Trinity-Synthesis: A Multi-Agent Architecture for AI Agents That Think Before They Speak Ever felt your AI agent is "shooting from the hip"? It latches onto a single line of thought and fails to produce a robust, well-rounded plan. This is a common struggle I've called the "AI Reasoning Paradox."
To tackle this, I developed Trinity-Synthesis, a multi-agent architecture designed to force reflection and synthesis before delivering a final answer. The philosophy is simple: constructive conflict between different perspectives leads to better solutions.
Here’s the core idea:
Instead of one agent, it uses four agents running on the same base model but with different "personalities" defined by their system prompts and temperature settings:
🧠 The Visionary: Thinks outside the box (high temp: 1.0). 📊 The Analyst: Focuses on logic, data, and structure (low temp: 0.3). 🛠️ The Pragmatist: Evaluates feasibility, costs, and risks (mid temp: 0.5). These three "thinkers" work in parallel on the same problem. Then, a final Synthesizer agent critically analyzes their outputs, rejects flawed arguments, and integrates the best points into a single, coherent, and often superior strategy.
The result is a more robust reasoning process that balances creativity with analytical rigor, making it ideal for solving complex, strategic problems where answer quality is critical.
I've written a deep dive on how it works, including a detailed case study ("The Helios Initiative") and the Python source code for you to experiment with.
Thought Simulation: A simple 2-step prompt architecture that nearly doubles LLM reasoning success rate. Hi everyone,
I'd like to share the results of my experiment with a zero-shot prompting method I call "Thought Simulation." It's a simple, training-free way to improve an LLM's ability to solve complex logical problems.
The method is a two-step process:
Analytical Step (temp=0.2): The model first acts as a cold analyst to break down the problem.
Synthesis Step (temp=0.7): The model then uses this analysis to formulate a final, comprehensive answer.
The Results: In a series of 17 challenging reasoning tasks performed on the latest Claude 4 Sonnet model (claude-sonnet-4-20250514), the results were striking:
Standard Prompting (Baseline): ~35% success rate
Thought Simulation (My Method): ~65% success rate
This simple architecture nearly doubled the model's effectiveness. Crucially, it unlocks answers to questions previously unsolvable by standard prompting, allowing the model to spot flawed premises or demonstrate deeper, qualitative reasoning.
The method is also universal, with similar effectiveness boosts observed on smaller, locally-run models (like the Polish Bielik family). This structured 'Analyst-Synthesist' approach seems more robust for certain tasks than a standard CoT monologue.
I've detailed the full experiment in the articles below. I'd be grateful for your feedback!
