Why Agentic AI Architecture Needs a Database, Not Just a Vector Store

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 […]

Why Attend TiDB SCaiLE 2026: Same Complexity, Different Clock Speeds

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 […]

Open Source Data Layers Will Win the AI Future

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 […]

Why Agent Memory Needs a Database That Can Write Back

I’ve spent the last year helping enterprise teams put AI agents into real workflows: Fraud detection, infrastructure monitoring, customer intelligence. The conversations follow a predictable pattern. The first question is always about the model. Which LLM? Which orchestrator? The second question, about six weeks after go-live, is always about the data. Why is the agent […]

ACID at Scale: Where ORMs and MySQL Disagree

If you have ever watched an ORM-heavy application meet a sharded MySQL backend, you know the moment I mean. The code looks right. The tests pass. Then production traffic arrives, and you start seeing the impossible: An order without its line items, a user whose profile says one thing and whose billing record says another, […]

From One Week to 22 Minutes: How Bolt Migrated MySQL to TiDB

When a table crosses one terabyte, MySQL does not fail. It just starts charging for everything. A single index change takes over a week, blocks every other change queued behind it on the cluster, and demands a full shadow copy of the table just to run. At Bolt, whose ride-hailing, micromobility, delivery, and car rental […]

TiDB Completes Independent Security Assessment by NCC Group

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, […]

When Amazon Aurora Stops Scaling: The Ceiling You Can’t Buy Your Way Past

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 […]

The Fintech AI Platform: Why Agents Need One Database, Not Five

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 […]

Database Branching for AI Agents: How TINE Solves the Schema Drift Problem

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 […]

Conway’s Law in Reverse: Why AI Agents Need One Database, Not Ten

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 […]

TiDB SCaiLE Europe 2026: Why Engineers Building Agentic AI Should Be in Stockholm on 4 June

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 […]
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