Thought Leadership

Editor’s note: This post originally appeared on The New Stack and is republished with permission. The original version is available here. Imagine you just finished a two-hour meeting. You were wearing a small AI work companion that promised to capture the conversation, structure it, and let you ask questions about it later. Two hours after the […]

When Plaid’s Amazon Aurora MySQL fleet faced a major-version upgrade, the estimate came back at six engineering months and tens of minutes of downtime. For a company whose APIs sit underneath thousands of financial apps, that is not a maintenance task. It is a product freeze. Plaid spent that effort moving to distributed SQL instead, […]

You have an AI app running on Vercel, or a prototype that v0.dev generated in a few minutes, and now it needs a real database. The choice is harder than it looks, because serverless functions and edge runtimes break the assumptions traditional databases are built on: long-lived TCP connections, a bounded pool, and a process […]

An AI coding agent can generate an application, change its schema, test several implementations, and discard most of them within a single session. Another agent might spend that session updating orders and querying months of operational history. Both need durable state, correct transactions, and fast access. They also need something query throughput doesn’t measure: databases […]

Key Takeaways An agent that forgets everything between calls can’t finish a multi-step task, can’t recover from a mid-workflow crash, and can’t show you what it actually did. That’s the practical cost of statelessness, and it’s the problem AI agent state exists to solve. AI agent state is the durable record of memory, task progress, […]

This article shows how to efficiently develop high-quality applications with TiDB, an open-source NewSQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads and can serve as a scale-out MySQL database without manual sharding.

A serverless database is a cloud database that decouples compute from storage, scales capacity automatically as demand changes, and bills for what a workload actually consumes. Servers still exist. The difference is that you never size them, patch them, or pay for idle ones, and capacity planning stops being a launch-day decision. That difference matters […]

On July 24, Jensen Huang made the first post of his life on X. It wasn’t a product launch or a victory lap. It was a policy letter signed by 25 companies, doubling to 50 within a day, asking Washington not to restrict open weight AI models. Microsoft, Meta, IBM, a16z, and Hugging Face. The […]

As a database veteran, I found traditional monolithic databases (such as Oracle) to have bottlenecks for mission critical applications in the cloud, which called for a next generation cloud native database to solve these challenges. This is what excited me about joining the PingCAP team, where we took the core concept of Distributed SQL’s multi-node auto-sharding architecture and extended it to allow for greater flexibility and functionality beyond Spanner and other Distributed SQL databases. It is also open source and has helped to change the landscape for developers.

Agentic AI architecture is the system design that lets an AI agent perceive context, reason over it, call tools, maintain memory, and take actions across multiple steps. It spans the model, the orchestration layer, and the data infrastructure that makes an agent’s work durable rather than disposable. Most teams get the model and framework right […]

A single user action in an agentic application no longer maps to a single database query. It spawns agent instances that branch context in milliseconds, hold memory across sessions, and provision their own tenants without waiting for a human. At Manus, agents create more than 90% of new database clusters. The stacks underneath them were […]

For most of the cloud era, infrastructure decisions followed a familiar pattern: choose the fastest path to production, accept some proprietary dependencies, and optimize later. AI breaks that pattern. Coding agents now reshape applications in hours, while models, frameworks, and workload shapes change underneath them. That leaves the data layer as the one place a […]