Building AI Roundtables: When APIs Fail Silently and LLMs Echo Themselves

Ever wanted to watch two AI models have a conversation? That's exactly what I set out to build this week—a consensus discussion feature where multiple AI providers could engage with each other in structured roundtable discussions. What started as a simple feature request turned into a masterclass in API error handling, streaming architecture, and the quirky behaviors of large language models.

The Vision: AI Roundtables

The goal was straightforward: transform our existing parallel AI discussions (where multiple providers respond independently) into true roundtables where AI models could build on each other's responses. Instead of two separate monologues, we wanted genuine AI-to-AI dialogue.

Here's what we were aiming for:

• Sequential turns: Providers take turns responding, seeing the full conversation history
• Multiple rounds: Each provider gets 2 rounds to engage and refine their thoughts
• Context awareness: Each AI can reference and build upon the other's responses
• Clean UI: Visual layout that makes it clear who's speaking when

Challenge #1: The Case of the Silent API

The first roadblock hit immediately—our OpenAI integration wasn't responding at all in consensus mode. No errors, no timeouts, just... silence.

This is where things got interesting. Our error handling looked reasonable at first glance:

// The problematic pattern
catch (error) {
  return null; // 😱 Silent failure!
}

The API was returning a 401 invalid_api_key error, but our catch block was swallowing it completely. The UI showed nothing, the logs showed nothing, and debugging became a guessing game.

The Fix: Verbose Error Propagation

We restructured the error handling to be much more transparent:

// In streamParallelProviders
catch (error) {
  console.error(`Provider ${provider} error:`, error);
  return { 
    _error: true, 
    provider, 
    message: `API Error: ${error.message}` 
  };
}

Now errors surface as visible messages in the chat stream: --- OPENAI (ERROR) --- API Error: invalid_api_key. Much better than silent failures!

Challenge #2: LLMs That Echo Themselves

With the API issues resolved, we implemented the roundtable logic. The approach was to build a conversation history that clearly identified each speaker:

[ANTHROPIC]: I think the key insight here is...
[OPENAI]: Building on that point, I'd add...
[USER]: What about edge cases?

This worked great for providing context to the models—until we noticed something odd. The LLMs started echoing the bracket format in their own responses:

Claude: "[ANTHROPIC]: I agree with the previous analysis..."

The models were learning the conversation format and applying it to their own responses, creating a recursive labeling problem.

The Solution: Explicit Instructions

We added a specific instruction to the system prompt:

You are ANTHROPIC in this roundtable discussion. You can see responses from other providers prefixed with [PROVIDER_NAME]. 

IMPORTANT: Do NOT prefix your own response with your name or brackets. 
The bracketed format is only for your reference.

This clean separation between internal context formatting and output formatting solved the echo problem completely.

The New Architecture: Sequential Streaming

The final consensus flow looks like this:

1. User sends message → triggers consensus mode
2. Round 1: Provider A responds with full context
3. Context update: Add Provider A's response to history
4. Round 1: Provider B responds seeing Provider A's input
5. Round 2: Provider A responds seeing Provider B's input
6. Round 2: Provider B gets the final word

With CONSENSUS_ROUNDS = 2, each user message generates 4 AI responses total. The streaming implementation handles provider switches mid-conversation, flushing previous content and updating the UI seamlessly.

UI/UX: Fighting Auto-Scroll Expectations

The chat interface needed a complete rethink. With multiple providers and longer conversations, the traditional "always scroll to bottom" approach became disruptive.

Our new approach:

• Spatial layout: Provider A on the left, Provider B on the right, user messages centered
• Smart scrolling: Track scroll position and only auto-scroll when user is already at the bottom
• New message indicator: A floating pill button that shows "New messages" when content arrives while scrolled up

const isNearBottom = () => {
  if (!scrollContainerRef.current) return false;
  const { scrollTop, scrollHeight, clientHeight } = scrollContainerRef.current;
  return scrollHeight - scrollTop - clientHeight < 100;
};

This respects user intent—if they've scrolled up to reference earlier messages, we don't yank them back to the bottom.

Lessons Learned

1. Silent Failures Are the Worst Failures

Error handling should be loud and obvious during development. A 401 API error should never disappear into a catch { return null } block. Always log, always surface, always make debugging easier for your future self.

2. LLMs Learn from Everything

When you format conversation history for context, the models will pick up on those patterns. Be explicit about what's for their reference vs. what they should output. The instruction "do NOT do X" is often necessary alongside "do Y."

3. Auto-Scroll Is Not Always Auto-Good

In multi-participant conversations, users need to reference earlier messages. Aggressive auto-scrolling breaks their mental model. Provide controls and respect their current focus.

4. Streaming + Provider Switching = Complexity

When streaming responses from multiple providers sequentially, you need clean handoff logic. Flush previous streams, update UI state, and make provider transitions visually clear.

What's Next

The consensus discussion feature is working beautifully now—watching Claude and GPT-4 build on each other's ideas is genuinely fascinating. But there's always more to explore:

• Making the round count configurable per discussion
• Adding support for more than 2 providers in a single roundtable
• Experimenting with different conversation structures (debate mode, anyone?)

The key insight from this build: AI-to-AI interactions reveal emergent behaviors you don't see in human-AI chats. The models reference each other, build complex arguments across turns, and sometimes surprise you with the depth of their collaborative reasoning.

Building tools for AI collaboration isn't just about the technical implementation—it's about creating space for these emergent behaviors to flourish.

Have you experimented with multi-AI conversations? I'd love to hear about your experiences with AI collaboration patterns in the comments below.