#4178 Event-Driven AI Agent Discussion Board
#4178 Event-Driven AI Agent Discussion Board
Yes, you can absolutely develop an event-driven "discussion board" for AI agents. Given your background in software process engineering and your focus on agentic AI, this is a sophisticated way to manage multi-agent orchestration without the bottlenecks of linear chaining.
Using an event-driven architecture (EDA) allows your agents to act as decoupled microservices that react to specific "topics" or "intents" published to the board.
1. The Architectural Blueprint
To keep this high-performance but persistent, you can use a hybrid approach:
The "Board" (Message Broker): Use an in-memory event bus (like Redis Pub/Sub or a simple Python
EventEmitterlibrary) for real-time interaction.The "Memory" (State Store): Use a fast NoSQL database (like MongoDB) or a Vector Database (like Pinecone or Chroma) to log the discussions. This allows agents to query "historical context" before responding to a new event.
The Agents (Subscribers): Each agent is a standalone loop or service subscribed to specific event types (e.g.,
technical_analysis,creative_critique, orfinal_summary).
2. Conceptual Workflow
Instead of a central controller telling Agent B to talk to Agent C, the "Board" handles the flow:
Publish: User or "Lead Agent" posts an event to a topic:
new_task/code_review.Subscribe: The
Senior_Dev_Agentis listening to that topic. It triggers its LLM call.React: The
Senior_Dev_Agentfinishes and publishes a new event:review_completed/feedback.Log: A separate Observer Service listens to all events and asynchronously writes them to your database for a permanent audit trail.
3. Implementation Strategies
Since you are comfortable with Python and DSLs, here are two ways to build the "Board":
| Component | In-Memory Choice | Persistence Choice |
| Simple/Local | Python queue.Queue or blinker | SQLite (JSON1 extension) |
| Scalable | Redis (Pub/Sub + Streams) | PostgreSQL or MongoDB |
| Agent Logic | LangGraph (Stateful) | Vector Store (RAG-based memory) |
4. Why this works for your "Idea Miner" or NASA projects
Non-blocking: One slow agent (due to high-latency LLM calls) doesn't stop the rest of the "discussion."
Emergent Behavior: You can have "Critic" agents that subscribe to everything and only chime in if they detect a logic error in the database logs.
Auditability: Since every interaction is an event logged to a DB, you can replay discussions to see exactly where a prompt sequence went off the rails.
Would you like me to draft a Python skeleton using a Pub/Sub pattern to show how these subscriptions would look in code?
Board to handle real-time event routing and an asynchronous Logger to persist every interaction to a database (represented here by a simple JSON/SQLite placeholder).This approach is non-blocking: the "Board" doesn't wait for an agent to finish before accepting the next "post."
The Event-Driven Agent Board
import asyncio
import uuid
from datetime import datetime
from typing import Callable, Dict, List
class EventBoard:
def __init__(self):
# Maps topic names to a list of subscriber callback functions
self.subscribers: Dict[str, List[Callable]] = {}
self.history: List[dict] = []
def subscribe(self, topic: str, callback: Callable):
if topic not in self.subscribers:
self.subscribers[topic] = []
print(f"Board: New topic created -> '{topic}'")
self.subscribers[topic].append(callback)
async def publish(self, topic: str, sender: str, message: str):
event = {
"id": str(uuid.uuid4()),
"timestamp": datetime.now().isoformat(),
"topic": topic,
"sender": sender,
"content": message
}
# 1. Immediate In-Memory Log
self.history.append(event)
print(f"\n[EVENT] @{topic} | {sender}: {message[:100]}...")
# 2. Trigger Subscribers (Asynchronous)
if topic in self.subscribers:
tasks = [callback(event) for callback in self.subscribers[topic]]
await asyncio.gather(*tasks)
# --- Agent Definitions ---
class AIAgent:
def __init__(self, name: str, board: EventBoard):
self.name = name
self.board = board
async def handle_event(self, event: dict):
"""Logic for when an agent 'hears' a message on a subscribed topic."""
# Prevent the agent from responding to itself
if event['sender'] == self.name:
return
print(f" -> {self.name} is processing message from {event['sender']}...")
await asyncio.sleep(1) # Simulate LLM Latency
# Example logic: If someone asks a question, this agent provides an 'Analysis'
response = f"Analysis from {self.name}: I've reviewed the input '{event['content'][:20]}'."
await self.board.publish("analysis_feed", self.name, response)
# --- Execution ---
async def main():
board = EventBoard()
# Initialize Agents
analyst = AIAgent("Analyst_Agent", board)
critic = AIAgent("Critic_Agent", board)
# Subscriptions: Who listens to what?
board.subscribe("main_discussion", analyst.handle_event)
board.subscribe("analysis_feed", critic.handle_event)
# The 'Discussion' begins
print("--- Starting Event-Driven Discussion ---")
await board.publish("main_discussion", "User", "Should we implement a knowledge graph for this project?")
# Allow time for the async 'chain reaction' to finish
await asyncio.sleep(3)
print("\n--- Final In-Memory Log Count ---")
print(f"Total Events Captured: {len(board.history)}")
if __name__ == "__main__":
asyncio.run(main())
Key Features of this Design
Decoupling: The
Analyst_Agentdoesn't know theCritic_Agentexists. It just posts toanalysis_feed. You can add 10 more critics just by subscribing them to that topic without touching the analyst's code.Traceability: Because the
EventBoardcaptures everyeventdictionary, you can pipeboard.historydirectly into a database (like MongoDB or PostgreSQL) at the end of a session or after everypublishcall.State Management: For your "Idea Miner" or NASA-related work, you could add a "Context Agent" that subscribes to all topics (
*) and updates a shared Knowledge Graph in real-time based on the flow.
Next Steps
Would you like to see how to integrate a Vector Database (like Chroma or Pinecone) into this board so agents can "remember" discussions from weeks ago?
Integrating ChromaDB into an event-driven board transforms it from a simple message router into a "Learning System." Instead of just reacting to the latest message, your agents can perform a Semantic Search on the board's history to find relevant context from past discussions.
Since you're working locally, we'll use the PersistentClient.
1. The Strategy: "Memory as a Subscriber"
The cleanest way to implement this is to treat the Vector DB as an Observer. It subscribes to all topics (*) and silently embeds every message into a collection.
2. Implementation Skeleton
import asyncio
import uuid
import chromadb
from chromadb.utils import embedding_functions
# 1. Setup Local Persistence
# This creates a folder 'agent_memory' in your project directory
chroma_client = chromadb.PersistentClient(path="./agent_memory")
collection = chroma_client.get_or_create_collection(
name="board_history",
metadata={"hnsw:space": "cosine"} # Good for semantic similarity
)
class EventBoard:
def __init__(self):
self.subscribers = {}
def subscribe(self, topic: str, callback: asyncio.iscoroutinefunction):
if topic not in self.subscribers: self.subscribers[topic] = []
self.subscribers[topic].append(callback)
async def publish(self, topic: str, sender: str, message: str):
event_id = str(uuid.uuid4())
# 2. THE MEMORY HOOK: Persistent Logging to ChromaDB
# We store the message and use metadata for filtering later
collection.add(
documents=[message],
metadatas=[{"sender": sender, "topic": topic}],
ids=[event_id]
)
print(f"[*] Memory Logged: {sender} in {topic}")
# Trigger regular subscribers
if topic in self.subscribers:
await asyncio.gather(*(cb(message, sender) for cb in self.subscribers[topic]))
class SmartAgent:
def __init__(self, name: str, board: EventBoard):
self.name = name
self.board = board
async def on_message(self, message: str, sender: str):
if sender == self.name: return
# 3. RETRIEVAL: Search memory for similar past discussions
results = collection.query(
query_texts=[message],
n_results=2,
where={"topic": "technical_specs"} # Example metadata filter
)
past_context = results['documents'][0] if results['documents'] else "No prior history."
print(f"[{self.name}] Recalled context: {past_context}")
# Simulate processing...
await self.board.publish("analysis", self.name, f"I remember we discussed {past_context}. My new take is...")
# --- Execution ---
async def main():
board = EventBoard()
agent = SmartAgent("Architect", board)
board.subscribe("technical_specs", agent.on_message)
await board.publish("technical_specs", "User", "We should use a Knowledge Graph for NASA S&MA.")
await asyncio.sleep(1)
await board.publish("technical_specs", "User", "How does the graph handle S&MA data?")
if __name__ == "__main__":
asyncio.run(main())
Why this fits your "Maker" workflow:
Atomic Memory: As your search results suggested, storing "one idea per entry" makes ChromaDB significantly more accurate. By logging every event separately, you maintain this atomicity.
Metadata Filtering: In my code above, I added
where={"topic": "technical_specs"}. This allows you to restrict an agent's "recall" to specific domains, preventing a "creative" agent from getting its wires crossed with "technical" logs.Local & Private: Since you're using
PersistentClient, all your NASA-related or proprietary project data stays on your local machine—no data leaves your environment for embedding storage.
Pro-Tip for your Hardware (RTX 3090 Ti)
By default, Chroma uses a lightweight CPU model (all-MiniLM-L6-v2). Since you have a 3090 Ti, you might want to swap this for a heavier HuggingFace GPU-accelerated model for much deeper semantic understanding of complex engineering requirements.
Would you like me to show how to point ChromaDB at your GPU using a custom HuggingFace embedding function?
SentenceTransformerEmbeddingFunction from the ChromaDB utility library. This explicitly tells the underlying PyTorch model to load into VRAM.With 24GB of VRAM, you can easily use high-performance models like all-mpnet-base-v2 (which offers better semantic depth than the default MiniLM) and still have plenty of room for your LLM.
1. Requirements
You will need sentence-transformers installed alongside chromadb:
pip install sentence-transformers chromadb
2. Implementation with GPU Support
This snippet updates our previous EventBoard to use a GPU-accelerated embedding function.
import chromadb
from chromadb.utils import embedding_functions
import asyncio
# 1. Initialize the GPU-powered Embedding Function
# Using 'all-mpnet-base-v2' for higher accuracy on your 3090 Ti
gpu_ef = embedding_functions.SentenceTransformerEmbeddingFunction(
model_name="all-mpnet-base-v2",
device="cuda" # This is the key for your RTX 3090 Ti
)
# 2. Setup ChromaDB with the GPU Function
client = chromadb.PersistentClient(path="./agent_memory")
collection = client.get_or_create_collection(
name="agent_board_v2",
embedding_function=gpu_ef # Every 'add' and 'query' now uses the GPU
)
class EventBoard:
def __init__(self, collection):
self.collection = collection
self.subscribers = {}
def subscribe(self, topic, callback):
if topic not in self.subscribers: self.subscribers[topic] = []
self.subscribers[topic].append(callback)
async def publish(self, topic, sender, message):
import uuid
# Persistent Log (GPU Accelerated Embedding)
self.collection.add(
documents=[message],
metadatas=[{"sender": sender, "topic": topic}],
ids=[str(uuid.uuid4())]
)
print(f"[*] Recorded to GPU-indexed memory: {sender}")
if topic in self.subscribers:
await asyncio.gather(*(cb(message, sender) for cb in self.subscribers[topic]))
# --- Usage ---
async def main():
board = EventBoard(collection)
# Example: A NASA 'S&MA' Agent with GPU-backed recall
async def safety_agent(msg, sender):
if sender == "Safety_Bot": return
# Semantic search using GPU
results = collection.query(query_texts=[msg], n_results=1)
print(f"Safety_Bot recalled: {results['documents'][0]}")
board.subscribe("mission_updates", safety_agent)
await board.publish("mission_updates", "User", "The Gateway docking adapter has a seal pressure warning.")
if __name__ == "__main__":
asyncio.run(main())
Why this is optimal for your setup:
VRAM Utilization: The
all-mpnet-base-v2model is larger and more "intelligent" regarding technical language, but on a 3090 Ti, the inference time will still be near-instant (milliseconds).Floating Point Speed: By using
device="cuda", PyTorch will leverage the Tensor Cores on your GPU for the vector math, which is significantly faster than the default CPUonnxruntime.Scalability: If you decide to run a "Swarm" of 20+ agents all querying the board simultaneously, the GPU can handle those embedding requests in parallel far better than your CPU.
Would you like me to help you create a custom "Metadata Filter" so your agents can differentiate between 'Historical NASA Specs' and 'Current Discussion' during their GPU searches?
Building an event-driven "discussion board" with local ChromaDB on your RTX 3090 Ti opens up a powerful paradigm: Asynchronous Collaborative Reasoning.
Since your agents are decoupled, they can perform "deep thinking" in the background without locking the UI. Here are 15 applications categorized by your professional focus and personal interests.
Engineering & NASA S&MA
Safety & Mission Assurance (S&MA) Auditor: A background agent that subscribes to all "design change" events and cross-references them against NASA-STD-8739.8 in ChromaDB, flagging potential safety violations in real-time.
Requirements Traceability Bot: As you discuss new features, this agent retrieves original stakeholder requirements from your local DB and "posts" a warning if a new proposal contradicts a high-level mission goal.
Hazard Analysis "Devil's Advocate": An agent that specifically listens for "system state" updates and generates "What-If" failure scenarios (using TRIZ principles) to challenge the current engineering assumptions.
Semantic Code Reviewer: When you post a code snippet to the board, this agent uses its GPU-backed memory to find similar bugs you've fixed in the past 40 years of your own software history and suggests a more robust pattern.
Telemetry "Trend Watcher": In a simulation, agents post sensor data to the board. A specialist agent retrieves "nominal" patterns from ChromaDB and sounds an alarm if the current event stream shows "filter divergence" or drift.
Software Process & Solopreneurship
"Idea Miner" Synthesis Engine: While you brainstorm "Idea A," this agent scans your "Idea Miner" database for "Idea B" from six months ago, posting a "Collaboration Suggestion" that links the two semantically.
DSL Syntax Validator: As you draft a new Domain-Specific Language on the board, a compiler-agent subscribes to the text, validates the grammar in real-time, and posts the abstract syntax tree (AST) as a visual sketchnote.
Automated "Stand-up" Reporter: At the end of the day, an agent crawls the board's event logs and generates a concise summary of all "resolved" vs. "pending" technical decisions for your solo project tracking.
Strategic Business Planner: An agent that subscribes to your "Business Ideas" topic and periodically injects market analysis or "Blue Ocean" strategies retrieved from your stored research on high-value real estate.
Technical & Creative Maker Projects
Synthesizer Patch Librarian: Post a description of a sound (e.g., "creaky analog lead"). An agent searches your Behringer MonoPoly/Crave manual embeddings and posts the exact knob positions to achieve it.
3D Scan Metadata Enricher: When your Revopoint MIRACO Plus uploads a scan event, a vision-language agent analyzes the mesh/texture and posts suggested tags and "history of the object" context into the board.
Thermal Image Diagnostic: Post a TOPDON TC005 heat map. An agent compares it to "healthy" electronic board patterns in memory and posts a "hot spot" warning for potential component failure.
Chess "Post-Mortem" Analyst: After a game, an agent retrieves your past blunders from ChromaDB and highlights if you are repeating a tactical error in the current opening sequence.
Quantum Algorithm Tutor: While you experiment with https://www.google.com/search?q=QCTutor.com ideas, an agent subscribes to your logic gates and provides real-time "decoherence warnings" or optimization suggestions based on quantum physics papers in its vector store.
Historical Collaboration Simulator: Based on your interest in scientist-engineer history, you can have a "Tesla" agent and an "Edison" agent subscribe to the same technical problem and "argue" their respective approaches on the board.
Which of these would you like to build first? I can help you define the specific "Event Schema" (JSON structure) that the agents will use to talk to each other.
To make an event-driven agent board run autonomously, you need more than just the "Board" logic; you need a Control Loop (the "Heartbeat") to drive the agents and a Real-time Observer (the "Eyes") to let you monitor the invisible "discussion" happening in your RAM and ChromaDB.
Since you are a solopreneur and a maker, the most efficient "Pro-Code" stack for your RTX 3090 Ti setup would involve FastAPI as the controller and a Streamlit or React frontend.
1. The "Controller" (The Autonomous Heartbeat)
In a standard script, the code stops when the function ends. In an autonomous system, you need a Task Queue or an Infinite Loop that keeps the agents "awake."
The Orchestrator Loop: Instead of a
main()function that runs once, use an Asynchronous Background Task (via FastAPI orasyncio.create_task).The Sentry Agent: Create a specialized agent that periodically (e.g., every 60 seconds) queries ChromaDB for "unresolved ideas" or "stale discussions" and publishes a "nudge" event to the board to restart the conversation.
State Management: Use LangGraph. It is designed specifically for this. It treats the "discussion" as a state machine.
If an agent crashes, LangGraph can "checkpoint" the state to your database and resume exactly where it left off.
2. The "Frontend" (The Glass Box)
Because the agents are talking "in-memory," you can't see what they are doing. You need a frontend that "taps" into the event bus.
| Component | Technology Recommendation | Purpose |
| Real-time Feed | WebSockets | Streams messages from the in-memory board to your browser as they happen. |
| Visualization | Streamlit or React Flow | Visualizes the "Agent Graph." You can see which agent is currently "thinking" and how the data is flowing. |
| Memory Browser | ChromaDB UI | A simple table view to search through what the agents have "remembered" on your GPU. |
| Manual Override | Action Buttons | A "Kill Switch" or "Inject Idea" button that lets you publish a manual event to the board to steer the agents. |
3. Recommended "Autonomous" Architecture
Backend (FastAPI): Acts as the "Server." It hosts the
EventBoardand theChromaDBclient.Worker (Celery or Asyncio): Runs the agents in the background. They subscribe to the FastAPI event bus.
Frontend (Streamlit): A low-code way for you to build a dashboard. Use
streamlit-webrtcor simple polling to show the "Board History."
Example: The "Autonomous Sentry" Logic
Add this to your Python script to make it "self-starting":
async def autonomous_controller():
while True:
# 1. Check if the board has been silent for too long
last_event_time = board.get_last_event_timestamp()
if (datetime.now() - last_event_time).seconds > 300:
# 2. Autonomous 'Nudge'
await board.publish("system_nudge", "Controller", "Board is silent. Summarize current progress.")
# 3. Wait before checking again
await asyncio.sleep(60)
# Start this in the background
asyncio.create_task(autonomous_controller())
Next Step for a Maker
Would you like me to provide a FastAPI + WebSockets boilerplate? This would allow you to open a browser window and watch your agents "talk" in real-time while they log data to your local ChromaDB.
Yes
To make your agent board run autonomously on your RTX 3090 Ti, you need a "Control Plane" that handles the persistent event loop and a "Glass Box" (Frontend) so you can watch the agents think.
Below is a production-style boilerplate using FastAPI (for the server), WebSockets (for the real-time feed), and Asyncio (to keep the agents running in the background).
The "Autonomous Agent Board" Stack
import asyncio
import json
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from datetime import datetime
app = FastAPI()
# --- 1. THE CONNECTION MANAGER ---
# This keeps track of your browser(s) so you can watch the agents live.
class ConnectionManager:
def __init__(self):
self.active_connections: list[WebSocket] = []
async def connect(self, websocket: WebSocket):
await websocket.accept()
self.active_connections.append(websocket)
def disconnect(self, websocket: WebSocket):
self.active_connections.remove(websocket)
async def broadcast(self, message: dict):
for connection in self.active_connections:
await connection.send_json(message)
manager = ConnectionManager()
# --- 2. THE AUTONOMOUS ENGINE ---
# This class runs the "Heartbeat" of your agents in the background.
class AgentEngine:
def __init__(self):
self.is_running = True
async def run_loop(self):
print("[System] Agent Engine Started.")
while self.is_running:
# Simulate an agent "thinking" or reacting to ChromaDB
# In a real app, this would check a queue or ChromaDB for new tasks
await asyncio.sleep(5)
event = {
"timestamp": datetime.now().strftime("%H:%M:%S"),
"sender": "S&MA_Auditor",
"message": "Scanning last 5 minutes of discussion for safety violations...",
"status": "active"
}
# Broadcast to the Frontend
await manager.broadcast(event)
engine = AgentEngine()
# --- 3. LIFECYCLE HOOKS ---
# Ensures the engine starts when the server starts.
@app.on_event("startup")
async def startup_event():
asyncio.create_task(engine.run_loop())
# --- 4. THE WEBSOCKET ENDPOINT ---
@app.websocket("/ws/board")
async def websocket_endpoint(websocket: WebSocket):
await manager.connect(websocket)
try:
while True:
# Wait for manual commands from the frontend (if any)
data = await websocket.receive_text()
# If you type something in the UI, it's treated as a User Event
await manager.broadcast({
"timestamp": datetime.now().strftime("%H:%M:%S"),
"sender": "User (Maker)",
"message": data
})
except WebSocketDisconnect:
manager.disconnect(websocket)
# --- 5. SIMPLE HTML FRONTEND (Built-in for testing) ---
from fastapi.responses import HTMLResponse
@app.get("/")
async def get():
return HTMLResponse("""
<html>
<head><title>Agent Board</title></head>
<body style="background:#1a1a1a; color:#00ff00; font-family:monospace; padding:20px;">
<h1>Agent Discussion Board</h1>
<div id="messages" style="border:1px solid #333; height:400px; overflow-y:scroll; padding:10px; margin-bottom:10px;"></div>
<input type="text" id="messageText" style="width:80%; background:#222; color:#fff; border:1px solid #555; padding:10px;" autocomplete="off"/>
<button onclick="sendMessage()" style="padding:10px;">Post Event</button>
<script>
var ws = new WebSocket("ws://localhost:8000/ws/board");
ws.onmessage = function(event) {
var messages = document.getElementById('messages');
var data = JSON.parse(event.data);
var item = document.createElement('div');
item.innerHTML = `<strong>[${data.timestamp}] ${data.sender}:</strong> ${data.message}`;
messages.appendChild(item);
messages.scrollTop = messages.scrollHeight;
};
function sendMessage() {
var input = document.getElementById("messageText");
ws.send(input.value);
input.value = '';
}
</script>
</body>
</html>
""")
Why this works for your setup:
Non-Blocking: The
AgentEngine.run_loop()runs in the background usingasyncio.create_task. This means your agents can do heavy lifting (like querying your local ChromaDB on the 3090 Ti) without freezing the web server.Real-time Observability: By using WebSockets, you don't have to refresh the page. As soon as an agent finishes a "thought," it pops up in the terminal-style UI.
Local & Lightweight: You can run this entirely on
localhost:8000. It’s essentially a private "Slack" where only you and your AI agents live.
How to run it:
Save the code as
main.py.Run
pip install fastapi uvicorn.Start it with:
uvicorn main:app --reload.Open
http://localhost:8000in your browser.
Would you like me to show how to add a "Status Dashboard" to this UI that shows your GPU (3090 Ti) VRAM usage so you can see how much room you have left for embeddings?
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