Building Real-Time Applications with AI Agents and Convex

Your users refresh the page to see new data. That's the tell. That's how you know your architecture is stuck in 2015.

Real-time is not a feature anymore. It's an expectation. When someone sends a message, the recipient should see it appear. When a database record changes, every connected client should reflect that change. When a teammate edits a document, you should see their cursor moving.

And yet, most developers still build request-response applications, then bolt on WebSocket connections as an afterthought. The result is a mess of state synchronization bugs, stale data, and race conditions that surface at the worst possible times.

Why Convex Changes the Game

I've built real-time features on Firebase, Supabase realtime, raw WebSockets, Socket.io, and Convex. Convex is the first backend where reactivity is not an add-on. It's the foundation.

When you write a Convex query, every client subscribed to that query automatically receives updates when the underlying data changes. You don't manage WebSocket connections. You don't write subscription logic. You don't handle reconnection or missed events. The backend does all of it.

This is not a small thing. The amount of code you don't write is the code that can't have bugs.

AI agents understand this reactive model deeply. They design your Convex schema knowing that queries will be subscribed to by multiple clients simultaneously. They structure your data so that a change to one record triggers minimal re-computation across subscribers. They think about reactivity at the schema level, not as a layer you add later.

The Hard Parts of Real-Time

Real-time sounds simple. Data changes, clients update. In practice, it's full of sharp edges.

Optimistic updates are the first challenge. When a user clicks "like," you want the UI to update instantly, before the server confirms the mutation. But what happens when the server rejects the mutation? You need to roll back the optimistic state, and you need to do it without causing a jarring visual flash.

AI agents implement optimistic updates with proper rollback patterns. They understand that the optimistic state and the server state need to merge cleanly, and they handle the edge cases where two users mutate the same data simultaneously.

Presence is the second challenge. Live cursors, typing indicators, "who's online" lists. These features require a different data model than your application data. Presence data is ephemeral, high-frequency, and tolerance for staleness is near zero. Storing it in the same database as your application data is a performance mistake.

Conflict resolution is where it gets truly interesting. Two users edit the same paragraph at the same time. What happens? If you're building a collaborative editor, you need either operational transforms (OTs) or conflict-free replicated data types (CRDTs). Both are well-studied algorithms, but implementing them correctly requires understanding their trade-offs.

AI agents select the right approach based on your requirements. CRDTs for applications that need offline editing and eventual consistency. OTs for applications that can assume connectivity and want precise ordering. Simple last-write-wins for data where conflicts are rare and resolution doesn't need to be sophisticated.

Performance in a Reactive World

Here's the trap with real-time systems: everything is live, so everything is expensive.

Every subscription consumes server resources. Every data mutation triggers computation for every subscriber. If you're not careful, a single frequently-updated record can cause thousands of unnecessary re-renders across your connected clients.

The solution is surgical subscriptions. Subscribe to exactly the data you need, nothing more. Don't subscribe to an entire collection when you only care about one document. Don't include fields in your subscription that you don't render.

AI agents are ruthless about subscription optimization. They analyze your component tree, determine exactly which data each component needs, and write queries that return precisely that data. They also identify opportunities for denormalization that reduce the number of subscriptions a client needs to maintain.

Batching is another optimization that AI agents handle well. Instead of triggering a re-render for every individual mutation, batch related mutations into a single transaction. The client sees one update instead of ten, and the server computes one diff instead of ten.

What I Build With This Stack

The Convex plus AI agent combination is what I use for every project that needs real-time features. Live dashboards where metrics update as events arrive. Collaborative tools where teams work on shared artifacts. Chat systems where messages appear instantly with read receipts and typing indicators.

The time savings are dramatic. What used to be a two-week sprint of WebSocket plumbing, reconnection logic, and state synchronization is now a few hours of schema design and query writing. The AI agent handles the reactive patterns. You focus on the product.

One thing I want to be clear about: real-time is not just for chat apps and Google Docs clones. Every SaaS application benefits from reactivity. Notification counts that update without polling. Admin dashboards that reflect the latest data without a manual refresh. E-commerce inventory that shows accurate stock levels across all sessions. These features used to require dedicated real-time infrastructure. Now they're just how the backend works.

Real-time is not optional anymore. And with the right tools, it's not hard either.