A new paradigm is emerging in AI: Instead of relying on a single model for answers, leading innovators are orchestrating multiple AI models to deliberate, debate, and synthesize responses. In December 2025, this approach is no longer experimental—it's becoming the new standard for high-stakes decision-making.
When Andrej Karpathy released his LLM Council and Satya Nadella demoed his Deep Research app within weeks of each other, I found myself in a unique position. Over the past year, I've been building AI Crucible—an ensemble platform exploring similar ideas. Watching these two luminaries validate the ensemble approach with their own implementations was both humbling and energizing.
Several people asked me to comment on how these approaches compare, and what I've learned building AI Crucible. This article is my attempt to do that—not as a competitive analysis, but as observations from someone equally fascinated by the challenge of orchestrating multiple AI models effectively.
What I'll explore:
Reading time: 15-18 minutes
Before diving into specific implementations, let's understand the fundamental problem these systems solve.
Even the most advanced LLMs have blind spots:
A single model, no matter how large, represents a single perspective with its own biases and limitations.
By orchestrating multiple models, ensemble systems can:
This isn't just about getting "better" answers—it's about getting more reliable, nuanced, and trustworthy AI assistance.
When Karpathy open-sourced his LLM Council, one can recognize the the elegance in his approach and his usuall brilliance to use code as a learning tool.
Released on GitHub as an open-source project, Andrej Karpathy's LLM Council implements a structured peer review process where multiple LLMs collaboratively answer questions.
The LLM Council follows a three-phase process:
User query is dispatched simultaneously to multiple models:
Each model generates a response independently, without seeing others' answers.
Each model reviews the anonymized responses from its peers:
This anonymization prevents bias based on model reputation.
A designated "Chairman" model (typically GPT-5.1 or Claude Opus 4.5):
1. Anonymization Prevents Bias
By hiding model identities during peer review, the system ensures evaluation based on content quality, not model reputation.
2. Peer Pressure Improves Quality
Models produce better outputs knowing they'll be peer-reviewed by other advanced LLMs.
3. Chairman Provides Coherence
The final synthesis step ensures the user gets a single, well-structured answer rather than a collection of competing responses.
In Karpathy's testing:
Karpathy's single elegant pattern actually maps to three of our strategies, depending on how you look at it:
Closest match: Competitive Refinement
The independent response phase mirrors our Competitive Refinement strategy:
Peer review phase: Elements of Debate Tournament
The peer review has debate-like qualities:
Chairman synthesis: Collaborative Synthesis
The chairman mirrors our arbiter model:
Key insight: Karpathy combined the best parts of three strategies into one streamlined workflow. That's the elegance—he didn't need separate strategies because he found the optimal middle ground. It makes me wonder if we've overcomplicated things with seven strategies.
Watching Nadella's demo was a great presentation of practical ensemble AI. What struck me most wasn't the technical aspect, as was how he built it over a Thanksgiving weekend and immediately used it for real decisions—even if one was picking the best Indian Test cricket XI. I suspect he was vibe-coding with Claude Opus 4.5 or Gemini 3.
At a developer event in Bengaluru on December 11, 2025, Microsoft CEO Satya Nadella demoed a personal "deep research" app he built over Thanksgiving weekend. This production-grade application showcases Microsoft's vision for ensemble AI in enterprise settings.
Built using Microsoft's own tools:
Nadella's app implements three distinct metacognitive approaches:
Concept: "Chain of debate" - multiple agents deliberate, then synthesize
Similar to Karpathy's approach but with iterative deliberation:
Key difference from LLM Council: Models see and respond to each other's arguments in real-time, creating true debate rather than just peer review.
Concept: Assign specialized roles to different models based on their strengths
Roles defined:
| Role | Model | Responsibility |
|---|---|---|
| Lead Researcher | Claude Opus 4.5 | Breadth-first research, comprehensive coverage |
| Critical Reviewer | Phi-1/GPT | Methodology validation, bias checking |
| Data Analyst | Gemini | Quantitative analysis, pattern recognition |
| Domain Expert | Kimi K2 | Specialized knowledge, context-specific insights |
Each role has explicit success criteria and deliverables. The framework ensures no perspective is overlooked.
Concept: Treat models as MCP (Model Context Protocol) servers, anonymize responses, fuse outputs
Process:
Key innovation: Models are pluggable "servers" - easy to swap in new models or remove underperforming ones.
Nadella's app streams the deliberation process live:
This transparency builds trust and helps users understand how conclusions are reached.
Nadella demonstrated the system by asking it to select an all-time Indian Test cricket XI.
Consensus picks (all models agreed):
Debate points (models disagreed):
The final XI emerged through weighted voting, with the system explaining why certain players were selected despite minority dissent.
Nadella positioned this as the future of Microsoft Copilot:
"These metacognitive decision frameworks need to come to Copilot and real domains—healthcare, finance, supply chain—where multi-agent debates improve decisions."
The goal: bring council-based AI to production systems where decisions have real consequences.
✅ Production-ready - Built with Microsoft enterprise stack ✅ Multiple frameworks - Choose approach based on problem type ✅ Live transparency - Watch deliberation in real-time ✅ Role specialization - DXO framework leverages model strengths ✅ Token budgeting - "Auto" selector optimizes cost vs quality ✅ MCP integration - Models as pluggable servers
⚠️ Proprietary - Not open source ⚠️ Microsoft ecosystem - Tight coupling with Azure/Office ⚠️ Limited documentation - Demo-stage, not yet productized ⚠️ Cost unclear - No published pricing for ensemble operations
Nadella's three frameworks map almost 1:1 to our strategies. The convergence is both validating and slightly unsettling:
1. AI Council → Collaborative Synthesis (near-identical)
His "chain of debate" with a chairperson:
2. DXO (Role-Based Analysis) → Expert Panel (perfect match)
This is remarkably close:
Fascinating detail: He chose Claude Opus for Lead Researcher (breadth-first), GPT for Critical Reviewer (bias checks), and Gemini/Kimi for Data Analyst/Domain Expert. That's domain-aware model selection—exactly what our AI Assistant recommends.
3. Ensemble (Anonymized Synthesis) → Competitive Refinement
His MCP-based synthesis:
What's missing: Neither Karpathy nor Nadella implements structured Debate Tournament (formal argumentation with judges) or Red Team/Blue Team (adversarial security testing). These might be the unique value AI Crucible provides.
I should be transparent about my perspective here: I've been building AI Crucible over the past year, exploring many of these same ideas. When I started, ensemble AI was still niche. Now, watching Karpathy and Nadella arrive at similar conclusions has been validating—but also humbling. They each solved the ensemble problem in beautifully simple ways.
AI Crucible took a different path. Instead of one elegant approach, we've been exploring multiple ensemble strategies, each optimized for different problem types. It's messier, more complex, and sometimes I wonder if we've overcomplicated things. But we're learning fascinating lessons about when different collaboration patterns work best.
Our core hypothesis has been that different problems need different collaboration patterns. A security review shouldn't work like creative writing. A debate about decisions shouldn't follow the same structure as synthesizing research. Here's what we've implemented (see our detailed guide on all seven strategies):
Best for: Creative content, marketing copy, product ideas
How it works:
New features (2025):
Use case: "Write a compelling product launch email for our AI writing assistant"
Best for: Business strategy, research reports, comprehensive analysis
How it works:
New features:
Use case: "Develop a go-to-market strategy for our B2B SaaS product"
Best for: Multi-domain problems, balanced perspectives, research
How it works:
New features:
Use case: "Analyze the feasibility of building vs. buying our CRM system"
Best for: Decision support, controversial topics, pros/cons analysis
How it works:
New features:
Use case: "Should we build a custom CRM or purchase an existing solution?"
Best for: Security review, vulnerability testing, adversarial analysis
How it works:
New features:
Use case: "Review our API authentication flow for security vulnerabilities"
Best for: Project planning, complex implementations, structured workflows
How it works:
New features:
Use case: "Create a comprehensive migration plan for our monolith-to-microservices transition"
Best for: Technical problems, step-by-step reasoning, math/logic
How it works:
New features:
Use case: "Design an optimal scheduling algorithm for our delivery fleet"
Early on, we realized a problem: with 7 strategies, a multitude of models, and countless configuration options, users were overwhelmed. Which strategy? Which models? How many rounds?
The AI Prompt Assistant emerged from watching people struggle with these choices. It's not perfect, but it's our attempt to make ensemble AI more accessible.
Three-agent system:
Classifier Agent - Detects prompt domain and complexity
Prompt Engineer Agent - Analyzes and improves prompts
Strategist Agent - Recommends optimal configuration
Users select what matters most:
| Priority | Focus | Models | Est. Cost | Est. Time |
|---|---|---|---|---|
| Speed | Fast responses | Gemini Flash, GPT-4o Mini, Claude Haiku | ~$0.02-0.10 | ~10-30s |
| Cost | Budget-conscious | DeepSeek, Qwen Flash, Ministral | ~$0.02-0.05 | ~20-40s |
| Depth | Comprehensive | Claude Opus, GPT-5, DeepSeek Reasoner | ~$0.20-0.50 | ~45-90s |
| Balanced | Optimal mix | Claude Sonnet, GPT-4o, Gemini Pro | ~$0.08-0.15 | ~30-50s |
Pre-configured setups for common use cases:
The AI Assistant tracks usage patterns to improve recommendations:
Personalization: Learns from your accept/reject patterns to improve future suggestions.
Here's an uncomfortable truth: we've been building ensemble strategies based on intuition and user feedback. But how do we know they're better than single models? How do we know one strategy outperforms another for specific tasks?
This matters more for ensemble systems than single models. When you orchestrate multiple LLMs through complex workflows—debates, hierarchical refinement, adversarial testing—you're introducing layers of complexity. Each layer can amplify quality improvements or compound errors. Without rigorous evaluation, you're flying blind.
The stakes are higher because:
We've already released some foundational pieces:
But there's much more to build: comprehensive benchmarks, automated regression testing, cross-strategy performance comparisons, and diversity-quality correlation analysis. It's ongoing work, and we're learning as much from failures as successes.
Bottom line: Ensemble AI without rigorous evals is just expensive guesswork. This is probably the most important unsolved problem in the space.
Tier 1: Individual Model Evaluation
Tier 2: Ensemble Strategy Evaluation
Tier 3: System-Level Evaluation
Each ensemble strategy has custom evaluation criteria:
Competitive Refinement:
Collaborative Synthesis:
Expert Panel:
Debate Tournament:
Hierarchical:
Chain-of-Thought:
Red Team / Blue Team:
Diversity-Quality Correlation:
Anti-Collusion Tests:
Mode Collapse Detection:
Routing Accuracy:
Things that seem to be working:
Things we're still figuring out:
Looking at all three approaches, some interesting patterns emerge. This isn't about declaring a "winner"—each optimizes for different things:
| Dimension | LLM Council (Karpathy) | Deep Research (Nadella) | AI Crucible |
|---|---|---|---|
| Strategies | 1 (Peer Review) | 3 (Council, DXO, Ensemble) | 7 (Competitive, Collaborative, Expert Panel, Debate, Red Team, Hierarchical, Chain-of-Thought) |
| Workflow | Linear (Query → Review → Synthesis) | Iterative deliberation | Strategy-dependent |
| Anonymization | Yes (during peer review) | Yes (Ensemble mode) | Optional per strategy |
| Role Assignment | Chairman only | DXO framework | Expert Panel, Hierarchical, Red Team |
| Debate | No (only peer review) | Yes (Council mode) | Yes (Debate Tournament) |
| Adversarial | No | No | Yes (Red Team / Blue Team) |
| Dimension | LLM Council | Deep Research | AI Crucible |
|---|---|---|---|
| Setup Complexity | Medium (Python + npm) | Unknown (proprietary) | Low (web-based) |
| Model Selection | Manual config file | "Auto" selector | AI Assistant recommendations |
| Prompt Engineering | Manual | Unknown | AI-assisted enhancement |
| Cost Estimation | No | Token budgeting | Yes (before run) |
| One-Click Config | No | Unknown | Yes (Model Packs) |
| User Learning | No | Unknown | Yes (personalized recommendations) |
| Dimension | LLM Council | Deep Research | AI Crucible |
|---|---|---|---|
| Live Streaming | No | Yes | Optional |
| Individual Responses | Yes | Unknown | Yes |
| Peer Reviews | Yes | Unknown | Strategy-dependent |
| Reasoning Traces | Limited | Yes | Yes (especially Chain-of-Thought) |
| Evaluation Scores | No | Unknown | Yes (LLM-as-Judge) |
| Analytics | No | Unknown | Yes (AI Assistant) |
| Dimension | LLM Council | Deep Research | AI Crucible |
|---|---|---|---|
| Automated Evals | No | Unknown | Yes (comprehensive framework) |
| Strategy Comparison | N/A | Unknown | Yes (cross-strategy benchmarks) |
| Regression Testing | No | Unknown | Yes (planned) |
| Quality Gates | No | Unknown | Yes (Hierarchical) |
| Confidence Scoring | No | Unknown | Yes (multiple strategies) |
| Dimension | LLM Council | Deep Research | AI Crucible |
|---|---|---|---|
| Open Source | ✅ Yes | ❌ No | ✅ Yes (platform) |
| Custom Strategies | Limited | ❌ No | ✅ Yes (7 built-in, extensible) |
| Custom Models | ✅ Yes (OpenRouter) | Limited (Microsoft stack) | ✅ Yes (13+ models, any provider) |
| API Access | ✅ Yes | Unknown | ✅ Yes |
| Self-Hosted | ✅ Yes | ❌ No | ✅ Yes (Firebase-based) |
| Dimension | LLM Council | Deep Research | AI Crucible |
|---|---|---|---|
| Deployment | Local/self-hosted | Azure cloud | Firebase/web |
| Scalability | Manual | Enterprise-grade | Cloud-based |
| Enterprise Features | Limited | ✅ Yes (Copilot integration planned) | Partial |
| SLA/Support | Community | Microsoft enterprise | Community |
| Cost Tracking | No | Yes | Yes |
Top feature: The elegance. One clean pattern that works for most cases. No configuration paralysis.
✅ I want to understand ensemble basics without complexity ✅ I need peer review but don't need specialized workflows ✅ I'm building my own tool and want clean code to learn from ✅ I value simplicity over specialized features
When it might not fit:
Best for: Developers learning ensemble patterns, research projects, simple integration into existing tools
Top feature: The practicality. He built it for real work and immediately started using it. That's the test.
✅ I'm already invested in the Microsoft ecosystem ✅ I need production-grade reliability and support ✅ I want to watch deliberation happen (the streaming is brilliant) ✅ I need role-based workflows (DXO is clever)
When it might not fit:
Best for: Microsoft shops, enterprises needing supported solutions, teams who value live transparency
Full disclosure: I'm building it, so take this with appropriate skepticism.
✅ I have diverse tasks needing different collaboration patterns ✅ I'm overwhelmed by configuration and need the AI Assiatant's help ✅ I want to experiment with different strategies ✅ I'm not locked to one provider (OpenAI, Anthropic, etc.) ✅ I'm willing to trade simplicity for flexibility
When it might not fit:
Watching Karpathy, Nadella, and building AI Crucible, I've realized we're all circling the same core insight: metacognition.
Metacognitive AI systems think about their thinking:
Single-model AI is like asking one expert for advice. Metacognitive ensemble AI is like convening a panel of experts who deliberate before answering.
The difference:
| Single Model | Ensemble (Metacognitive) |
|---|---|
| One perspective | Multiple perspectives synthesized |
| Hidden biases | Cross-validated insights |
| Opaque reasoning | Transparent deliberation |
| Static response | Iterative refinement |
| No self-correction | Peer review and debate |
| Confidence uncalibrated | Explicit uncertainty quantification |
Nadella highlighted where this matters most:
Healthcare: Multi-specialist consultation for diagnosis Finance: Risk assessment with diverse analytical approaches Legal: Case analysis from prosecution and defense perspectives
Supply Chain: Scenario planning with distributed intelligence Research: Literature synthesis with critical peer review Security: Adversarial testing (red team / blue team)
In these domains, single-model errors are unacceptable. Ensemble deliberation provides safety through redundancy and diversity.
Everyone's making predictions about where ensemble AI is headed, so why not me as well? Here's what I think we'll see:
1. Standardization Efforts
Expect emergence of:
2. Model Context Protocol (MCP) Adoption
Both Nadella's Ensemble framework and AI Crucible can leverage MCP:
3. Specialized Ensemble Models
Model providers may release:
4. Software Coding Integration
IDEs are already experimenting with multi-model approaches:
The gap between "here are 3 solutions" and "here's the synthesized best solution" is the opportunity.
1. Automated Strategy Selection
AI Crucible's AI Assistant approach will inspire:
2. Enterprise Integration
Nadella's Copilot vision will materialize:
3. Cost Optimization
As usage scales, expect:
1. Recursive Self-Improvement
Ensemble systems that:
2. Human-AI Hybrid Ensembles
Integration of human experts into ensemble workflows:
3. Specialized Vertical Solutions
Domain-specific ensemble systems:
Start simple, scale complexity:
Optimize for your needs:
Integrate ensemble thinking into product:
Architecture patterns:
// Simple ensemble wrapper
async function ensembleQuery(
prompt: string,
strategy: 'council' | 'debate' | 'synthesis' = 'council'
): Promise<EnsembleResult> {
switch (strategy) {
case 'council':
return runCouncil(prompt);
case 'debate':
return runDebate(prompt);
case 'synthesis':
return runSynthesis(prompt);
}
}
// With automatic strategy selection (AI Crucible-style)
async function smartEnsemble(prompt: string): Promise<EnsembleResult> {
const classification = await classifyPrompt(prompt);
const strategy = selectOptimalStrategy(classification);
const models = selectModels(strategy, { priority: 'balanced' });
return runEnsemble(prompt, strategy, models);
}
Strategic considerations:
Integration roadmap:
The ensemble AI revolution is just beginning. As these systems mature:
The question isn't whether ensemble AI will replace single-model AI—it's how quickly.
Karpathy's LLM Council
AI Crucible
Nadella's Deep Research
The ensemble AI revolution is happening. I'm excited to be building AI Crucible as my contribution to this space, but I'm equally excited to see what Karpathy, Nadella, and others will build next. We're all learning together.
If you want to experiment with what we're building: Try AI Crucible
If you want to learn from elegant simplicity: Check out Karpathy's LLM Council
And watch for Microsoft to bring Nadella's vision to Copilot—that's when ensemble AI will truly go mainstream.