RFC 10008 defines a new HTTP method called QUERY, which sends query parameters in the request body instead of the URI while remaining safe and idempotent. It enables automatic retries, caching, and clearer intent compared to POST-based queries. The document specifies headers, media type requirements, status codes, and how servers assign URIs to queries and their results.

• QUERY sends query params in the request body instead of the URL, so it's not limited by URI length like GET is
• Unlike POST, QUERY is explicitly defined as safe and idempotent, so retries, caching, and load balancers can treat it predictably
• Servers can respond with a Content-Location/Location header or a 303 redirect to give a stable, GETable URI for a query or its results
• A new Accept-Query header lets clients discover what query formats/media types a server supports

This article breaks down how to design a web system for scale, starting with scope, use cases, and constraints. It then walks through high-level and component design, scaling strategies (caching, load balancing, replication), performance tuning, reliability, and front-end considerations.

• Start every system design by pinning down use cases and traffic patterns (RPS, read/write ratio) before drawing architecture
• When the database becomes the bottleneck, add Memcached/Redis caching before jumping to vertical or horizontal scaling
• Choose replication vs. partitioning based on whether you need more read capacity or more write throughput
• Isolate shared concerns (auth, caching, DB access) in a platform layer so web, mobile, and API servers can scale independently

The author recounts the pitfalls of building a custom context layer—handling memory, data retrieval, caching and permissions—only to face endless complexity. They found that Redis Iris bundles syncing, search, semantic caching and memory into a single context engine that sits close to the agent runtime.

• Building a "simple" memory feature for an agent inevitably explodes into a full context engine covering memory categories, expiry, permissions, and privacy.
• Connecting to real data sources creates a cascade of problems: schema drift, stale data, expensive queries, and cross-system joins across silos like databases, PDFs, emails, and tickets.
• Getting context volume wrong breaks the agent in both directions—too much data raises cost and confuses the model, too little causes hallucination, and improper access risks security breaches.
• Redis Iris packages data sync, retrieval, short/long-term memory, search, and semantic caching (LangCache) into one runtime-adjacent platform, replacing custom-built glue code across multiple services.