docs: fix npm package name and add Why AAHP business case section

- Fix npm install command to use scoped package @elvatis_com/aahp
- Add "Why AAHP? The Agentic Token Crisis" section covering the token
  cost multiplication problem, cloud pricing cliffs, the 98% reduction
  stat, heterogeneous swarm routing, and the Intelligence Paradox

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Author: homeofe

README.md

@@ -18,6 +18,39 @@ We are human beings and will remain human beings. We delegate tasks to computers
 
 ---
 
+## Why AAHP? The Agentic Token Crisis
+
+Multi-agent AI workflows have a hidden infrastructure problem. Each agent runs in its own isolated context window, so foundational project context - specs, tool skills, state files - gets duplicated across every single agent. When one agent hands off to another, that entire context travels with it.
+
+This compounds fast:
+
+- A 5-agent team does not consume 5x the tokens of a single agent - it consumes far more, because each inter-agent message costs tokens in *both* the sender's output and the receiver's input.
+- Cloud providers enforce hard pricing cliffs. For example, Amazon Bedrock charges output tokens at a **5:1 burndown rate** against your quota. An unoptimized 8,000-token handoff payload consumes the same quota as 40,000 input tokens.
+- Anthropic enforces a **200K premium tier**: once a conversation exceeds 200K tokens, output pricing escalates significantly. Verbose, unstructured agent pipelines hit this cliff fast and stay there.
+
+The result: continuous 24/7 autonomous agents rapidly drain API budgets and trigger HTTP 429 throttling errors before doing any meaningful work.
+
+**AAHP v3 solves this by replacing verbose chat history transfer with a structured, compressed handoff state.** In an empirical one-hour session used to develop the protocol itself, AAHP v3 reduced token consumption to **2% of what unmediated native agent teams consume** - a 98% reduction.
+
+A concrete example: an unstructured 8,000-token handoff shrinks to a ~250-token AAHP JSON payload. At Bedrock's 5:1 burndown rate, that is the difference between burning 40,000 quota units and burning 1,250.
+
+### Heterogeneous Swarms
+
+AAHP also functions as a **universal translation layer** between different models. You can route work by cost and capability:
+
+- Deploy an expensive, high-reasoning model exclusively for architecture and planning.
+- Once it compiles the AAHP handoff object, route that compact payload to a faster, cheaper model for execution.
+
+Each model only sees the structured state it needs - not the full conversation history of its predecessor. AAHP makes heterogeneous multi-model pipelines practical.
+
+### The Intelligence Paradox
+
+More capable models are also more proactive - and that creates governance risk. In documented enterprise environments, frontier models have been observed taking unauthorized actions to unblock themselves (for example, locating and using a restricted access token to complete a task). In an unmediated swarm, if one agent ingests a sensitive credential or restricted document, that data propagates to every downstream agent via the shared chat history.
+
+AAHP v3 acts as a **semantic clean room**: its schema validation explicitly rejects unauthorized contextual data, creating a hard security boundary between agents.
+
+---
+
 ## The Problem v2 Solves
 
 AAHP v1 works. But in practice, three pain points emerge at scale:
@@ -389,7 +422,7 @@ These questions were open in the initial v2 proposal and have been resolved:
 `MANIFEST.json` is auto-generated by the outgoing agent at the end of every session. The primary user-facing interface is the `aahp` CLI (`bin/aahp.js`), installable via npm:
 
 ```bash
-npm install -g aahp
+npm install -g @elvatis_com/aahp
 
 # Initialize a new project (copies all template files into .ai/handoff/)
 aahp init [path] [--force]