Why Real-Time Is the Missing Piece in Today’s AI Agents

Thinking... Ruminating... Billowing... Wibbling... Cerebrating...

These words invented by AI companies to mask processing are all very cute, but in reality, they're all just apologetic loading states. When ChatGPT shows "thinking" or Claude displays "ruminating," they're admitting their models aren't ready to interact with you yet. For text chat, a few seconds of delay feels tolerable. You're typing anyway. The agent is "thinking." Fine.

With voice and video, those same delays become intolerable. Speech-to-text takes half a second. LLM reasoning takes a full second. Text-to-speech is another second. Suddenly, the user waits for seconds, wondering if the app has frozen.

For AI to take its next steps into real-world integration, this latency problem has to be solved. Real-time voice and video capabilities transform AI agents from interfaces you interact with into participants you collaborate with. The difference matters more than most developers realize.

Where Traditional AI Agents Break Down

To understand why latency cripples conversational AI, we need to examine how current systems work. Most AI agents follow a sequential pipeline:

When you speak to an AI assistant, your audio travels through separate, disconnected services. Speech-to-text transcribes your words. Only after complete transcription does the LLM receive your message and begin generating a response. Once the LLM finishes reasoning, text-to-speech converts the response back into audio.

Even before network and audio delays, large language models introduce latency at the token level. They process your entire input prompt (the prefill phase) before generating the first token of a response, then decode one token at a time. Each generated token compounds delay, which is why real-time architectures must stream outputs as they're created instead of waiting for complete responses.

The problem compounds because these systems can't overlap their work. The LLM can't start thinking while you're still speaking. The TTS can't begin synthesizing audio until the entire response is complete. Everything happens in sequence, and latency accumulates at each boundary.

HTTP request-response cycles make this worse. Each utterance waits for full completion before the next component can act. There's no streaming context, no way for the system to process audio continuously while simultaneously reasoning and speaking.

The result feels mechanical. The pauses aren't long enough to be obviously broken, but they're long enough to feel wrong.

What Real-Time Actually Means for AI

"Real-time" has become a marketing term in AI, often meaning nothing more than "kinda fast." But true real-time AI requires millisecond latencies between user audio and AI response.

Achieving this demands an architectural shift. Instead of sequential processing, real-time systems stream audio bidirectionally through WebRTC connections. Voice Activity Detection continuously monitors for speech. Speech-to-text transcribes incrementally as you speak. The LLM begins reasoning on partial transcripts. Text-to-speech synthesizes audio in chunks as the response is generated.

Listening, thinking, and speaking happen in parallel, not in sequence.

Modern real-time AI systems coordinate this through two key technologies:

1. WebRTC transport provides low-latency bidirectional streaming. Unlike HTTP, which waits for complete requests and responses, WebRTC maintains continuous connections optimized for media. Audio packets flow in both directions simultaneously with minimal buffering, typically adding only 30-50ms of transport latency.

2. Model Context Protocol (MCP) standardizes how components share context and coordinate actions. When you speak, MCP shares context between STT, LLM, and TTS services. The protocol enables continuous message flow instead of discrete API calls, allowing each component to react incrementally rather than waiting for complete inputs.

This architecture produces a qualitatively different experience. In traditional systems, you speak, wait for silence, then wait for the agent to respond. In real-time systems, the agent can respond mid-sentence, can interrupt when it has urgent information, and can provide feedback while you're still explaining a problem.

The difference transforms AI from a tool you use into a participant you work with.

The Real-Time Stack: How the Pieces Fit Together

Building real-time AI requires coordinating multiple specialized components, each optimized for minimal latency. Here's how they work together:

The distinction between VAD and turn detection illustrates why timing matters so much. VAD answers "is someone speaking right now?" with minimal latency, allowing the system to stop talking when you start. Turn detection answers the harder question: "have they finished their thought?" This requires understanding conversational patterns, detecting intentional pauses versus brief hesitations, and recognizing when someone expects a response.

Getting this wrong produces the awkward experiences we've all had with voice assistants that interrupt us mid-sentence or wait too long after we've clearly finished speaking.

This parallelization reduces latency from seconds to milliseconds. More importantly, it enables behaviors impossible in sequential systems: agents that can interrupt themselves when you start speaking, that can provide feedback while listening, that can adjust their responses based on your tone or video input in real-time.

Why Realtime LLMs Matter

The emergence of Realtime LLMs from OpenAI and Google represents the most significant architectural change in conversational AI. These models don't just process text faster. They process audio directly, eliminating the STT and TTS steps entirely.

Realtime LLMs accept audio input natively. They process PCM data directly over WebRTC connections, reasoning about the speech signal without intermediate text representation. They generate audio output directly, speaking with natural prosody and emotion without text-to-speech synthesis.

The six-step pipeline above collapses to just: Audio → Realtime LLM → Audio

Eliminating two processing steps cuts hundreds of milliseconds of latency from every interaction. But the benefits extend beyond speed. Realtime LLMs can process incomplete thoughts, can respond with verbal acknowledgments while listening, and can adjust their responses based on how you're speaking, not just what you're saying.

They also enable genuinely bidirectional conversation. Realtime LLMs can listen and speak simultaneously, allowing them to provide overlapping or affective audio feedback and to pause or resume output dynamically when users begin speaking.

This creates conversations that feel natural because they follow the actual patterns of human dialogue: overlapping speech, backchannel feedback, collaborative turn-taking.

Real-Time Video Enables New Use Cases

Adding real-time video to everything above opens up entirely new possibilities for AI agents.

Consider a golf coach application. In traditional systems, you'd record a swing, upload it, wait for analysis, then read feedback. The disconnect between action and feedback makes improvement difficult. You can't remember exactly what you were thinking during that specific swing. The delay breaks the learning loop.

With real-time video and voice, the AI watches your swing and provides immediate coaching: "Your weight is shifting too early. Try keeping it on your back foot longer." You adjust. The AI responds: "Better. Now let's work on your follow-through." The feedback loop operates at the speed of thought, enabling natural skill development through conversation.

This pattern extends across domains:

• Manufacturing and robotics: An AI agent monitoring assembly lines can alert workers to defects in real-time, guide them through complex repairs, and coordinate with other systems. It sees what you see, understands what you're doing, and provides contextualized assistance without interrupting workflow.

• Healthcare: Telemedicine agents can observe patient movements during physical therapy, provide real-time corrections, and adjust exercise difficulty based on observed performance. Remote specialists can use AI agents as extensions of their perception, getting immediate analysis of patient conditions.

• Collaboration tools: Meeting assistants that watch and listen in real-time can surface relevant documents when they notice you struggling to explain something, highlight action items as they emerge naturally in conversation, and provide live translation with cultural context.

• Accessibility: Real-time video processing enables sign language translation with the low latency required for natural conversation. An AI agent can translate between ASL and spoken English fast enough that deaf and hearing participants can have genuinely fluid discussions.

Real-time video doesn't just make existing applications faster. It enables entirely new interactions, where AI participates in physical-world activities that require immediate feedback.

Where Real-Time AI Goes Next

The infrastructure for real-time AI agents exists now. Streaming architectures, WebRTC transport, Realtime LLMs that skip transcription entirely.

What's missing is adoption. Most developers still build workarounds with traditional architectures because they're familiar and well-documented. But once users experience agents that respond in milliseconds, they won't tolerate the seconds of delay of batch processing systems.

Real-time changes what AI can do. Agents can participate in physical activities that require immediate feedback. They can guide complex procedures in real-time. They can collaborate on creative work with the responsiveness of a human partner.

The shift won't be gradual. Real-time AI will feel less like an incremental improvement and more like the difference between reading email and having a conversation. One is asynchronous and deliberate. The other happens at the speed of thought.