn how enterprise engineering teams can scale from isolated AI pilots to a governed ecosystem of specialized agents without creating 100 independent systems to maintain.
Legacy .NET to Agent-Native: A Zero-Rewrite Migration Playbook
Learn how to modernize legacy .NET applications for agentic AI without rewriting business-critical systems. This enterprise migration playbook explains facade adapters, ASP.NET Core reverse proxies, OpenAPI contracts, Semantic Kernel integration, agent orchestration, security boundaries, deployment strategies, and a phased roadmap for introducing AI capabilities while preserving existing business logic.
MCP Protocol Explained: Building the Agent Internet with Model Context Protocol
As enterprises move from isolated AI assistants to agentic systems, interoperability is becoming a critical challenge. Model Context Protocol (MCP) provides a standardized approach for connecting AI models with tools, data sources, enterprise applications, and other agents. This guide explains MCP architecture, enterprise adoption patterns, security considerations, and its role in building the emerging Agent Internet in 2026.
FinOps for AI: How We Cut LLM Costs by 70% Using Hybrid Routing
As enterprise AI adoption accelerates, inference spending is becoming a significant operational expense. This guide explains how a hybrid routing architecture can reduce LLM costs by up to 70% by matching workloads to the appropriate model, implementing governance controls, and applying FinOps principles to AI platforms.