# Post 4: LLM Reasoning Comes Last — And That's the Point

**Series:** The Graph-of-Agents Reasoning Model (4 of 7)
**Visual:** `stories/query-journey/story-traversal-2-messages.png`
**Platforms:** LinkedIn, Twitter, Reddit

---

## LinkedIn / Facebook Caption

```
Every other tool puts the LLM first.
We put it last. Here's why that changes everything.

By the time the LLM sees the question, two things
have already happened:

1. MEMORY filtered all nodes down to connected ones
   (10,127 nodes → 11 relevant nodes)

2. EMBEDDINGS scored relevance and activated the subgraph
   (11 connected → 8 highly relevant)

Now the LLM receives:
→ 8 activated nodes (not 10,127)
→ Their chunks, edges, and evidence (not raw files)
→ 500 tokens of precise context (not 50,000 tokens of everything)

But here's where it gets interesting.

Each activated node becomes an AGENT.
They don't just answer independently.
They TALK to each other.

auth → routes: "Do you consume my JWT validation?"
routes → auth: "Yes — 3 endpoints use @auth_required"
api → auth: "I import verify_token() and AuthError"
middleware → all: "I wrap auth for every request"

This is graph-of-agents reasoning.
Not one model answering alone.
Multiple specialized agents reaching consensus.

The result:
→ 92% confidence (scored across 5 axes)
→ Evidence chain linking every claim to source code
→ $0.0003 total cost
→ 5.2 seconds

The LLM's job isn't to discover your architecture.
It's to synthesize what the graph already knows.

That's why it only needs 500 tokens.

pip install graqle

#GraphOfAgents #AI #LLM #DevTools #GraQle
```

## Twitter / X Thread (continuation)

```
9/ LAYER 3: LLM Reasoning (cost: $0.0003)

The LLM is the LAST thing that runs. Not the first.

By now:
→ Memory: 10,127 → 11 nodes
→ Embedding: 11 → 8 nodes
→ LLM receives: 500 tokens of curated context

Not 50,000 tokens of raw files.
500 tokens of architecture.
```

```
10/ But it gets better.

Each activated node becomes an AGENT.

auth talks to routes: "Do you use my JWT?"
routes responds: "3 endpoints depend on it"
api confirms: "I import verify_token + AuthError"

They negotiate. Exchange evidence. Reach consensus.

This is graph-of-agents reasoning.
```

```
11/ The consensus produces:

→ 92% confidence (not a guess — scored on 5 axes)
→ Evidence chain (every claim linked to code)
→ Impact radius (3 direct, 8 transitive)
→ Cost: $0.0003

One model guessing = hallucination.
Eight agents negotiating = consensus.
```
