SUB-SECOND MULTI-AGENT STREAMING WITH EVENT WORKFLOWS

Chaining multiple AI agents together in a sequential pipeline—where Agent A finishes, passes text to Agent B, who processes it and calls Agent C—creates intolerable latency. In enterprise customer-facing applications, users wait 25+ seconds staring at a loading spinner before seeing the first word.

We eliminated sequential bottlenecks by rebuilding our multi-agent orchestrator around an event-driven asynchronous DAG. Specialized agents run in parallel, stream intermediate tokens over WebSockets, and speculatively execute downstream dependencies.

Here is the event-driven architecture, WebSocket streaming protocol, and production Node.js engine.

The Latency Tax of Sequential Multi-Agent Pipelines

Traditional agent frameworks execute steps in a strict linear sequence:

[User Request]
     |
     v  (Wait 6.2s)
[Research Agent] ----(Full JSON Output)----> [Risk Agent]
                                                  |
                                                  v  (Wait 7.1s)
[Final Client Brief] <---(Full Summary)---- [Synthesizer Agent]

Structural Pain Points:

1. Compounding Latency: The user receives zero feedback while agents execute sequentially in the background.
2. Brittle Error Propagation: If the Risk Agent fails at second 13, the entire workflow crashes, wasting all prior LLM execution tokens.
3. State Corruption: Concurrent user requests modifying shared resource states led to race conditions in database mutations.

Event-Driven Architecture: Asynchronous DAG and Streaming States

To break sequential dependencies, we migrated to an event-driven architecture powered by Redis Pub/Sub, Async DAG State Machine, and WebSocket Stream Multiplexing.

                           +------------------------+
                           | User WebSocket Gateway |
                           +-----------+------------+
                                       |
                                       v
                           +------------------------+
                           |   Event Stream Bus     |
                           |   (Redis Pub/Sub)      |
                           +----+--------------+----+
                                |              |
            +-------------------+              +--------------------+
            v                                                       v
  +--------------------+                                  +--------------------+
  | Researcher Agent   |                                  | Market Data Agent  |
  | (Streams Tokens)   |                                  | (Fetches Tickers)  |
  +---------+----------+                                  +---------+----------+
            |                                                       |
            +-------------------+--------------+--------------------+
                                | Chunk Stream |
                                v              v
                      +-----------------------------------+
                      | Risk & Compliance Agent           |
                      | (Speculative Early Execution)     |
                      +-----------------+-----------------+
                                        |
                                        v
                      +-----------------------------------+
                      | WebSocket UI Chunk Streamer       |
                      +-----------------------------------+

Pillar 1: Speculative Early Execution

Instead of waiting for the Researcher Agent to finish its full 2,000-token generation, it streams output chunks into Redis Pub/Sub. The Risk Agent listens to the stream. As soon as key entities are detected (via regex/NER stream parsing), the Risk Agent begins speculative validation concurrently while the Researcher Agent is still completing its response.

Pillar 2: Distributed State Synchronization

To manage state across decoupled workers, we implemented atomic state locks using Redis Redlock:

• State mutations are versioned with optimistic concurrency control.
• If two sub-agents attempt to update the project plan simultaneously, the lock engine enforces determinism based on DAG topological ordering.

Pillar 3: Single-Pass WebSocket Stream Multiplexing

All agent events (token streaming, tool invocations, state updates, errors) are wrapped in typed event envelopes and pushed through a single multiplexed WebSocket connection to the frontend UI.

TypeScript Implementation: Async Multi-Agent Event Dispatcher

Here is our production TypeScript Event Dispatcher that routes streaming tokens between concurrently executing agent workers:

import { EventEmitter } from "events";
import Redis from "ioredis";

interface AgentEvent {
  traceId: string;
  agentId: string;
  eventType: "TOKEN_STREAM" | "TOOL_START" | "TOOL_END" | "STATE_MUTATION";
  payload: Record<string, any>;
}

export class MultiAgentEventDispatcher extends EventEmitter {
  private redisPub: Redis;
  private redisSub: Redis;

  constructor(redisUrl: string) {
    super();
    this.redisPub = new Redis(redisUrl);
    this.redisSub = new Redis(redisUrl);
  }

  public async publishAgentEvent(channel: string, event: AgentEvent): Promise<void> {
    const serialized = JSON.stringify(event);
    await this.redisPub.publish(channel, serialized);
  }

  public async subscribeToTrace(traceId: string, onEvent: (event: AgentEvent) => void): Promise<void> {
    const channel = `trace:${traceId}`;
    await this.redisSub.subscribe(channel);
    
    this.redisSub.on("message", (chan, message) => {
      if (chan === channel) {
        const parsed: AgentEvent = JSON.parse(message);
        onEvent(parsed);
      }
    });
  }
}

Production Benchmarks: Latency Reduction and TTFT Comparison

We measured performance across 2,500 execution traces in enterprise customer deployments:

Metric	Sequential Chain	Event-Driven Async DAG	Improvement
Time to First Token (TTFT)	6,400 ms	380 ms	94.0% reduction
P95 Total Execution Time	22.4 seconds	4.1 seconds	81.7% faster
Workflow Completion Rate	91.2%	99.4%	+8.2% reliability
Concurrent Workflows / Server	45 workflows	320 workflows	7.1x scale factor

Engineering Lessons for Event-Driven AI Systems

1. Stream everything early: Never wait for complete agent responses before triggering downstream analytical tasks.
2. Use explicit DAG state graphs: Do not rely on unpredictable LLM prompts to decide which agent runs next; use hardcoded event routing for control flow.
3. Isolate state mutations: Wrap shared agent state in atomic distributed locks to eliminate race conditions under heavy load.