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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)

{
  "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)

[
  { type: 'addNodePrototype', prototypeData: {...} },
  { type: 'addNodeInstance', graphId: '...', prototypeId: '...', position: {x,y} },
  { type: 'addEdge', edgeData: {...} }
]

Level 4: UI Mutations (Committer Output)

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:

// 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:

// 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)

// 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

// 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

// 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

// 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.