interface AgentTask {
  id: string;
  type: 'research' | 'code' | 'review' | 'design';
  input: unknown;
  dependencies: string[]; // Task IDs that must complete first
}

interface AgentResult {
  taskId: string;
  output: unknown;
  confidence: number;
  reasoning: string;
}

async function orchestrateAgents(
  goal: string,
  availableAgents: Agent[]
): Promise<AgentResult[]> {
  // Step 1: Use extended thinking to decompose goal into tasks
  const taskPlan = await planTasks(goal, availableAgents);

  // Step 2: Build dependency graph
  const dependencyGraph = buildDependencyGraph(taskPlan.tasks);

  // Step 3: Execute tasks respecting dependencies
  const results: AgentResult[] = [];
  const completed = new Set<string>();

  while (completed.size < taskPlan.tasks.length) {
    // Find tasks with satisfied dependencies
    const ready = taskPlan.tasks.filter(task =>
      !completed.has(task.id) &&
      task.dependencies.every(dep => completed.has(dep))
    );

    // Execute ready tasks in parallel
    const batchResults = await Promise.all(
      ready.map(task => executeAgentTask(task, availableAgents))
    );

    // Validate results - use extended thinking for conflicts
    const validatedResults = await validateAndResolveConflicts(
      batchResults,
      results
    );

    results.push(...validatedResults);
    ready.forEach(task => completed.add(task.id));
  }

  return results;
}

import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic();

async function planTasks(
  goal: string,
  agents: Agent[]
): Promise<{ tasks: AgentTask[]; rationale: string }> {
  // Extended thinking requires budget_tokens < max_tokens
  // Minimum budget: 1,024 tokens
  const response = await anthropic.messages.create({
    model: 'claude-opus-4-5-20251101', // Or claude-sonnet-4-5-20250929
    max_tokens: 16000,
    thinking: {
      type: 'enabled',
      budget_tokens: 10000 // Extended thinking for complex planning
    },
    messages: [{
      role: 'user',
      content: `
        Goal: ${goal}

        Available agents and their capabilities:
        ${agents.map(a => `- ${a.name}: ${a.capabilities.join(', ')}`).join('\n')}

        Decompose this goal into tasks. For each task, specify:
        1. Which agent should handle it
        2. What input it needs
        3. Which other tasks it depends on
        4. Expected output format

        Think carefully about:
        - Optimal parallelization opportunities
        - Potential conflicts between agent outputs
        - Information that needs to flow between tasks
      `
    }]
  });

  // Response contains thinking blocks followed by text blocks
  // content: [{ type: 'thinking', thinking: '...' }, { type: 'text', text: '...' }]
  return parseTaskPlan(response);
}

async function validateAndResolveConflicts(
  newResults: AgentResult[],
  existingResults: AgentResult[]
): Promise<AgentResult[]> {
  // Check for conflicts with existing results
  const conflicts = detectConflicts(newResults, existingResults);

  if (conflicts.length === 0) {
    return newResults;
  }

  // Use extended thinking to resolve conflicts
  const resolution = await anthropic.messages.create({
    model: 'claude-opus-4-5-20251101',
    max_tokens: 8000,
    thinking: {
      type: 'enabled',
      budget_tokens: 5000
    },
    messages: [{
      role: 'user',
      content: `
        The following agent outputs conflict:

        ${conflicts.map(c => `
          Conflict: ${c.description}
          Agent A (${c.agentA.name}): ${JSON.stringify(c.resultA)}
          Agent B (${c.agentB.name}): ${JSON.stringify(c.resultB)}
        `).join('\n\n')}

        Analyze each conflict and determine:
        1. Which output is more likely correct and why
        2. If both have merit, how to synthesize them
        3. What additional verification might be needed
      `
    }]
  });

  return applyResolutions(newResults, resolution);
}

async function selectOptimalAgent(
  task: AgentTask,
  agents: Agent[],
  context: ExecutionContext
): Promise<Agent> {
  // Score each agent based on:
  // - Capability match
  // - Current load
  // - Historical performance on similar tasks
  // - Cost (model tier)

  const scores = agents.map(agent => ({
    agent,
    score: calculateAgentScore(agent, task, context)
  }));

  // For complex tasks, use Opus; for simple tasks, use Haiku
  const complexity = assessTaskComplexity(task);

  if (complexity > 0.7) {
    // Filter to agents that can use Opus
    const opusCapable = scores.filter(s => s.agent.supportsOpus);
    return opusCapable.sort((a, b) => b.score - a.score)[0].agent;
  }

  return scores.sort((a, b) => b.score - a.score)[0].agent;
}

interface AgentMessage {
  from: string;
  to: string | 'broadcast';
  type: 'request' | 'response' | 'update' | 'conflict';
  payload: unknown;
  timestamp: Date;
}

class AgentCommunicationBus {
  private messages: AgentMessage[] = [];
  private subscribers: Map<string, (msg: AgentMessage) => void> = new Map();

  send(message: AgentMessage): void {
    this.messages.push(message);

    if (message.to === 'broadcast') {
      this.subscribers.forEach(callback => callback(message));
    } else {
      this.subscribers.get(message.to)?.(message);
    }
  }

  subscribe(agentId: string, callback: (msg: AgentMessage) => void): void {
    this.subscribers.set(agentId, callback);
  }

  getHistory(agentId: string): AgentMessage[] {
    return this.messages.filter(
      m => m.from === agentId || m.to === agentId || m.to === 'broadcast'
    );
  }
}

import { encoding_for_model } from 'tiktoken'

function countTokens(text: string, model = 'gpt-4'): number {
  const encoder = encoding_for_model(model)
  const tokens = encoder.encode(text)
  encoder.free()
  return tokens.length
}

undefined

---

const cache = new Map<string, { embedding: number[]; response: string }>()

async function cachedRAG(query: string) {
  const queryEmbedding = await createEmbedding(query)

  // Check if similar query exists in cache
  for (const [cachedQuery, cached] of cache.entries()) {
    const similarity = cosineSimilarity(queryEmbedding, cached.embedding)
    if (similarity > 0.95) {
      return cached.response
    }
  }

  // Not cached, perform RAG
  const response = await ragQuery(query)
  cache.set(query, { embedding: queryEmbedding, response })
  return response
}

interface ConversationMemory {
  messages: Message[] // Last 10 messages
  summary?: string // Summary of older messages
}

async function getConversationContext(userId: string): Promise<Message[]> {
  const memory = await db.memory.findUnique({ where: { userId } })

  return [
    { role: 'system', content: `Previous conversation summary: ${memory.summary}` },
    ...memory.messages.slice(-5) // Last 5 messages
  ]
}

const fewShotExamples = `
Example 1:
Input: "I love this product!"
Sentiment: Positive

Example 2:
Input: "It's okay, nothing special"
Sentiment: Neutral
`

// Include in system prompt

const prompt = `${problem}\n\nLet's think step by step:`