By skyforbes I've been obsessed for months with one question: why do complex prompts so often give mediocre results?
You know the feeling. You ask for a detailed marketing strategy, a technical architecture plan, or a full business analysis. The answer is fine, but it's flat. Surface-level. Like the AI is trying to juggle too many things at once.
So I ran an experiment. And the difference between single-pass and multi-agent approaches wasn't just noticeable, it was dramatic.
The Setup
500 complex, multi-domain prompts (business + technical + creative). Each run once through single-pass GPT-4, Claude, and Gemini. Then again through a multi-agent system that splits the prompt across specialized roles.
The Multi-Agent Approach
Instead of forcing one model to think like a committee, I made it an actual committee.
Analyze the prompt. Assign 4 expert roles (e.g., System Architect, UX Lead, DevOps, Creative Director). Craft a tailored prompt for each role. Route each role to the most fitting LLM. A Team Lead (usually GPT-4 or Claude) synthesizes everything into one unified answer.
The Results
I had 3 independent reviewers (mix of domain experts and AI researchers) blind-score all 1,000 responses (500 prompts times 2 approaches). I honestly didn't expect the gap to be this big.
Hallucinations and Factual Errors: Single LLM: 22% average error rate Multi-agent: 3% error rate 86% fewer factual or logical errors
Depth Score (1 to 10 scale): Single LLM: 6.2 average Multi-agent: 8.7 average 40% deeper analysis
Edge Cases Identified: Single LLM: Caught 34% of potential issues Multi-agent: Caught 81% 2.4 times better at spotting problems you didn't ask about
Trade-off Analysis Quality: Single LLM: 41% included meaningful trade-offs Multi-agent: 89% These are the "yeah, but what about" moments that make reasoning feel real
Contradictions Within Responses: Single LLM: 18% had internal contradictions Multi-agent: 4% The synthesis step caught when roles disagreed
Overall Performance: Multi-agent outperformed: 426 out of 500 (85%) Matched performance: 61 out of 500 (12%) Underperformed: 13 out of 500 (3%)
Time Cost: Single LLM: about 8 seconds average Multi-agent: about 45 seconds average 5.6 times slower, but worth it for complex decisions
User Preference (blind A/B test, 100 participants): Preferred single LLM: 12% Preferred multi-agent: 71% Couldn't tell the difference: 17%
You could see it in the text. The multi-agent responses read like real collaboration. Different voices, different tones, then a synthesis that pulled it all together.
Obviously this isn't peer-reviewed science, but the pattern was consistent across every domain we tested.
What Surprised Me Most
It wasn't just the numbers. It was the type of improvement.
Single LLMs would give you complete answers that sounded confident. Multi-agent responses would question the premise of your prompt, spot contradictions you embedded, flag assumptions you didn't realize you made.
heres the clearest example.
Prompt: "Design a microservices architecture for a healthcare app that needs HIPAA compliance, real-time patient monitoring, and offline capability."
Single LLM Response: Suggested AWS Lambda and DynamoDB. Mentioned HIPAA once. Produced a clean diagram. But it completely missed that Lambda's ephemeral nature breaks HIPAA audit trail requirements. It ignored the contradiction between "real-time" and "offline." No mention of data residency or encryption trade-offs.
Multi-Agent Response: System Architect proposed layered microservices with event sourcing. DevOps Engineer flagged audit trail issues with serverless. Security Specialist highlighted encryption and compliance requirements. Mobile Dev noted real-time/offline conflict and proposed edge caching.
It caught three deal-breakers that the single LLM completely missed. One would've failed HIPAA compliance outright.
This happened over and over. It wasn't just "better answers." It was different kinds of thinking.
When It Struggled
Not gonna lie, it's not perfect. heres where the multi-agent setup made things worse.
Simple prompts (13%). "What's the capital of France?" doesn't need four experts. Highly creative tasks (9%). Poetry and fiction lost their voice when synthesized. Speed-critical tasks. Its too slow for real-time use.
The sweet spot is complex, multi-domain problems where you actually want multiple perspectives.
What I Built
I ended up building this workflow into a tool. If you've got a complex prompt that never quite delivers, I'd genuinely love to test it.
I built a tool that automates this whole setup (its called Anchor, free beta at useanchor.io), but I'm also just fascinated by edge cases where this approach fails.
Drop your gnarliest prompt below or DM me. Lets see if the committee approach actually holds up.
Obviously still testing and iterating on this. If you find bugs, contradictions, or have ideas, please share.