Enhance home page with multi-agent system capabilities and emergent properties

- Transform positioning from simple workflow engine to multi-layered orchestration platform
- Add compelling real-world use cases: research networks, enterprise development, education
- Explain emergent intelligence patterns and collective behavior examples
- Include detailed examples of self-improving agent networks and living ecosystems
- Clarify technical capabilities: dynamic spawning, cross-agent memory, adaptive workflows

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: mfittko

docs/index.md

@@ -14,20 +14,63 @@ description: Open source workflow engine enabling agentic IDEs to execute sophis
 
 ## 🚀 What is Agents CLI?
 
-Agents CLI enables agentic IDEs (Cursor, VS Code, etc.) to dynamically create and execute sophisticated multi-agent workflows. Define agents, tools, and orchestration patterns through JSON configuration and receive structured outputs optimized for IDE consumption.
+Agents CLI transforms your IDE into a **multi-layered multi-agent orchestration platform**. Beyond simple AI assistance, it enables the creation of complex agent ecosystems where specialized AI agents collaborate, coordinate, and evolve together to solve sophisticated problems.
+
+**Think of it as turning your development environment into a living, breathing AI organization** - where each agent has specific expertise, agents can dynamically spawn other agents, and the collective intelligence emerges from their interactions.
 
 ## 📋 Current Status: Phase 1 Development
 
 We're currently implementing the foundation and core MCP functionality.
 [View detailed progress →](contributing/phase-roadmap)
 
-## 🎯 Key Features
+## 🧠 Multi-Agent System Capabilities
+
+### 🏗️ **Hierarchical Agent Networks**
+Create layered agent architectures where manager agents coordinate specialist teams, enabling complex problem decomposition and parallel processing.
+
+### 🔄 **Emergent Intelligence Patterns**
+- **Peer-to-peer collaboration**: Agents that debate, fact-check, and improve each other's work
+- **Swarm intelligence**: Multiple agents working on different aspects simultaneously
+- **Self-organizing workflows**: Agents that dynamically restructure based on task complexity
+- **Collective learning**: Agent networks that improve through shared experiences
+
+### 🌐 **Real-World Applications**
+
+**🔍 Research & Analysis Network**
+```
+Research Coordinator → [Web Researcher, Academic Researcher, Market Analyst]
+                    ↓
+Data Synthesizer → [Fact Checker, Citation Validator, Summary Generator]
+                ↓
+Report Generator → [Technical Writer, Visual Designer, Quality Reviewer]
+```
+
+**🏢 Enterprise Software Development**
+```
+Project Manager Agent → [Requirements Analyst, Solution Architect]
+                     ↓
+Development Orchestrator → [Backend Dev, Frontend Dev, DevOps, Tester]
+                        ↓
+Code Quality Network → [Security Auditor, Performance Optimizer, Documentation Generator]
+```
+
+**🎓 Educational Content Creation**
+```
+Learning Objectives Designer → [Subject Matter Expert, Pedagogical Specialist]
+                            ↓
+Content Production Team → [Writer, Interactive Designer, Assessment Creator]
+                       ↓
+Quality Assurance Network → [Accessibility Checker, Learning Effectiveness Validator]
+```
+
+## 🎯 Core Technical Features
 
-- **Dual Interface**: CLI and MCP server modes for flexible integration
-- **Configuration-Driven**: JSON-based agent and workflow definitions
-- **Security-First**: Built-in sandboxing and access controls
-- **IDE-Optimized**: Real-time streaming and structured outputs
-- **Extensible**: Plugin architecture for custom tools and workflows
+- **Multi-Layer Orchestration**: Hierarchical and peer-to-peer agent coordination
+- **Dynamic Agent Spawning**: Agents can create specialized sub-agents on demand
+- **Emergent Behavior Monitoring**: Track how agent interactions create unexpected solutions
+- **Cross-Agent Memory**: Shared knowledge bases and learning from collective experiences
+- **Adaptive Workflows**: Self-modifying processes based on success patterns
+- **Real-time Collaboration**: Live agent-to-agent communication and handoffs
 
 ## 🔗 Quick Links
 
@@ -37,17 +80,57 @@ We're currently implementing the foundation and core MCP functionality.
 - [Example Workflows](examples/)
 - [Contributing Guide](contributing/development-setup)
 
-## 🏗️ Example Usage
+## ⚡ Emergent Properties in Action
+
+When multiple specialized agents interact, something remarkable happens - **emergent intelligence** that exceeds the sum of its parts:
+
+### 🌟 **Example: Self-Improving Code Review Network**
+
+```bash
+# Simple command that triggers complex multi-agent behavior
+agents-cli run --config networks/code-review-network.json \
+  --input "Optimize this codebase for production"
+```
+
+**What happens behind the scenes:**
+1. **Analysis Agent** identifies performance bottlenecks
+2. **Security Agent** discovers potential vulnerabilities
+3. **Architecture Agent** suggests structural improvements
+4. **Learning Agent** notices patterns from previous reviews
+5. **Coordinator Agent** synthesizes insights and creates optimization plan
+6. **Implementation Agents** execute changes in parallel
+7. **Validation Network** tests, benchmarks, and validates changes
+
+**The Emergent Magic:** The network discovers optimization strategies that no single agent would have found, learns from each review to improve future performance, and adapts its approach based on codebase characteristics.
+
+### 🚀 **Advanced Multi-Layer Example**
 
 ```bash
-# Execute a code review workflow
-agents-cli run \
-  --config examples/code-review.json \
+# Deploy a complete AI-powered development team
+agents-cli network deploy --config networks/dev-team.json
+agents-cli network scale --agents 50 --auto-spawn
+```
+
+This creates a **living development ecosystem** where:
+- Agents form temporary collaboration groups for specific features
+- Senior agents mentor junior agents, improving the overall skill level
+- The network self-organizes based on project complexity and deadlines
+- Emergent patterns like "design patterns" evolve naturally from agent interactions
+- The system develops its own "culture" and coding standards through collective behavior
+
+## 🏗️ Quick Start Examples
+
+```bash
+# Simple single-agent task
+agents-cli run --config examples/code-review.json \
   --input "Review this pull request" \
   --files "src/**/*.ts"
 
-# Start MCP server for IDE integration
-agents-cli serve --port 3000
+# Multi-agent network orchestration
+agents-cli network start --config networks/research-collective.json
+
+# IDE integration with real-time agent collaboration
+agents-cli serve --port 3000 --enable-network-mode
 ```
 
 ## 🤝 Contributing