By lunchtime I have already bounced between Amazon Q, Claude, Gemini, and a local model. Each gives a slightly different answer. The context switching is real, and the contradictions add up. That is the tax of AI tool fragmentation.
TL;DR
- The AI tool ecosystem is fragmented, and swapping between tools slows delivery.
- This creates context switching, inconsistent quality, and single-model bias.
- I built AI Consensus CLI, an open-source Rust CLI that queries multiple tools in parallel and synthesizes a single consensus response.
- The tool is configurable via TOML, uses async execution with Tokio, and handles unavailable tools gracefully.
The problem: AI tool fragmentation
The proliferation of specialized AI models is a blessing and a complexity tax. In day-to-day engineering work, this often looks like:
- Amazon Q for AWS-specific questions
- Claude for complex reasoning tasks
- Gemini for creative problem-solving
- Local models (Ollama) for privacy-sensitive work
The downside is a fragmented workflow where engineers must manually switch contexts across tools.
That manual orchestration is slow and error-prone. You end up trusting a single-model perspective on critical reasoning tasks, which becomes a single point of failure.
| Geek Corner | |:--| | Consensus is not just averaging. It is about surfacing agreement, disagreement, and the one model that saw the edge case everyone else missed. |
The solution: AI Consensus CLI
To address this, I built AI Consensus CLI (Rust, open source). As a London-based engineering leader, I wanted a tool that makes multi-model reasoning fast, reliable, and repeatable. It acts as an aggregation and synthesis layer:
- It runs multiple solver LLMs in parallel with the same prompt.
- It feeds all outputs into a consensus engine that synthesizes agreements, disagreements, and unique insights.
- It uses a TOML-based configuration so you can add new tools without recompiling.
- It is async-first with Rust and Tokio for performance and reliability.
In short: you get the speed of parallel execution, the diversity of multiple models, and a single actionable answer.
How it works in practice
# Get insights from multiple AIs with consensus
ai-co -s q,gemini,ollama -c claude -p "Design a microservices architecture for e-commerce"
# Compare different perspectives
ai-co -s q,claude,gemini -c q -p "Optimize database performance for high-traffic applications"
The CLI executes your query across all selected solver models, then passes the responses to a consensus model that synthesizes the result.
Technical architecture (Rust + Tokio)
The CLI is built in Rust for performance and predictable concurrency:
- Async/Await: Parallel tool execution with Tokio
- Error resilience: Graceful handling of unavailable tools
- Extensible config: TOML definitions for each model provider
- Cross-platform: Works on Linux and macOS
The modular design keeps the orchestration layer stable while allowing new AI tools to be plugged in quickly.
Real-world applications
This approach is useful in any scenario where multiple perspectives matter:
- Architecture decisions: Compare design tradeoffs across models
- Code reviews: Get multiple viewpoints on correctness and maintainability
- Problem solving: Blend different reasoning styles into one answer
- Research and discovery: See how different models frame the same topic
Key benefits
- Diverse perspectives: Each model brings unique training and reasoning
- Time efficiency: Multiple answers in one round-trip
- Bias reduction: Consensus helps surface blind spots in single-model answers
- Quality assurance: Cross-validation improves reliability
Adoption checklist (practical tips)
If you want to operationalize multi-model consensus in a team setting, these patterns help:
- Standardize prompts so solvers see consistent input
- Define consensus criteria (agreement, novelty, risk) before you scale usage
- Track cost and latency to avoid tool sprawl
- Separate sensitive data paths for privacy and compliance
- Store reasoning trails so decisions are auditable
Open source and community-driven
The project is open source and available on GitHub:
Repository: https://github.com/ShubhenduVaid/ai-consensus-cli
The goal is to build a community around multi-AI orchestration and consensus building.
Looking forward
Potential enhancements include:
- Web interface for non-technical users
- Integration with additional AI providers
- Advanced consensus algorithms
- Team collaboration workflows
- API access for programmatic use
The bigger picture
As AI tools continue to multiply, orchestration and synthesis become a competitive advantage. A consensus-first workflow lets teams leverage the strengths of multiple models instead of being locked into a single ecosystem.
AI Consensus CLI is a step toward that future: collaborative AI systems that deliver richer, more reliable insights.
Try it out: The CLI is available now on GitHub with installation instructions and examples.
What is your experience with multiple AI tools? Have you wished for a way to get diverse AI perspectives quickly? I would love to hear your use cases.
Keywords: AI orchestration, consensus CLI, multi-LLM workflows, Rust, Tokio, Amazon Q, Claude, Gemini, Ollama, open source, software architecture, engineering leadership.
Tags: #AI #MachineLearning #Rust #OpenSource #CLI #Consensus #ArtificialIntelligence #SoftwareDevelopment #Innovation