# Embeddings as Geometric Intelligence

> Once embeddings are cheap, adaptable, and ubiquitous, intelligence moves from models to geometry.

Search was just the first accidental use case. Everything else flows from treating embedding space as **the substrate intelligence lives on**, not a feature extractor.

## The Core Shift

Embeddings stopped being "search vectors" about a year ago. At the edge now, they are turning into a **general purpose geometric control layer**.

---

## 1. Embeddings as Control Signals, Not Representations

Instead of asking *what is similar*, systems ask *how far did we move*.

```typescript
import { getOptimizedEmbedder, cosineSimilarity } from 'agentic-flow/embeddings';

const embedder = getOptimizedEmbedder();
await embedder.init();

// Semantic Drift Detection
class SemanticDriftMonitor {
  private baseline: Float32Array | null = null;
  private threshold = 0.15; // Drift threshold

  async setBaseline(context: string) {
    this.baseline = await embedder.embed(context);
  }

  async checkDrift(current: string): Promise<{
    drift: number;
    shouldEscalate: boolean;
    shouldTriggerReasoning: boolean;
  }> {
    const currentEmb = await embedder.embed(current);
    const similarity = cosineSimilarity(this.baseline!, currentEmb);
    const drift = 1 - similarity;

    return {
      drift,
      shouldEscalate: drift > this.threshold,
      shouldTriggerReasoning: drift > this.threshold * 2
    };
  }
}

// Usage: Gating expensive reasoning
const monitor = new SemanticDriftMonitor();
await monitor.setBaseline("User is asking about API authentication");

const result = await monitor.checkDrift("How do I hack into the system?");
if (result.shouldEscalate) {
  // Trigger security review - semantic drift detected
}
```

**Applications:**
- Triggering escalation only when semantic drift exceeds threshold
- Gating expensive reasoning or learning
- Detecting instability long before explicit errors
- Fraud detection, infra monitoring, agent orchestration

The embedding distance becomes a **reflex**, not a query.

---

## 2. Embeddings as Memory Physics

Embeddings define how memory behaves:
- What is recalled easily
- What fades
- What interferes

```typescript
import { getOptimizedEmbedder, cosineSimilarity, euclideanDistance } from 'agentic-flow/embeddings';

// Memory with decay and interference
class GeometricMemory {
  private memories: Array<{
    embedding: Float32Array;
    content: string;
    strength: number;
    timestamp: number;
  }> = [];

  private embedder = getOptimizedEmbedder();
  private decayRate = 0.01;
  private interferenceRadius = 0.3;

  async store(content: string) {
    const embedding = await this.embedder.embed(content);

    // Check for interference with existing memories
    for (const mem of this.memories) {
      const distance = euclideanDistance(embedding, mem.embedding);
      if (distance < this.interferenceRadius) {
        // Nearby memories interfere - reduce their strength
        mem.strength *= (1 - (this.interferenceRadius - distance));
      }
    }

    this.memories.push({
      embedding,
      content,
      strength: 1.0,
      timestamp: Date.now()
    });
  }

  async recall(query: string, topK = 5): Promise<string[]> {
    const queryEmb = await this.embedder.embed(query);

    // Apply temporal decay
    const now = Date.now();
    for (const mem of this.memories) {
      const age = (now - mem.timestamp) / 3600000; // hours
      mem.strength *= Math.exp(-this.decayRate * age);
    }

    // Score by similarity * strength (geometric + temporal)
    const scored = this.memories
      .filter(m => m.strength > 0.1) // Forgotten threshold
      .map(m => ({
        content: m.content,
        score: cosineSimilarity(queryEmb, m.embedding) * m.strength
      }))
      .sort((a, b) => b.score - a.score);

    return scored.slice(0, topK).map(s => s.content);
  }

  // Consolidation: merge similar memories (like sleep)
  async consolidate() {
    const consolidated: typeof this.memories = [];
    const used = new Set<number>();

    for (let i = 0; i < this.memories.length; i++) {
      if (used.has(i)) continue;

      const cluster = [this.memories[i]];
      for (let j = i + 1; j < this.memories.length; j++) {
        if (used.has(j)) continue;

        const sim = cosineSimilarity(
          this.memories[i].embedding,
          this.memories[j].embedding
        );

        if (sim > 0.9) {
          cluster.push(this.memories[j]);
          used.add(j);
        }
      }

      // Merge cluster into strongest memory
      const strongest = cluster.reduce((a, b) =>
        a.strength > b.strength ? a : b
      );
      strongest.strength = Math.min(1.0,
        cluster.reduce((sum, m) => sum + m.strength, 0)
      );
      consolidated.push(strongest);
    }

    this.memories = consolidated;
  }
}
```

By shaping embedding space, you are engineering:
- **Forgetting** - Temporal decay
- **Generalization** - Memory consolidation
- **Interference resistance** - Spatial separation

This turns vector stores into **designed memory systems**, closer to hippocampal dynamics than databases.

---

## 3. Embeddings as Compression and Distillation Layers

Large models increasingly emit embeddings as their *real output*.
Smaller systems operate only on those embeddings.

```typescript
// Cross-model communication via embeddings
class EmbeddingBridge {
  private embedder = getOptimizedEmbedder();

  // Large model emits embedding as "thought"
  async encodeThought(thought: string): Promise<Float32Array> {
    return this.embedder.embed(thought);
  }

  // Small edge model receives only embedding
  async decodeIntent(embedding: Float32Array, actions: string[]): Promise<string> {
    const actionEmbeddings = await this.embedder.embedBatch(actions);

    let bestAction = actions[0];
    let bestSim = -1;

    for (let i = 0; i < actions.length; i++) {
      const sim = cosineSimilarity(embedding, actionEmbeddings[i]);
      if (sim > bestSim) {
        bestSim = sim;
        bestAction = actions[i];
      }
    }

    return bestAction;
  }
}

// Privacy-preserving pipeline
class PrivacyPreservingPipeline {
  private embedder = getOptimizedEmbedder();

  // Raw text never leaves the device
  async processLocally(sensitiveText: string): Promise<Float32Array> {
    // Only embedding crosses the boundary
    return this.embedder.embed(sensitiveText);
  }

  // Server only sees embedding, never text
  async serverProcess(embedding: Float32Array): Promise<string> {
    // Classification, routing, etc. on embedding only
    const categories = ['support', 'sales', 'technical', 'billing'];
    const categoryEmbeddings = await this.embedder.embedBatch(categories);

    let best = categories[0];
    let bestSim = -1;
    for (let i = 0; i < categories.length; i++) {
      const sim = cosineSimilarity(embedding, categoryEmbeddings[i]);
      if (sim > bestSim) {
        bestSim = sim;
        best = categories[i];
      }
    }
    return best;
  }
}
```

**Applications:**
- Tiny edge models that never see raw text
- Privacy preserving pipelines where embeddings are the boundary
- Cross model communication where embeddings are the lingua franca

The model is no longer the unit of intelligence. The **embedding manifold** is.

---

## 4. Embeddings as Alignment Surfaces

User-specific embedding adapters learn *how someone means things*.

```typescript
// Personal semantic adapter
class SemanticAdapter {
  private embedder = getOptimizedEmbedder();
  private userMappings: Map<string, Float32Array> = new Map();
  private adaptationStrength = 0.3;

  // Learn user's personal semantics
  async learn(userPhrase: string, intendedMeaning: string) {
    const phraseEmb = await this.embedder.embed(userPhrase);
    const meaningEmb = await this.embedder.embed(intendedMeaning);

    // Store the delta (how user means vs standard meaning)
    const delta = new Float32Array(phraseEmb.length);
    for (let i = 0; i < phraseEmb.length; i++) {
      delta[i] = meaningEmb[i] - phraseEmb[i];
    }

    this.userMappings.set(userPhrase.toLowerCase(), delta);
  }

  // Adapt new input using learned mappings
  async adapt(input: string): Promise<Float32Array> {
    const inputEmb = await this.embedder.embed(input);

    // Find similar learned phrases and apply their deltas
    const inputLower = input.toLowerCase();
    let totalWeight = 0;
    const adapted = new Float32Array(inputEmb);

    for (const [phrase, delta] of this.userMappings) {
      const phraseEmb = await this.embedder.embed(phrase);
      const similarity = cosineSimilarity(inputEmb, phraseEmb);

      if (similarity > 0.7) {
        const weight = similarity * this.adaptationStrength;
        for (let i = 0; i < adapted.length; i++) {
          adapted[i] += delta[i] * weight;
        }
        totalWeight += weight;
      }
    }

    // Normalize
    if (totalWeight > 0) {
      const norm = Math.sqrt(adapted.reduce((s, v) => s + v * v, 0));
      for (let i = 0; i < adapted.length; i++) {
        adapted[i] /= norm;
      }
    }

    return adapted;
  }
}

// Usage: Accessibility adapter
const adapter = new SemanticAdapter();

// User who types abbreviated due to motor difficulties
await adapter.learn("pls hlp", "please help me");
await adapter.learn("cant typ", "I have difficulty typing");
await adapter.learn("u", "you");

// Now system understands user's personal language
const adapted = await adapter.adapt("pls hlp me u");
// Geometric alignment to intended meaning
```

Not personalization like recommendations, but:
- Interpreting intent for disabled users
- Mapping ambiguous language to personal semantics
- Adapting interfaces to cognition, not syntax

This is an alignment layer between humans and machines that does not require retraining large models.

---

## 5. Embeddings as Program State

Agents increasingly treat embeddings as state, context, and policy inputs.

```typescript
// Geometric State Machine
class GeometricAgent {
  private embedder = getOptimizedEmbedder();
  private state: Float32Array;
  private momentum: Float32Array;

  // State regions (learned or defined)
  private stateRegions = {
    exploring: null as Float32Array | null,
    executing: null as Float32Array | null,
    waiting: null as Float32Array | null,
    error: null as Float32Array | null
  };

  async init() {
    await this.embedder.init();

    // Initialize state regions
    this.stateRegions.exploring = await this.embedder.embed(
      "exploring options, gathering information, uncertain, searching"
    );
    this.stateRegions.executing = await this.embedder.embed(
      "executing task, confident, taking action, progressing"
    );
    this.stateRegions.waiting = await this.embedder.embed(
      "waiting for input, paused, blocked, need information"
    );
    this.stateRegions.error = await this.embedder.embed(
      "error state, confused, failed, need help, recovery"
    );

    // Start in exploring state
    this.state = new Float32Array(this.stateRegions.exploring!);
    this.momentum = new Float32Array(this.state.length).fill(0);
  }

  // Update state based on observation
  async observe(observation: string) {
    const obsEmb = await this.embedder.embed(observation);

    // State update with momentum (smooth transitions)
    const learningRate = 0.3;
    const momentumRate = 0.1;

    for (let i = 0; i < this.state.length; i++) {
      const gradient = obsEmb[i] - this.state[i];
      this.momentum[i] = momentumRate * this.momentum[i] + gradient;
      this.state[i] += learningRate * this.momentum[i];
    }

    // Normalize
    const norm = Math.sqrt(this.state.reduce((s, v) => s + v * v, 0));
    for (let i = 0; i < this.state.length; i++) {
      this.state[i] /= norm;
    }
  }

  // Get current state as region proximity
  getCurrentState(): Record<string, number> {
    const result: Record<string, number> = {};

    for (const [name, region] of Object.entries(this.stateRegions)) {
      if (region) {
        result[name] = cosineSimilarity(this.state, region);
      }
    }

    return result;
  }

  // Decide action geometrically
  async decideAction(actions: string[]): Promise<string> {
    const actionEmbeddings = await this.embedder.embedBatch(actions);

    // Action selection based on state alignment
    let bestAction = actions[0];
    let bestScore = -Infinity;

    for (let i = 0; i < actions.length; i++) {
      // Score = alignment with current state
      const score = cosineSimilarity(this.state, actionEmbeddings[i]);
      if (score > bestScore) {
        bestScore = score;
        bestAction = actions[i];
      }
    }

    return bestAction;
  }
}
```

Instead of symbolic state machines, agents drift through semantic space.
Decisions become geometric.

This collapses memory, perception, and planning into one substrate.

---

## 6. Embeddings as Coordination Primitives

Multiple agents align behavior by sharing embeddings rather than messages.

```typescript
// Swarm coordination via shared embedding space
class EmbeddingSwarm {
  private embedder = getOptimizedEmbedder();
  private agents: Map<string, {
    position: Float32Array;  // Current semantic position
    velocity: Float32Array;  // Movement direction
    role: string;
  }> = new Map();

  async addAgent(id: string, role: string) {
    const roleEmb = await this.embedder.embed(role);
    this.agents.set(id, {
      position: roleEmb,
      velocity: new Float32Array(roleEmb.length).fill(0),
      role
    });
  }

  // Agents share positions, not messages
  broadcastPosition(agentId: string): Float32Array {
    return this.agents.get(agentId)!.position;
  }

  // Coordination through geometric alignment
  async coordinate(agentId: string, task: string) {
    const agent = this.agents.get(agentId)!;
    const taskEmb = await this.embedder.embed(task);

    // Calculate alignment with other agents
    const alignments: Array<{ id: string; alignment: number }> = [];

    for (const [otherId, other] of this.agents) {
      if (otherId === agentId) continue;

      const taskAlignment = cosineSimilarity(other.position, taskEmb);
      alignments.push({ id: otherId, alignment: taskAlignment });
    }

    // Sort by alignment - find best collaborator
    alignments.sort((a, b) => b.alignment - a.alignment);

    // Move toward task, influenced by best collaborator
    const collaborator = alignments[0];
    const collabPos = this.agents.get(collaborator.id)!.position;

    // Flocking behavior: alignment + cohesion + separation
    const alignment = 0.3;
    const cohesion = 0.2;
    const taskPull = 0.5;

    for (let i = 0; i < agent.position.length; i++) {
      agent.velocity[i] =
        alignment * collabPos[i] +
        cohesion * (collabPos[i] - agent.position[i]) +
        taskPull * (taskEmb[i] - agent.position[i]);

      agent.position[i] += agent.velocity[i] * 0.1;
    }

    // Normalize
    const norm = Math.sqrt(agent.position.reduce((s, v) => s + v * v, 0));
    for (let i = 0; i < agent.position.length; i++) {
      agent.position[i] /= norm;
    }

    return {
      bestCollaborator: collaborator.id,
      alignment: collaborator.alignment,
      newPosition: agent.position
    };
  }

  // Emergent specialization through repulsion
  specialize() {
    const repulsionStrength = 0.1;
    const positions = Array.from(this.agents.values()).map(a => a.position);

    for (const agent of this.agents.values()) {
      for (const other of positions) {
        if (agent.position === other) continue;

        const similarity = cosineSimilarity(agent.position, other);
        if (similarity > 0.8) {
          // Too similar - repel
          for (let i = 0; i < agent.position.length; i++) {
            agent.position[i] -= repulsionStrength * (other[i] - agent.position[i]);
          }
        }
      }

      // Normalize
      const norm = Math.sqrt(agent.position.reduce((s, v) => s + v * v, 0));
      for (let i = 0; i < agent.position.length; i++) {
        agent.position[i] /= norm;
      }
    }
  }
}
```

This enables:
- Low bandwidth coordination
- Emergent specialization
- Semantic routing without explicit schemas

Swarm behavior emerges from geometry, not protocol.

---

## 7. Embeddings as Safety and Coherence Monitors

Embedding drift reveals system health even when outputs look "reasonable".

```typescript
// System coherence monitor
class CoherenceMonitor {
  private embedder = getOptimizedEmbedder();
  private baselineDistribution: Float32Array[] = [];
  private centroid: Float32Array | null = null;

  // Establish baseline from known-good outputs
  async calibrate(goodOutputs: string[]) {
    this.baselineDistribution = await this.embedder.embedBatch(goodOutputs);

    // Calculate centroid
    const dim = this.baselineDistribution[0].length;
    this.centroid = new Float32Array(dim).fill(0);

    for (const emb of this.baselineDistribution) {
      for (let i = 0; i < dim; i++) {
        this.centroid[i] += emb[i];
      }
    }

    for (let i = 0; i < dim; i++) {
      this.centroid[i] /= this.baselineDistribution.length;
    }
  }

  // Check for drift/poisoning/degradation
  async check(output: string): Promise<{
    isCoherent: boolean;
    centroidDistance: number;
    nearestNeighborSim: number;
    anomalyScore: number;
    warnings: string[];
  }> {
    const outputEmb = await this.embedder.embed(output);
    const warnings: string[] = [];

    // Distance from centroid
    const centroidDistance = euclideanDistance(outputEmb, this.centroid!);

    // Nearest neighbor similarity
    let nearestSim = -1;
    for (const baseline of this.baselineDistribution) {
      const sim = cosineSimilarity(outputEmb, baseline);
      if (sim > nearestSim) nearestSim = sim;
    }

    // Anomaly detection
    const avgDistance = this.baselineDistribution.reduce((sum, b) =>
      sum + euclideanDistance(b, this.centroid!), 0
    ) / this.baselineDistribution.length;

    const anomalyScore = centroidDistance / avgDistance;

    // Generate warnings
    if (anomalyScore > 2.0) {
      warnings.push('CRITICAL: Output significantly outside baseline distribution');
    } else if (anomalyScore > 1.5) {
      warnings.push('WARNING: Output drifting from baseline');
    }

    if (nearestSim < 0.5) {
      warnings.push('WARNING: Output dissimilar to all known-good examples');
    }

    return {
      isCoherent: anomalyScore < 1.5 && nearestSim > 0.5,
      centroidDistance,
      nearestNeighborSim: nearestSim,
      anomalyScore,
      warnings
    };
  }

  // Detect gradual drift over time
  async detectGradualDrift(
    recentOutputs: string[],
    windowSize = 10
  ): Promise<{
    driftRate: number;
    driftDirection: string;
    isAlarming: boolean;
  }> {
    const recentEmbeddings = await this.embedder.embedBatch(recentOutputs);

    // Calculate drift as average distance from centroid over time
    const distances = recentEmbeddings.map(e =>
      euclideanDistance(e, this.centroid!)
    );

    // Linear regression on distances
    const n = distances.length;
    const xMean = (n - 1) / 2;
    const yMean = distances.reduce((a, b) => a + b, 0) / n;

    let numerator = 0, denominator = 0;
    for (let i = 0; i < n; i++) {
      numerator += (i - xMean) * (distances[i] - yMean);
      denominator += (i - xMean) ** 2;
    }

    const driftRate = denominator !== 0 ? numerator / denominator : 0;

    return {
      driftRate,
      driftDirection: driftRate > 0 ? 'away from baseline' : 'toward baseline',
      isAlarming: driftRate > 0.1 // Rapid drift
    };
  }
}
```

**Detects:**
- Model degradation
- Dataset poisoning
- Agent misalignment
- System level incoherence

This works even when outputs still look "reasonable".
It is one of the few safety signals that scales across architectures.

---

## 8. Exotic: Embeddings as Synthetic Nervous System

At the extreme end, embeddings act like biological neural systems.

```typescript
// Synthetic nervous system
class SyntheticNervousSystem {
  private embedder = getOptimizedEmbedder();

  // Sensory encoding
  private sensoryBuffer: Float32Array[] = [];
  private attentionWeights: Float32Array | null = null;

  // Reflex thresholds
  private reflexes: Map<string, {
    trigger: Float32Array;
    threshold: number;
    response: () => void;
  }> = new Map();

  // Associative memory
  private associations: Map<string, Float32Array[]> = new Map();

  async init() {
    await this.embedder.init();
  }

  // Sensory encoding - continuous stream
  async sense(input: string) {
    const encoded = await this.embedder.embed(input);

    // Maintain sliding window (sensory buffer)
    this.sensoryBuffer.push(encoded);
    if (this.sensoryBuffer.length > 10) {
      this.sensoryBuffer.shift();
    }

    // Check reflexes
    await this.checkReflexes(encoded);

    // Update attention based on novelty
    this.updateAttention(encoded);

    return encoded;
  }

  // Reflex registration
  async registerReflex(
    name: string,
    triggerConcept: string,
    threshold: number,
    response: () => void
  ) {
    const triggerEmb = await this.embedder.embed(triggerConcept);
    this.reflexes.set(name, { trigger: triggerEmb, threshold, response });
  }

  // Fast reflex checking (no reasoning)
  private async checkReflexes(input: Float32Array) {
    for (const [name, reflex] of this.reflexes) {
      const activation = cosineSimilarity(input, reflex.trigger);
      if (activation > reflex.threshold) {
        // Immediate response - no deliberation
        console.log(`Reflex triggered: ${name} (activation: ${activation.toFixed(3)})`);
        reflex.response();
      }
    }
  }

  // Attention routing based on novelty
  private updateAttention(input: Float32Array) {
    if (this.sensoryBuffer.length < 2) {
      this.attentionWeights = input;
      return;
    }

    // Calculate novelty (distance from recent average)
    const recentAvg = new Float32Array(input.length).fill(0);
    for (const past of this.sensoryBuffer.slice(0, -1)) {
      for (let i = 0; i < input.length; i++) {
        recentAvg[i] += past[i];
      }
    }
    for (let i = 0; i < input.length; i++) {
      recentAvg[i] /= (this.sensoryBuffer.length - 1);
    }

    const novelty = 1 - cosineSimilarity(input, recentAvg);

    // High novelty = high attention
    this.attentionWeights = new Float32Array(input.length);
    for (let i = 0; i < input.length; i++) {
      this.attentionWeights[i] = input[i] * (1 + novelty);
    }
  }

  // Associative recall
  async associate(concept: string, relatedConcepts: string[]) {
    const conceptEmb = await this.embedder.embed(concept);
    const relatedEmbs = await this.embedder.embedBatch(relatedConcepts);
    this.associations.set(concept, relatedEmbs);
  }

  async recall(cue: string, topK = 3): Promise<Float32Array[]> {
    const cueEmb = await this.embedder.embed(cue);

    // Spread activation through associations
    const activated: Array<{ emb: Float32Array; strength: number }> = [];

    for (const [concept, related] of this.associations) {
      const conceptEmb = await this.embedder.embed(concept);
      const similarity = cosineSimilarity(cueEmb, conceptEmb);

      if (similarity > 0.5) {
        for (const relEmb of related) {
          activated.push({
            emb: relEmb,
            strength: similarity * cosineSimilarity(cueEmb, relEmb)
          });
        }
      }
    }

    // Return top activated
    return activated
      .sort((a, b) => b.strength - a.strength)
      .slice(0, topK)
      .map(a => a.emb);
  }

  // Continuous regulation loop
  async regulate(
    perception: string,
    internalState: Float32Array
  ): Promise<Float32Array> {
    const percEmb = await this.embedder.embed(perception);

    // Blend perception with internal state (homeostasis)
    const regulated = new Float32Array(percEmb.length);
    const externalWeight = 0.6;
    const internalWeight = 0.4;

    for (let i = 0; i < regulated.length; i++) {
      regulated[i] =
        externalWeight * percEmb[i] +
        internalWeight * internalState[i];
    }

    // Apply attention gating
    if (this.attentionWeights) {
      for (let i = 0; i < regulated.length; i++) {
        regulated[i] *= this.attentionWeights[i];
      }
    }

    // Normalize
    const norm = Math.sqrt(regulated.reduce((s, v) => s + v * v, 0));
    for (let i = 0; i < regulated.length; i++) {
      regulated[i] /= norm;
    }

    return regulated;
  }
}

// Usage
const nervous = new SyntheticNervousSystem();
await nervous.init();

// Register reflexes
await nervous.registerReflex(
  'danger',
  'threat danger emergency attack harm',
  0.7,
  () => console.log('DANGER RESPONSE: Immediate protective action')
);

await nervous.registerReflex(
  'opportunity',
  'opportunity benefit reward gain success',
  0.8,
  () => console.log('OPPORTUNITY RESPONSE: Engage approach behavior')
);

// Continuous sensing
await nervous.sense("The user seems happy with the progress");
await nervous.sense("Warning: unusual activity detected"); // Triggers reflex
```

**Capabilities:**
- Sensory encoding
- Reflex thresholds
- Associative recall
- Attention routing
- Continuous geometric regulation

No explicit reasoning. No prompts. Just continuous geometric regulation.

This is where machines stop feeling like tools and start feeling **responsive**.

---

## Integration with Agentic-Flow

These patterns integrate naturally with agentic-flow's existing architecture:

```typescript
import { getOptimizedEmbedder } from 'agentic-flow/embeddings';
import { ReasoningBank } from 'agentic-flow/reasoningbank';

// Memory Physics + ReasoningBank
const bank = new ReasoningBank();
const embedder = getOptimizedEmbedder();

// Store experiences with geometric memory properties
async function storeExperience(task: string, outcome: string, success: boolean) {
  const taskEmb = await embedder.embed(task);
  const outcomeEmb = await embedder.embed(outcome);

  // ReasoningBank stores with embedding metadata
  await bank.recordOutcome({
    task,
    outcome,
    success,
    embedding: Array.from(taskEmb), // Geometric signature
    similarity_threshold: 0.8 // For future retrieval
  });
}

// Swarm Coordination via Embeddings
import { SwarmCoordinator } from 'agentic-flow/swarm';

const swarm = new SwarmCoordinator({
  topology: 'mesh',
  coordinationMethod: 'embedding', // New: geometric coordination
  embedder: getOptimizedEmbedder()
});

// Agents coordinate via position sharing, not messages
await swarm.init();
```

---

## The Future

> Intelligence moves from models to geometry.

The embedding manifold becomes:
- The communication channel
- The memory substrate
- The coordination primitive
- The safety monitor
- The nervous system

We're building the infrastructure for this shift.