Agentic Architecture Performance and Cost Benchmarking

The first wave of enterprise AI was driven by enthusiasm. The second is being shaped entirely by cost. Token-based tools carry a structural paradox: the more useful they become, the more your team uses them and the higher the bill grows. AI is no longer an isolated IT experiment. It is fast becoming a massive operating expense shared across the entire corporate P&L.

Most enterprises have been trapped in Tokenmaxxing, where bigger compute bills are treated as evidence of progress, yet no one can answer the basic question: What did we actually deliver?

This benchmarking report introduces a fundamentally different approach: Outcomemaxxing, flipping the core question from how many tokens were burned to how much useful work the AI actually executed, at what cost.

This whitepaper covers:

• A side-by-side structural analysis of Federated Model Context Protocol (MCP) vs. MIKI

• Why token-based architectures create an unsustainable OpEx spiral at enterprise scale

• How MIKI's Unified Context Graph delivers 57% fewer tokens per query versus MCP agents

• A full cost comparison across Claude Opus, GPT-5.5, GPT-5.4, Gemini Pro, Glean, and MIKI

• How MIKI delivers 95%+ lower inference spend, potentially saving $500K–$1.2M/year for a 100-rep organization