Enhance graph creation and management features

- Increased PLANNER_MAX_TOKENS to 2000 to accommodate complex graph structures.
- Improved the HIDDEN_DOMAIN_APPENDIX for better clarity on graph components and policies.
- Added new tasks for creating subgraphs and defining connections in the task and goal queues.
- Enhanced error handling and acknowledgment processes in various queues for better tracking of graph-related tasks.
- Introduced a fuzzy deduplication helper in role runners to improve string similarity calculations.

Author: grantiguess

AGENTIC_ARCHITECTURE.md

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+# Agentic Architecture & Context Management
+
+## What We Fixed
+
+### 1. ✅ Connection Naming (word_word → Title Case)
+**Problem**: LLM was outputting `"in_relationship"`, `"close_friends_with"`, `"mother_son"` (snake_case)  
+**Fix**: Added explicit CONNECTION NAMING RULES to prompt:
+```
+✅ Good: "Romantic Partnership", "Inner Circle Bond", "Coaching Relationship"
+❌ Bad: "romantic_partnership", "inner_circle_bond", "coaching_relationship"
+```
+
+### 2. ✅ Active Graph Context
+**Problem**: User said "add more" in "Swift-Kelce Network", but Wizard added to "NC State University" graph  
+**Fix**: Added CRITICAL instruction to `create_node` intent:
+```
+CRITICAL: You MUST set "graph": {"name": "{EXACT active graph name from CURRENT GRAPH context}"} - DO NOT invent a different graph name!
+```
+
+### 3. ✅ Delete Operations
+**Verified**: Delete is working correctly. Log shows: `[Executor] delete_node_instance: Deleting instance inst-1763261052499-5-xarla9 from graph graph-1763261052233-uc9dif`
+
+---
+
+## Your Bigger Question: Abstraction & Token Management
+
+> "we need the executor to build up agentic flows essentially right? are we achieving a level of abstraction with this and providing token limits for each to the one actually generating the responses that get turned into networks?"
+
+### Yes! Here's What We're Achieving:
+
+## 1. **Separation of Concerns (Like Cursor's Agent System)**
+
+| Layer | Role | Token Budget | LLM Involvement |
+|-------|------|--------------|-----------------|
+| **Planner** | Decides WHAT to do | 2000 tokens | ✅ LLM call |
+| **Executor** | Generates operations | 0 tokens | ❌ Deterministic |
+| **Auditor** | Validates ops | 0 tokens | ❌ Rule-based |
+| **Committer** | Applies to UI | 0 tokens | ❌ Deterministic |
+| **Continuation** | Decides NEXT step | 1500 tokens | ✅ LLM call (agentic loop) |
+
+**Key Insight**: Only 2 LLM calls per iteration (Planner + Continuation), rest is deterministic graph operations.
+
+## 2. **Abstraction Levels**
+
+### Level 1: User Intent (Natural Language)
+```
+User: "add more to the active graph"
+```
+
+### Level 2: Semantic Plan (LLM Output)
+```json
+{
+  "intent": "create_node",
+  "graph": {"name": "Swift-Kelce Network"},
+  "graphSpec": {
+    "nodes": [{"name": "Jason Kelce", ...}],
+    "edges": [{"source": "Jason Kelce", "target": "Travis Kelce", "type": "siblings"}]
+  }
+}
+```
+
+### Level 3: Operations (Executor Output)
+```js
+[
+  { type: 'addNodePrototype', prototypeData: {...} },
+  { type: 'addNodeInstance', graphId: '...', prototypeId: '...', position: {x,y} },
+  { type: 'addEdge', edgeData: {...} }
+]
+```
+
+### Level 4: UI Mutations (Committer Output)
+```js
+graphStore.applyMutations([...ops])
+// Updates React state, triggers re-render
+```
+
+**This is true abstraction**: Each layer only knows about its immediate inputs/outputs, not the full pipeline.
+
+## 3. **Token Budget Per Role**
+
+### Current Configuration:
+```js
+// PLANNER (initial request)
+const PLANNER_MAX_TOKENS = 2000;  // Enough for ~10 nodes + edges + connection defs
+
+// CONTINUATION (agentic loop)
+const CONTINUE_MAX_TOKENS = 1500;  // Smaller batches for iterative building
+
+// EXECUTOR (no LLM)
+// Just runs algorithms: fuzzy matching, layout, operation generation
+
+// AUDITOR (no LLM)
+// Schema validation, reference checks
+
+// COMMITTER (no LLM)
+// Applies operations, triggers UI updates
+```
+
+### Why This Works:
+- **Planner** gets the full context (conversation, graph state, colors) → 2000 tokens
+- **Continuation** gets simplified context (just node names, counts) → 1500 tokens
+- **Executor** runs deterministic code (no token cost)
+- Total: ~3500 tokens output per iteration (vs 10,000+ for monolithic approaches)
+
+## 4. **Agentic Flows We're Building**
+
+### Flow 1: Create Graph (Single Iteration)
+```
+User: "make a graph of X"
+  ↓
+Planner: { intent: "create_graph", graphSpec: {...} }
+  ↓
+Executor: Generate ops (nodes, edges, layout)
+  ↓
+Auditor: Validate
+  ↓
+Committer: Apply + Check if agenticLoop=true
+  ↓
+Continuation: LLM decides "continue" or "complete"
+  ↓ (if continue)
+Planner: { intent: "create_node", graphSpec: {...} }
+  ↓
+[Loop repeats up to 5x]
+```
+
+### Flow 2: Add to Graph (Context-Aware)
+```
+User: "add more" [in active graph]
+  ↓
+Planner: Gets active graph context
+  |  🎯 CURRENT GRAPH: "Swift-Kelce Network"
+  |  Status: 10 nodes, 12 edges
+  |  Example concepts: Taylor Swift, Travis Kelce, Selena Gomez...
+  ↓
+LLM: MUST use "graph": {"name": "Swift-Kelce Network"}
+  ↓
+Executor: Fuzzy dedup + link to existing nodes
+  ↓
+Committer: Apply + trigger continuation
+```
+
+### Flow 3: Delete (CRUD Operation)
+```
+User: "take Blake Lively out"
+  ↓
+Planner: { intent: "delete_node", delete: { target: "Blake Lively" } }
+  ↓
+Executor: Find instance by name → generate deleteNodeInstance op
+  ↓
+Auditor: Validate instance exists
+  ↓
+Committer: Apply deletion
+  ↓
+No continuation (delete is final)
+```
+
+## 5. **Context Management (Like Cursor's @-mentions)**
+
+### What We Have:
+```js
+// Active graph context (injected into every prompt)
+🎯 CURRENT GRAPH: "Swift-Kelce Network"
+Status: 10 nodes, 12 edges
+Example concepts: Taylor Swift, Travis Kelce, Selena Gomez...
+
+// Conversation history (last 10 messages)
+📝 RECENT CONVERSATION:
+User: make a new graph for Taylor Swift
+You: I'll weave a fresh "Swift-Kelce Network"...
+User: add more
+You: I'll expand with 4 more associates...
+
+// Color palette (extracted from existing nodes)
+🎨 AVAILABLE COLORS: #8b0045, #00458b, #8b0000, ...
+```
+
+### What We Could Add (Cursor-style):
+- **@graphs**: Explicit graph references (`@Swift-Kelce-Network`)
+- **@nodes**: Reference specific nodes (`@Taylor-Swift`)
+- **@definitions**: Reference connection types (`@Romantic-Partnership`)
+- **Implicit context switching**: "open the Solar System graph" → switches active graph
+
+### Implementation Path:
+1. **Parse @-mentions** in user message
+2. **Inject as explicit context** in prompt
+3. **Track context stack** (active graph, selected nodes, etc.)
+4. **Auto-detect context switches** (e.g., "in the X graph" changes active graph)
+
+## 6. **Token Efficiency: Before vs After**
+
+### Before (Monolithic)
+```
+User: "make a Taylor Swift graph with associates"
+  ↓
+1 LLM call: Generate 20 nodes at once
+  ↓
+Token usage: 5000+ tokens
+  ↓
+Result: Truncated at 1200 tokens, incomplete JSON
+```
+
+### After (Agentic Batching)
+```
+User: "make a Taylor Swift graph with associates"
+  ↓
+Iteration 0: 6 nodes (2000 tokens) ✅
+  ↓
+Continuation: "I need to add their associates" (800 tokens) ✅
+  ↓
+Iteration 1: 4 nodes (1500 tokens) ✅
+  ↓
+Continuation: "Graph sufficiently populated" (200 tokens) ✅
+  ↓
+Total: 4500 tokens across 4 calls
+Result: Complete graph, no truncation
+```
+
+**Efficiency Gain**: 4500 tokens (usable) vs 5000 tokens (truncated)
+
+## 7. **Are We Achieving True Abstraction?**
+
+### ✅ Yes, in these ways:
+
+1. **Semantic → Spatial Separation**
+   - LLM outputs: node names, relationships, colors
+   - Executor adds: x/y coordinates via layout algorithms
+   - LLM never sees spatial data (solves original bottleneck!)
+
+2. **Plan → Execute Separation**
+   - Planner outputs: high-level intent + graphSpec
+   - Executor outputs: low-level operations
+   - No mixing of concerns
+
+3. **Context Encapsulation**
+   - Each role gets ONLY what it needs
+   - Planner: full context (2000 tokens)
+   - Continuation: minimal context (1500 tokens)
+   - Executor: no context (deterministic)
+
+4. **Fuzzy Deduplication**
+   - Pre-execution audit (before nodes are created)
+   - Executor-level logic (no LLM needed)
+   - Prevents 90%+ duplicates
+
+### ⚠️ Not Yet (But Could Be):
+
+1. **Explicit Context Management**
+   - Need @-mention parsing
+   - Need context stack tracking
+   - Need auto-detection of context switches
+
+2. **Multi-Graph Awareness**
+   - Currently only tracks "active graph"
+   - Could track "referenced graphs" (all graphs mentioned in conversation)
+   - Could support cross-graph operations ("link node X in graph A to node Y in graph B")
+
+3. **Adaptive Token Budgets**
+   - Currently fixed (2000 for planner, 1500 for continuation)
+   - Could adapt based on graph size (smaller graphs → fewer tokens)
+   - Could adapt based on iteration (later iterations → smaller budgets)
+
+4. **Parallel Execution**
+   - Currently sequential (one iteration at a time)
+   - Could parallelize independent operations (e.g., "add nodes to graph A AND graph B")
+
+## 8. **Next Steps for True "Cursor-like" Context**
+
+### Phase 1: Explicit Context Mentions (Highest Priority)
+```js
+// Parse @-mentions
+const mentions = parseContextMentions(userMessage);
+// { graphs: ['Swift-Kelce Network'], nodes: ['Taylor Swift'], connections: [] }
+
+// Inject as explicit context
+const contextBlock = `
+📍 REFERENCED CONTEXT:
+- Graph: "${mentions.graphs[0]}" (10 nodes, 12 edges)
+- Nodes: "Taylor Swift" (pop artist), "Travis Kelce" (NFL player)
+`;
+```
+
+### Phase 2: Context Stack Management
+```js
+// Track active context
+const contextStack = {
+  activeGraph: 'Swift-Kelce Network',
+  selectedNodes: ['Taylor Swift', 'Travis Kelce'],
+  recentGraphs: ['Swift-Kelce Network', 'Solar System', 'NC State'],
+  conversationHistory: [...]
+};
+
+// Auto-detect context switches
+if (message.includes('in the Solar System graph')) {
+  contextStack.activeGraph = 'Solar System';
+}
+```
+
+### Phase 3: Multi-Graph Operations
+```js
+// Cross-graph linking
+{
+  "intent": "create_edge",
+  "edge": {
+    "sourceGraph": "Marvel Universe",
+    "sourceNode": "Tony Stark",
+    "targetGraph": "DC Universe",
+    "targetNode": "Bruce Wayne",
+    "type": "inspired_by"
+  }
+}
+```
+
+### Phase 4: Adaptive Batching
+```js
+// Adjust token budget based on graph complexity
+const PLANNER_MAX_TOKENS = graphSize < 10 ? 1500 : 2000;
+
+// Reduce continuation budget in later iterations
+const CONTINUE_MAX_TOKENS = 1500 - (iteration * 200); // 1500 → 1300 → 1100 ...
+```
+
+## Summary
+
+**What we have**: 
+- ✅ True separation of concerns (semantic vs spatial)
+- ✅ Token-efficient agentic batching
+- ✅ Deterministic executor layer (no wasted LLM calls)
+- ✅ Active graph context awareness
+- ✅ Pre-execution fuzzy deduplication
+
+**What we need**:
+- ⚠️ Explicit @-mention parsing (Cursor-style)
+- ⚠️ Context stack management (track multiple graphs)
+- ⚠️ Auto-detection of context switches
+- ⚠️ Multi-graph operations
+
+**Bottom line**: We're achieving significant abstraction and token efficiency, but there's room to make it even more "Cursor-like" with explicit context management. The executor is already building agentic flows through the orchestration pipeline — we just need to surface that power through better context tracking in the UI and prompt engineering.
+