How Manus 1.5 Uses TiDB X to Let Agents Ship Full-Stack Apps at Scale

Software is shifting from developer-driven to agent-driven. With Manus 1.5, AI agents don’t just “help you code”; they spin up full-stack applications end-to-end. Ideas move from prompt to deployed app in minutes. It’s a glimpse of what’s next: rapid iteration, parallel exploration, and continuous change without human bottlenecks.

That speed creates a new reality for infrastructure. You’re no longer operating one app and one database. You’re now managing thousands of short-lived apps, each with its own schema, unpredictable usage spikes, and evolving data needs. Traditional monoliths, weekend maintenance windows, and ETL-heavy analytics don’t survive this pace.

This post explains how to meet that moment with a data layer designed for agents. We’ll show how TiDB X, a breakthrough new architecture powering TiDB Cloud, provides instant isolation (databases in seconds), online schema evolution, safe branching for experiments, and real-time analytics on fresh data. This allows agent platforms to scale up, change fast, and stay within SLOs and budget.

Manus 1.5: Agents that Build & Evolve Full-Stack Apps

Manus began as a digital assistant. However, with Manus 1.5, it becomes a full-stack development platform. Its agents can generate a complete application—front end, backend, authentication, and database—from a single prompt.

Example: One prompt can produce a web app with:

• User signup/login flows
• A custom domain
• Built-in analytics dashboards
• A cloud database for persistent data

Behind the scenes, an agent might spin up a TiDB Cloud cluster for the app’s database, create tables (users, content, logs), seed initial data, and connect analytics. It can then iterate autonomously: add features, adjust the schema, or branch the app to test variations, no manual intervention.

This kind of AI DevOps speed is powerful. It also demands a database platform that can keep pace with rapid automation, frequent schema changes, and large numbers of short-lived apps.

The Scaling Challenge: Thousands of Apps and Unpredictable Workloads

Manus 1.5 runs on a multi-layer model: tenants → apps → clusters/branches. A single Manus user (tenant) might have several agents; each agent runs its own app. An agent can create its own database cluster and branch it for safe experiments. Multiply that by thousands of tenants, and you get an explosion of isolated databases. This is far beyond what traditional setups handle efficiently.

What the workload really looks like:

• Bursty creation/setup: An agent spins up a database, fires a burst of queries to build schema and seed data, then goes quiet.
• Unpredictable spikes: Traffic returns when real users arrive or the agent iterates; the cycle repeats.
• Mixed modalities: Short, intense OLTP bursts (user transactions + agent writes) alternate with heavier analytical and semantic queries (e.g., behavior analysis, vector searches over stored embeddings).

Manual provisioning and tuning won’t keep up. The data layer must adapt in real time, creating/retiring databases in seconds, handling OLTP and analytics together, and absorbing spikes without human intervention.

TiDB X: A Database Architecture Built for Agentic Workloads

TiDB X is TiDB Cloud’s breakthrough new distributed SQL engine. It’s built for the dynamic, spiky, multi-modal workloads AI agents generate. Manus 1.5’s ability to create tens of thousands of databases in days rides on TiDB’s serverless architecture under the hood. Below is how TiDB X meets the core challenges of an agent-based platform.

Elastic Scale to “Millions” of Instances

TiDB X decouples compute from storage and lets compute nodes attach to a shared object-store backbone.

• Spinning up a new database—even 10,000 of them—doesn’t require heavy provisioning; each “cluster” is a lightweight compute allocation tied to common storage.
• Databases can be created or retired on the fly as agents come and go, without straining capacity.
• For Manus, agents created tens of thousands of isolated TiDB Cloud clusters in parallel, each ready in seconds, while TiDB automatically distributed data and load.
• Traditional databases struggle with either massive multi-tenancy or launching thousands of separate instances; TiDB X’s cloud-native design handles both.
• In short, TiDB X is elastic by design—able to expand or contract in seconds to follow unpredictable agent spikes.

Schema Agility for Every App, Agent, and Branch

Each agent-generated app is unique: different schema, different data, and frequent schema changes as the app evolves. TiDB X is designed for a schema-per-agent model.

• Each agent gets an isolated database (or schema) so one agent’s complex DDL or heavy queries never impact others.
• Internal metadata storage supports large numbers of tables and schemas.
• Online DDL lets agents add tables, columns, and indexes without downtime.
• Branching creates an instantaneous, point-in-time copy of a database (copy-on-write), effectively branching data like Git.
  • Agents “fork” database state to test risky changes or alternate approaches without touching production.
  • Because storage is versioned and branchable, multiple variants of an app’s data can exist simultaneously.
• Branches are object-storage snapshots, not full copies, so thousands of agents can evolve their own schemas—and multiple branches—without degrading performance.

Hybrid OLTP + Analytics + Search in One Platform

AI agents mix workloads: transactional updates, analytical queries, and semantic search.

• TiDB Cloud provides an OLTP layer for fast transactions and an analytics path for heavy reads/aggregations in the same system.
• TiDB X extends this with a unified query engine that can incorporate vector similarity search and knowledge-graph queries alongside SQL.
• For Manus, this means agents don’t need separate databases for transactions, embeddings, and logs; everything can live in TiDB and be queried seamlessly.
• Running OLTP, analytics, vector, and AI queries together removes brittle ETL and cross-system hops, enabling real-time dashboards and AI reasoning on fresh data while protecting OLTP tail latency.

Cost Visibility and Self-Tuning via RUs

Serverless brings efficiency—and a new budgeting challenge—when agents spawn thousands of tiny, short-lived workloads.

• TiDB Cloud eliminates always-on provisioned compute; clusters scale elastically and idle when unused, reducing waste from over-provisioning.
• TiDB X’s Request Unit (RU) model makes every query’s cost visible per agent, so the platform can set limits and budgets.
• RU signals feed back to agents so they can rewrite queries or adjust plans, learning to stay efficient over time.

The result? You keep performance and reliability under control and maintain predictable spend, even as agent workloads surge and evolve.

Why TiDB X is Uniquely Suited for Agentic Workloads

Agentic platforms don’t scale like traditional apps. They fan out across tenants → apps → branches, churn schemas constantly, mix OLTP + analytics + semantic queries, and spike unpredictably in both traffic and cost. To keep up, the data layer has to create isolation in seconds, absorb change without downtime, serve fresh insights, and stay within budget all at once.

TiDB X is built for exactly that blend of speed, safety, and control. Here’s how the pieces fit together, with clean handoffs between them:

• Elasticity without toil: Start with raw speed and scale. You need to spin up and retire huge numbers of tiny databases and branches on demand, not after a ticket queue. Elastic, serverless scale ensures new databases are ready in seconds and safe to abandon when experiments end.
• Speed alone isn’t enough—you also need safe change: Once instances exist, agents must evolve them continuously. Per-agent isolation and copy-on-write branching let each agent adjust schema and data versions without impacting neighbors, test risky ideas, and merge or discard outcomes cleanly.
• Keep insight real-time without hurting OLTP: Isolation and change only work if the platform can answer questions on fresh data. A unified HTAP + AI engine runs transactions, analytics, and semantic/vector queries in one place, routing heavy reads off the hot path so tail latency for OLTP stays predictable.
• Close the loop with cost visibility: With thousands of short-lived workloads, unpredictability shifts from capacity to spend. RU-based metering and controls make every query’s cost visible per app/agent, cap noisy workloads before they blow budgets, and feed signals back so agents can learn cheaper plans over time.

TiDB X gives agents freedom to spin up and evolve full-stack apps aggressively while keeping the data layer scalable, observable, and economically sane.