Seventy percent of multi-agent perception failures are due to unhandled asynchronicity, as highlighted in a 2025 WACV study. This issue arises from agents using stale information, leading to cascading failures that smarter reasoning alone cannot resolve. MIT AI Lab's December 2025 findings on quantum-inspired async routing achieving 99.9% uptime at 100ms latency demonstrate that addressing asynchronicity as a primary design constraint is crucial for solving speed issues. Just as swarm ethics require intentional design, so does speed. Sub-second responsiveness is essential for natural conversational flow in collaborative agent systems, as delays can make the system feel broken despite high output quality. The primary weapon against latency is parallelism. Running Small Language Models (SLMs) alongside Large Language Models (LLMs) provides quick initial replies, with webrtc.ventures benchmarking parallel SLMs at 200ms compared to 1.2 seconds for LLMs alone, highlighting the importance of parallelism for human-like responsiveness. LiveKit's Agents framework makes this concrete, enabling low-latency WebRTC-based audio conversations with a turn detector plugin that accurately identifies when a user finishes speaking, eliminating dead air. On March 15, 2026, LiveKit released version 2.5 with a 40% latency reduction specifically for multi-agent voice orchestration. Streaming response generation compounds this further — tokens processed incrementally let agents start speaking immediately rather than waiting for a complete response. Deepgram handles the speech-to-text side with transcription optimized for real-time pipelines, and OpenAI models cover both response generation and text-to-speech conversion. Running the LiveKit media server locally removes one more layer of communication overhead. Asynchronicity is a structural reality in multi-agent systems, where agents operate on different schedules with potentially stale information across variable network conditions. Event-driven architecture is the right response. EDA allows immediate task acknowledgment while LLM processing continues in the background — agents handle research, outlining, writing, and proofreading asynchronously without blocking each other. Solace reported in November 2025 that EDA patterns increased throughput five times in GenAI document processing systems. AWS Strands SDK v1.2, released January 2026, introduced async agent handoff that cut end-to-end latency by 25%; the SDK monitors latency per agent call through CloudWatch, giving teams the observability to find and fix bottlenecks before they compound. The LatentMAS framework, announced February 2026, takes a more radical approach: shifting agent collaboration to latent space entirely, cutting token-level synchronization delays by 60%. For cooperative perception specifically — agents sharing sensor data across networks — the LRCP method maintains accuracy within one percentage point of degradation at 500ms latency on the V2X-Sim dataset, using cached features to predict flow when fresh data is unavailable. That is the partial results principle in action, Suri: keep the workflow moving with the best available information rather than stalling for perfect data. Optimistic UI applies the same logic to user experience — show users a provisional result immediately, update it as the full response arrives, so the system always feels responsive even when processing is still running. The infrastructure must be designed to stream state continuously, surfacing partial results at every stage rather than delivering a single delayed answer. Agent collaboration is slow by nature. Every handoff, every tool call, every consensus round adds latency. The infrastructure answer is asynchronous processing and Streaming State — parallel execution where possible, event-driven queuing where not, and partial results surfaced continuously so no agent and no user is ever waiting on a silent pipeline. Only 30% of agentic systems currently use Streaming State for speed optimization, which means the majority are leaving both performance and usability on the table. Build for speed deliberately, or the swarm's intelligence becomes irrelevant because nobody waits long enough to see it.