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◈ Agents & Manager/2026-03-25Advanced

Running 5 Agents in Parallel on Antigravity: A Production Design for Decomposition, Conflict Resolution, and Merging

A production design for running up to five agents in parallel on Antigravity, written from an indie developer's operations perspective. Covers task decomposition, policy-based conflict resolution, artifact validation and merging, and how to tell when parallelization stops helping.

multi-agent50parallel processingarchitecture20orchestration21production71

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Running four content sites in parallel taught me something counterintuitive: the tasks I handed to a single agent with "please do all of it" were the ones that needed the most rework afterward. When one agent tries to optimize the frontend and the infrastructure at the same time, both tend to stall at about seventy percent.

That is why, as an indie developer, I began leaning on a setup of several role-specific agents running at once. This article walks through how to decompose work for up to five parallel agents on Antigravity, how to settle their conflicts, and how to merge their output safely—using the implementation code I actually run.

I will also be honest about where parallelization stops helping. When adding agents becomes the goal in itself, operations only get heavier.

Foundations: Single Agent vs. Parallel Multi-Agent

Why Parallel Agents Matter

Single Agent Limitations

A solo agent must juggle all responsibilities—frontend design, backend architecture, database optimization, testing, DevOps—simultaneously. While capable, the result is often a "jack of all trades, master of none" implementation.

Problems:

  • One agent can't simultaneously optimize for performance, security, and maintainability
  • Large codebases (>50K lines) show quality degradation
  • Specialist domain knowledge is missing
  • Execution time for complex projects exceeds human expectations

Multi-Agent Parallel Advantages

Input: "Build a full-stack SaaS application"
  │
  ├─ [Frontend Specialist] → React components, styling (parallel)
  ├─ [Backend Architect] → API design, data layer (parallel)
  ├─ [DevOps Engineer] → CI/CD, Kubernetes config (parallel)
  ├─ [Database Architect] → Schema optimization (parallel)
  └─ [QA Automation Master] → Test strategy (parallel)
  ↓
Results:
✓ Each domain is maximally optimized
✓ Quality scales with project size
✓ Total execution time = longest task (~4-6 hours vs. 10 business days)
✓ Risk distributed across agents with guardrails

Task Decomposition Strategies

Pattern A: Lightweight (3 Agents) – Mid-Size Projects

// Minimal viable parallel configuration
const lightweightConfig = {
  parallel_tier: {
    "agent_frontend": {
      type: "Frontend Specialist",
      responsibility: "UI/UX → React implementation",
      input: "project_specification.json",
      output: ["src/components/", "src/pages/", "styles/"]
    },
    "agent_backend": {
      type: "Backend Architect",
      responsibility: "API design → implementation",
      input: "project_specification.json",
      output: ["api/", "models/", "services/"]
    },
    "agent_devops": {
      type: "DevOps Engineer",
      responsibility: "CI/CD pipeline → deployment",
      dependencies: ["agent_frontend", "agent_backend"],
      input: ["frontend_outputs", "backend_outputs"],
      output: [".github/workflows/", "docker/", "terraform/"]
    }
  },
  execution_profile: {
    tier_1_duration: "45 minutes (parallel)",
    tier_2_duration: "30 minutes (after tier_1)",
    total_duration: "75 minutes"
  }
};

Pattern B: Enterprise Scale (5 Agents) – Large Projects

const enterpriseConfig = {
  stage_1_design: {
    // 30 minutes, 3 agents in parallel
    agents: ["UIDesignMaster", "APIDesignEngineer", "InfrastructureArchitect"],
    outputs: ["design_spec.json", "openapi.yaml", "architecture_diagram.json"]
  },
  stage_2_implementation: {
    // 2 hours, depends on stage_1
    agents: ["FrontendImplementation", "BackendImplementation", "DatabaseSpecialist", "InfraSetup"],
    dependencies: ["stage_1_design"]
  },
  stage_3_integration_and_testing: {
    // 1 hour, depends on stage_2
    agents: ["IntegrationTestMaster", "PerformanceTester", "SecurityTestingAnalyst"],
    dependencies: ["stage_2_implementation"]
  },
  total_time: "3.5 hours (vs. 10-15 business days traditionally)"
};

DAG-Based Dependency Management

The Directed Acyclic Graph (DAG) prevents circular dependencies and optimizes scheduling:

// dag.js - Orchestrate agent execution with dependency tracking
class ExecutionDAG {
  constructor() {
    this.tasks = {};
    this.dependencies = {};
  }
 
  addTask(taskId, agent, input, dependencies = []) {
    this.tasks[taskId] = {
      agent_type: agent,
      input: input,
      dependencies: dependencies,
      status: "pending",
      output: null,
      duration_ms: 0
    };
 
    dependencies.forEach(dep => {
      if (!this.dependencies[dep]) {
        this.dependencies[dep] = [];
      }
      this.dependencies[dep].push(taskId);
    });
  }
 
  computeExecutionTiers() {
    // Topological sort: assign tier based on max dependency tier
    const tiers = [];
    const computed = new Set();
 
    const computeTier = (taskId) => {
      if (computed.has(taskId)) return this.tasks[taskId].tier;
 
      const task = this.tasks[taskId];
      const depTiers = task.dependencies.length === 0
        ? [-1]
        : task.dependencies.map(dep => computeTier(dep));
 
      task.tier = Math.max(...depTiers) + 1;
      computed.add(taskId);
 
      if (!tiers[task.tier]) tiers[task.tier] = [];
      tiers[task.tier].push(taskId);
 
      return task.tier;
    };
 
    Object.keys(this.tasks).forEach(computeTier);
    return tiers.filter(t => t);
  }
 
  async executeParallel() {
    const tiers = this.computeExecutionTiers();
    const results = {};
 
    for (const tier of tiers) {
      console.log(`📌 Tier ${tiers.indexOf(tier)}: Running ${tier.length} tasks in parallel`);
 
      const tierPromises = tier.map(async (taskId) => {
        const task = this.tasks[taskId];
        const start = Date.now();
 
        console.log(`  ▶ [${taskId}] starting...`);
        const agent = await getAgent(task.agent_type);
        task.output = await agent.execute(task.input);
 
        task.duration_ms = Date.now() - start;
        task.status = "completed";
        results[taskId] = task.output;
 
        console.log(`  ✓ [${taskId}] done in ${(task.duration_ms / 1000).toFixed(1)}s`);
      });
 
      await Promise.all(tierPromises);
    }
 
    return results;
  }
}
 
// Usage
const dag = new ExecutionDAG();
dag.addTask("design_api", "APIDesignEngineer", projectSpec);
dag.addTask("design_db", "DatabaseArchitect", projectSpec);
dag.addTask("impl_api", "BackendImplementation", {}, ["design_api"]);
dag.addTask("impl_db", "DatabaseImplementation", {}, ["design_db"]);
dag.addTask("test_integration", "IntegrationTestMaster", {}, ["impl_api", "impl_db"]);
 
const results = await dag.executeParallel();

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WHAT YOU'LL LEARN
✦A DAG-based scheduler that resolves dependencies and runs each tier of agents in parallel
✦A policy-driven conflict resolution engine for performance-vs-security and framework disputes
✦The three situations where adding more agents makes things slower, and why review and merge become the bottleneck
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