AI Logs

What Is an MCP Gateway? Tool Access and Policy Checks An MCP gateway gives an application a common route to connected Model Context Protocol servers. It can centralize tool discovery, authentication and configured governance, making integrations easier to operate. To understand its security value, follow a real call from the…

Five Stages of LLM Guardrails: What Each One Checks An AI workflow can receive a question, retrieve a document, call a tool and return an answer. Each step exposes different information. Reviewing five guardrail stages—input, output, MCP, RAG and action—helps a team decide where a check belongs. The stages are…

LLM Conversation Summarization: Keep Useful Context A long support conversation often contains details the assistant still needs and exchanges it no longer needs to repeat in full. Conversation summarization creates a compact record of older turns while recent messages remain available. The aim is to preserve useful state for the…

Cascade Routing for LLMs: Balance Cost and Quality A mixed AI workload does not always need the same model for every request. Cascade routing starts with a suitable candidate model and escalates when the result needs another level of capability. The approach can improve efficiency, but its value depends on…

Provider Prompt Caching: APIs, Prefixes and Limits A support application may send the same instructions and tool descriptions with thousands of different questions. Provider prompt caching can reuse eligible processing for that shared prefix while the provider generates each new answer. It is useful to understand this as reuse of…