Ten seconds with a blank screen feels like a minute. The same ten seconds with visible progress feels reasonable. Real-time streaming transforms perceived responsiveness by showing users what's happening as it happens. See streaming in action →

Users waiting for agent responses experience time differently than clocks measure it. Ten seconds with a blank screen feels like a minute. The same ten seconds with visible progress feels reasonable. Real-time streaming transforms the perception of agent responsiveness by showing users what is happening as it happens, converting dead time into engaged waiting.

The Perception Problem

Agent tasks take time. Even simple requests require an LLM call that takes one to five seconds. Complex tasks involving multiple tool calls, searches, and analysis can take thirty seconds or more. Long-running research or processing tasks might take several minutes.

During this time, what does the user see? In many implementations, the answer is nothing - a spinner, a loading indicator, or simply a frozen interface. The user has no information about whether the request is being processed, how far along it is, or whether something has gone wrong.

This absence of feedback creates anxiety. Users wonder if they should retry. They question whether the system is working. They lose trust in the agent's reliability. The actual performance might be excellent, but the perceived experience is poor.

Streaming changes this by providing continuous feedback throughout the agent's work. Users see the agent thinking, calling tools, receiving results, and generating responses. The same duration feels shorter because users understand what is happening. Transparency replaces uncertainty.

What Can Be Streamed

Different types of events can stream to users during agent execution, each providing different value.

Text generation can stream token by token as the model produces it. Rather than waiting for the complete response and showing it all at once, text appears incrementally. Users start reading while the agent continues generating. This is the most immediately impactful form of streaming for conversational interfaces.

Reasoning and thinking events show the agent's internal deliberation. When the agent considers what to do next, that reasoning process can be visible. This demystifies agent behavior and helps users understand why certain approaches are taken.

Tool execution status shows when tools are called and when they complete. Users see "Searching the web for quarterly reports" rather than unexplained waiting. If a search takes longer than expected, the user understands why.

Progress through stages indicates movement through multi-step processes. A research task might progress from gathering sources to analyzing content to writing a summary. Each transition can be visible.

Partial results can sometimes be shown before a task completes. Early search results can be displayed while additional searches continue. Initial findings can be shared while analysis proceeds.

Not all events need to stream to users. Some internal operations are not meaningful to show. The goal is providing enough information to understand what is happening without overwhelming with irrelevant detail.

The User Experience Impact

Streaming affects user experience across several dimensions.

Perceived speed improves dramatically. Research shows that progress indicators make waits feel shorter. Streaming takes this further by showing actual work happening, not just animated indicators. The psychological effect is substantial.

Trust and transparency increase when users can see agent reasoning. If the agent explains what it is doing, users better understand its capabilities and limitations. Surprising behavior is less surprising when the reasoning is visible.

Error recovery is faster when problems are visible early. If a tool call fails, the user sees it immediately rather than waiting until the end to learn something went wrong. Users can provide guidance or abort early rather than waiting for a bad result.

Engagement increases when users can read partial responses while the agent continues. Rather than switching away during a long wait, users stay engaged with emerging content.

The flip side is that streaming reveals more about agent behavior, including its limitations. An agent that visibly struggles with a task cannot hide that struggle behind a polished final output. This transparency is generally positive for trust even when it reveals imperfection.

Implementation Approaches

Several technical approaches enable streaming from agent systems.

Server-sent events provide a simple, HTTP-based streaming mechanism. The server holds the connection open and sends events as they occur. Clients receive events in order without polling. This works well for most agent streaming needs.

WebSocket connections enable bidirectional streaming. While agent responses primarily flow server to client, WebSockets allow for more complex interaction patterns where users might send input during streaming. The added complexity is worthwhile for some use cases.

Polling with short intervals is a fallback when true streaming is not available. The client repeatedly requests updates. This is less efficient and higher latency than true streaming but works across all environments.

On the client side, handling streaming events requires incremental UI updates. Text content appends as tokens arrive. Status indicators update as tools execute. The interface must handle both streaming updates and final results gracefully.

Event Types and Handling

A well-designed streaming interface uses typed events that clients can handle appropriately.

Thinking events indicate the agent is reasoning about what to do. Display might show a brief indication like "Considering options..." or might show the actual reasoning text for transparency.

Tool start events fire when a tool call begins. Display shows what tool is being called and with what parameters. "Searching for: quarterly report Q4 2024" tells users what is happening.

Tool completion events fire when a tool call finishes. Display might show a brief summary of the result or simply indicate completion. Duration information can be included.

Content events carry incremental text generation. Display appends new content to the growing response. Proper handling of word boundaries and formatting ensures smooth reading.

Error events indicate something went wrong. Display shows appropriate error messages. The client decides whether to retry, wait for recovery, or show the error to users.

Completion events signal the agent has finished. Display transitions from streaming mode to the final result. Any cleanup or finalization happens.

Clients should handle missing or out-of-order events gracefully. Network issues can cause gaps. Robust implementations maintain consistent state even with imperfect event delivery.

Design Considerations

Several design decisions affect streaming quality.

Granularity determines how often events fire. Too granular (every token, every millisecond) creates overhead and can overwhelm clients. Too coarse (only at task completion) misses the benefits of streaming. Token-level streaming for text generation and operation-level streaming for tool calls is typically appropriate.

Buffering trades latency for efficiency. Sending each token individually creates many small network messages. Buffering multiple tokens before sending reduces message count but increases latency. Small buffers (50-100ms) balance these concerns.

Filtering determines which events reach users. Some internal events are meaningful only for debugging, not for user display. Streaming interfaces should filter to user-relevant events while preserving complete data for observability.

Fallback behavior handles environments where streaming is not available. The system should work without streaming, just with a worse user experience. Design streaming as an enhancement, not a requirement.

Streaming in Multi-Agent Systems

Multi-agent systems present additional streaming considerations.

Events from sub-agents can surface through the orchestrator. When a sub-agent starts working, that event can stream to users. When it completes, the result transitions. This keeps users informed even when work is happening in nested agents.

Parallel sub-agents produce concurrent event streams. The display must handle multiple activities happening at once. A timeline or multi-track display can show parallel work more clearly than a single stream.

Deep nesting can produce overwhelming event volumes. Filtering and summarization become important. Users might see top-level events while detailed sub-agent activity stays in observability logs.

For teams building interactive agent experiences, inference.sh provides streaming as a built-in capability. Agent execution events stream to clients automatically. You build the UI; the runtime handles the event delivery.

Streaming is not just a nice-to-have feature. It fundamentally changes how users experience agent interactions, transforming opaque waits into transparent processes. The implementation effort pays off in user satisfaction and trust.

FAQ

Does streaming add latency to agent responses?

Streaming slightly increases total processing overhead due to event packaging and transmission. However, users perceive streaming responses as faster because they see content immediately rather than waiting for completion. The time to first visible content drops dramatically. Total time to completion might increase marginally, but perceived time decreases substantially. The trade-off strongly favors streaming for interactive use cases. For batch processing where users do not watch responses, streaming overhead is unnecessary and can be disabled.

How do I handle streaming in a chat interface that displays markdown?

Markdown streaming requires careful handling because markdown syntax is only meaningful in complete form. A partial markdown block might have opening syntax without closing syntax. Common approaches include buffering until complete syntactic units are available, rendering partial markdown optimistically and re-rendering as more content arrives, or escaping markdown until blocks complete. The best approach depends on your markdown renderer's capabilities. Some renderers handle incomplete markdown gracefully. Others require complete input. Test with real agent outputs that include code blocks, lists, and other formatted content.

Should I show all agent reasoning to users or just high-level status?

The answer depends on your users and use case. Technical users building with agents often want to see detailed reasoning - it helps them understand and improve agent behavior. End users completing tasks typically want high-level status - enough to understand progress without overwhelming detail. Consider offering both: a clean interface with high-level status by default and an option to expand detail for users who want it. Never hide errors or failures - users should always know when something goes wrong, even if they do not see every internal detail.