AutoCouncil Addendum

Scaling Constraints

Token economics, capability dimensions, and the dissolution thesis. How the council scales \u2014 and when it won't need to.

Token Economics

Flat Pool

26,000 tokens/run

All agents receive full context. Linear token growth with agent count.

prompt_tokens × num_agents = total_input_tokens

Scaling Problem

At 12 agents with 4K context each = 48K input tokens per run. Cost compounds with turn count. Hierarchical routing reduces this by 97%.

Capability Dimensions

Agents mapped to capability dimensions. The orchestrator routes tasks based on dimensional fit, not round-robin distribution.

Reasoning Depth

ClaudeDeepSeekGPT-4

Multi-step logical chains, mathematical proof, complex argument structure

Breadth / Coverage

GeminiPerplexityMeta AI

Wide-ranging knowledge, cross-domain connections, comprehensive surveys

Code Generation

GPT-4DeepSeekCopilot

Working code production, debugging, architecture design, testing

Creative / Divergent

GrokClaudeLeChat

Unconventional angles, metaphor, reframing, lateral thinking

Research / Citation

PerplexityGemini

Real-time data, source attribution, fact verification

Adversarial / Red Team

GreyClayton

Challenge assumptions, stress-test invariants, find failure modes

THE DISSOLUTION THESIS

“Agents will inherently start to get there as well.”

As foundation models converge in capability, the value of multi-agent orchestration shifts from capability diversity to perspective diversity. The council doesn't need 12 agents because they're different \u2014 it needs them because convergence from independent sources is the strongest validation signal available. When models become interchangeable, the architecture still works. The finding is in the agreement, not the disagreement.