Why Real-Time Matters for AI Agents: Building Responsive Agent UX with Convex
AI agents take 5-30 seconds to respond. Users hate waiting. Real-time state sync with Convex transforms that experience from frustrating to engaging.
December 28, 2025 7 min read
Your AI agent takes 15 seconds to respond. During that time, your user sees a loading spinner. They wonder if it crashed. They refresh the page. Your agent finishes processing to an empty room.
This happens constantly with AI agents. LLMs are slow. Multi-step agents are slower. Users don't wait.
Real-time state synchronization fixes this. Show users exactly what the agent is doing. Display progress as it happens. Stream results as they're generated. Turn waiting into engagement.
Convex makes this trivial. Real-time database sync built in. No polling. No websocket management. Just reactive state that updates instantly.
Here's why real-time matters for AI agents, and how to build it.
The AI Agent UX Problem
Traditional apps respond in milliseconds. AI agents respond in seconds or minutes.
Typical agent timeline:
0s: User submits request
2s: Agent starts thinking
5s: Agent calls first tool
10s: Agent analyzes results
15s: Agent calls second tool
20s: Agent generates response
20s: User sees result
That's 20 seconds of staring at a spinner. Awful experience.
Stop planning and start building. We turn your idea into a production-ready product in 6-8 weeks.
"Analyzing your request..." (2s)
"Searching documentation..." (5s)
"Found 12 relevant sources" (7s)
"Analyzing sources..." (10s)
"Searching for additional context..." (15s)
"Generating response..." (18s)
Final response appears (20s)
Same 20 seconds. Completely different experience. One feels broken. One feels responsive.
Why Traditional Polling Fails for Agents
The naive approach: poll the backend for updates.
Problems with polling:
Wastes API calls checking status
1-second delay between updates (users notice)
Backend load scales with active users
Doesn't work well on mobile (battery drain)
Polling continues if user navigates away
Race conditions with rapid state changes
Polling was designed for occasional status checks. AI agents have continuous state changes. Updates happen every 1-3 seconds as agents progress through steps.
Polling creates lag. Real-time sync eliminates it.
What Convex Provides for Real-Time Agents
Convex is a real-time database with TypeScript functions. Write a query. It reactively updates when data changes. No websockets to manage. No polling logic.
Convex's real-time primitives:
Reactive queries that auto-update
Optimistic updates for instant feedback
Ordered writes with conflict resolution
Built-in presence and typing indicators
Subscription management handled automatically
For AI agents, this means your frontend instantly reflects backend state changes. Agent starts a new step? UI updates immediately. Tool call completes? Result appears instantly.
Basic Convex agent state sync:
Frontend uses it:
That's it. No polling. No websockets. State updates automatically when the backend writes to Convex.
Agent State Modeling in Convex
Agents have complex state. You need to model it properly for real-time updates.
What to track:
Overall agent status (idle, running, complete, failed)
Current step in the workflow
Tool calls made and results
Intermediate artifacts generated
Error messages if failures occur
Timestamps for debugging
Convex schema for agent sessions:
This schema separates session state, steps, and messages. Allows granular updates without overwriting everything.
Streaming Agent Progress Updates
As your agent executes, write state to Convex. Frontend reactively updates.
Backend agent execution:
Frontend watches and displays:
Every mutation to Convex triggers reactive query updates. UI stays in sync with agent state automatically.
Handling Streaming LLM Responses
LLMs stream tokens. Users want to see text appear as it's generated, not wait for completion.
Convex handles this with incremental updates.
Stream tokens to Convex:
Frontend displays streaming text:
Text appears character by character as the LLM generates it. No waiting for completion. Feels responsive and alive.
Multi-User Agent Sessions
Some agents are collaborative. Multiple users watching the same agent run. Convex makes this trivial.
Example: Team research agent
Team leader starts an agent. Team members watch progress in real-time.
All users see the same state updates simultaneously. No coordination needed. Convex handles sync.
Optimistic Updates for Perceived Performance
Real-time sync is fast. Optimistic updates make it feel instant.
Pattern: User sends message to agent
Without optimistic updates:
User clicks send
Request goes to server
Server writes to Convex
Convex notifies client
Message appears in UI
With optimistic updates:
User clicks send
Message appears in UI immediately (optimistic)
Request goes to server
Server confirms write
UI updates with confirmed data (or rolls back on error)
Implementation with Convex:
Convex's mutation system handles optimistic state automatically. You write the optimistic logic. Convex reconciles it with server state.
Error Handling and Recovery Patterns
Agents fail. Real-time systems need to surface errors immediately.
Convex mutation for error state:
Frontend displays errors immediately:
Users see errors the instant they happen. No waiting for timeout or refresh. Immediate feedback enables immediate recovery.
Performance Characteristics at Scale
Real-time systems need to scale. Convex handles this well, but you need to design for it.
Convex scaling properties:
Queries are automatically cached
Only changed data sent to clients
Subscriptions managed efficiently
Automatic connection management
These infrastructure decisions impact your overall AI development costs significantly.
Performance patterns for agents:
Don't write to Convex on every token (batch updates)
Use separate tables for high-frequency vs low-frequency updates
Index by sessionId for fast lookups
Paginate step history for long-running agents
Batched token streaming:
This reduces write volume without sacrificing perceived real-time performance. 100ms batching is imperceptible to users.
Alternative: Vercel AI SDK's useChat
Vercel AI SDK provides useChat hook for streaming. Works well for simple chatbots.
When useChat is enough:
Single-turn conversations
No complex agent state
Streaming text is the only real-time need
Don't need multi-user sync
When you need Convex:
Multi-step agent workflows
Want to display intermediate steps
Need persistent chat history in database
Multiple users watching same agent
Complex state beyond just messages
useChat streams responses from the server to a single client. Convex syncs state across all connected clients and persists it durably.
Hybrid approach:
Use useChat for message streaming, Convex for agent state.
Best of both worlds. useChat handles streaming UX. Convex handles everything else.
Real-World UX Patterns
Here's what actually works in production agent UIs.
Pattern 1: Progress with estimated time
Pattern 2: Step-by-step disclosure
Pattern 3: Typing indicators for thinking
These patterns transform waiting into engagement. Users see progress. They understand what's happening. They trust the agent is working.
When Real-Time Isn't Worth It
Real-time adds complexity. Not every agent needs it.
Skip real-time sync when:
Agent completes in under 3 seconds
Users don't care about intermediate steps
Simple request/response flow
No collaborative features needed
Add real-time sync when:
Agent takes longer than 5 seconds
Multi-step workflows users want to monitor
Multiple users need to see same state
Intermediate results are valuable
Errors need immediate surfacing
For quick agents, a loading spinner is fine. For slow agents, real-time feedback is critical. These UX considerations are often missed in early AI implementation planning.
Implementation Checklist
Ready to add real-time state to your AI agents? Here's the path.
Setup:
Install Convex: npm install convex
Initialize Convex: npx convex dev
Define schema for agent sessions and steps
Create mutations for state updates
Create queries for state reads
Agent integration:
Wrap agent execution with Convex client
Write state updates at each step
Use typed mutations for safety
Handle errors with dedicated mutations
Frontend integration:
Import Convex React hooks
Subscribe to agent state with useQuery
Display progress and steps
Handle loading, running, complete, failed states
Add optimistic updates for user actions
Testing:
Test state sync with multiple browser windows
Verify updates appear within 100ms
Simulate errors and check error display
Test with slow network (throttle in DevTools)
Most implementations take 1-2 days. The UX improvement is worth it for any agent longer than 5 seconds. Factor this into your MVP development timeline.
Ready to Build Responsive AI Agents?
Real-time state sync transforms AI agent UX from frustrating to engaging. Convex makes it simple.
NextBuild helps startups build AI features with great UX, not just functional backends. We handle the real-time infrastructure, agent orchestration, and polish that makes users trust your AI.
We'll help you build agents that feel fast, even when they're slow.
Chatbots are stateless. Agents accumulate state, make decisions, and run for minutes. Here are the 7 backend requirements that make or break production agents.
