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When enterprises evaluate a distributed SQL database for production workloads, security isn’t a checkbox. It’s a prerequisite. Teams running financial transactions, customer data, and AI agent infrastructure need to know that the database they depend on holds up under independent scrutiny, not just internal testing. That’s why TiDB engaged NCC Group, a global cybersecurity consultancy, […]

If you have ever run Amazon Aurora at scale, you know the moment I mean. Write latency on the primary starts creeping up during peak hours. You bump the writer to the next instance class, and the relief buys you a quarter. You bump it again. Then one day you open the AWS console to […]

A few months ago I watched a fintech engineering team walk me through their AI agent platform. They had a transactional database for balances and ledgers, a separate vector database for semantic retrieval, a cache for session state, a document store for agent memory, and a stream processor stitching it together. Five systems. Each one […]

Key Takeaways AI coding agents are no longer a novelty. From Claude Code to Cursor’s agent mode, from GitHub Copilot Workspace to OpenAI Codex, “generate an app from a prompt” demos flood developer feeds. Each one follows the same arc: a developer types a prompt, an application appears, and the audience applauds. What those demos […]

Agentic AI did not create a new kind of database. It revealed which ones were already built for it. Across 2026 so far, a wave of large software companies have cut tens of thousands of roles and explained the cuts in nearly identical language: Too many layers of leadership, too many coordination-heavy roles, a need […]

I gave a session at Microsoft Build 2026 on agent memory with TiDB. A few people asked for the code afterward, so here’s a complete write up of the session: The same pattern as the talk, with copy-paste-ready schema and queries. You can watch the original Microsoft Build 2026 session here. Why an Agent Memory […]

Key Takeaways There is a moment using an AI agent that ruins you a little, because once you have felt it, you cannot go back. The agent remembers something from three weeks ago that you had half-forgotten you mentioned. Not in a strange way, but in a useful way. It picks up where you actually […]

Editor’s note: This post originally appeared on The New Stack and is republished with permission. The original version is available here. A few months ago, one of our users filed a bug report that stuck with me. They had built a customer-support agent on top of a RAG pipeline. A user asked whether they could return […]

Most teams shipping AI agents in 2026 hit the same wall around the same time. The prototype works. Ten users or even a thousand users mostly work. But then one user action triggers thousands of agent instances, context has to branch per agent in milliseconds, and vector lookups stack on top of transactional reads. The […]

Agentic AI changes the database problem. A single user action can trigger many agent steps. However, each agent needs state, memory, transactions, analytics, and retrieval to stay consistent under real production load. That pressure exposes the weak points in stacks built from separate OLTP databases, vector stores, warehouses, and sync pipelines. TiDB SCaiLE Europe 2026 […]

The stories sound the same in every engineering review. A team ships a working AI prototype in a week. The demo is impressive. Leadership greenlights production. Six months later, the app is still not in production. The failure rarely traces back to the model. The team did not pick the wrong LLM. They did not […]

Cloud-native databases have made it easier to ship transactional applications. Managed services, automatic failover, and elastic storage are now table stakes. The harder problems show up later: When a multi-tenant SaaS app starts hitting tail-latency spikes during compaction, when a serverless tier saturates a noisy neighbor, or when a dedicated cluster sits at 30% utilization […]