Multi-Model Implementation with Agent SDK
From Bias Divergence Testing to Decision Frameworks
Leverages differences in LLM tuning biases to improve accuracy through multi-model combinations.
Covers the theory of independence based on Condorcet's jury theorem, experimental data showing 46% divergence rate across three vendors' models, implementation code for Agent SDK / MCP / A2A, and a decision framework using the TPC (Tokens Per Correctness) metric for cost-effectiveness analysis.
Evaluates four dialogue patterns — debate, red-team, cross-verification, and routing — against their failure modes including sycophancy, hidden profiles, and diminishing returns. Provides criteria for when to use multi-agent and when not to.
Contents
- Part 1: Theory — Why Dialogue?
- 1. Why Multi-Agent — The Three Virtues
- 2. The Three-Layer Protocol Stack
- 3. Dialogue Patterns and Failure Modes
- Part 2: Implementation — Building with Three Layers
- 4. Multi-Agent Construction with Agent SDK
- 5. Bias Diversity and Cross-Model Design
- 6. Cross-Ecosystem Integration with A2A
- Part 3: Judgment — When to Use and When Not To
- 7. The Decision Framework
- 8. When Not to Use It