class AutonomousAgent {
  constructor(capabilities) {
    this.reasoning = new LLMEngine();
    this.tools = capabilities;
    this.memory = new WorkingMemory();
  }

  async plan(task) {
    const ctx = await this.analyze(task);
    return this.reasoning.decompose(ctx);
  }
}

# Multi-agent code generation
from agent_framework import ReasoningAgent

class CodeReviewAgent(ReasoningAgent):
    def analyze_pr(self, diff):
        issues = self.detect_patterns(diff)
        fixes = self.suggest_improvements()
        return self.format_review(issues, fixes)

interface AgentOrchestrator {
  delegate(task: Task): Promise<AgentResult>;
  coordinate(agents: Agent[]): Workflow;
  monitor(ctx: ExecCtx): void;
}

const swarm = new AgentSwarm();
await swarm.collaborate(complexTask);

// Hybrid human-agent loop
const pipeline = [
  'requirement_analysis',
  'architecture_design',
  'code_generation',
  'quality_assurance',
  'deployment'
].map(stage => new Agent(stage));

> agent.plan(task)
[planner] decomposing into 4 steps
[coder]   generating tests…
[runner]  pytest passed (12/12)
[critic]  refactor suggested
[deploy]  ✓ shipped to staging

// LLM agent loop
while (!task.isDone()) {
  const thought = await llm.reason(state);
  const action = planner.next(thought);
  state = tools.execute(action);
}