Multi-Agent Workflows — Coordinate Multiple AIs for Faster Development

Setup and context to Multi-Agent Workflows

Multi-agent workflows represent a paradigm shift in AI-assisted development. Instead of relying on a single AI agent to handle everything sequentially, Antigravity enables orchestrating multiple specialized agents that work independently and in parallel, each contributing unique expertise to your project.

This approach mirrors how human teams work: code specialists, QA engineers, and documentation experts each focus on their domain while collaborating toward a shared objective.

Fundamentals of Task Decomposition

The foundation of effective multi-agent workflows is decomposing complex objectives into discrete, independent tasks suitable for agent execution.

Start with your goal: "Build a user authentication system with comprehensive testing and documentation."

Identify major components: Authentication API, database schema, client library integration, test suite, usage documentation.

Define agent responsibilities: Which agents handle which components? A code generation agent creates the API, a testing agent writes comprehensive tests, a documentation agent produces user guides.

Map dependencies: What must complete before subsequent work begins? Tests might depend on API generation, documentation might depend on API specification being finalized.

Effective decomposition creates minimal dependencies while maximizing parallelization opportunities. This requires understanding both your project requirements and your agents' capabilities.

Parallel Processing Architecture

Once tasks are decomposed, Antigravity's orchestration system manages parallel execution intelligently.

The beauty of parallel processing is that while your code generation agent creates REST endpoints, your testing agent simultaneously writes unit tests for those endpoints (using specifications), and your documentation agent drafts API documentation. What would require sequential steps from a human team happens concurrently.

Dependency resolution: The system automatically determines execution order. Tasks with no dependencies start immediately. Tasks dependent on others wait until prerequisites complete, then start automatically.

Resource management: Parallel execution doesn't create exponential resource consumption. Antigravity allocates resources intelligently based on available compute and your token budget.

Failure isolation: If one agent encounters issues, others continue working. You address the failure without cascading problems across your entire workflow.

Specialized Agent Types

Antigravity provides agents optimized for specific development tasks:

Code Generation Agents excel at creating new code, implementing features, and generating boilerplate. They understand your project's architecture and generate code consistent with existing patterns.

Testing Agents write comprehensive unit tests, integration tests, and test documentation. They ensure code quality and catch edge cases developers might miss.

Documentation Agents generate API documentation, user guides, code comments, and architectural documentation. They keep documentation in sync with implementations.

Code Review Agents analyze generated code for security vulnerabilities, performance issues, code style violations, and architectural concerns. They provide detailed feedback and suggestions for improvement.

Refactoring Agents identify code that could be improved, optimize algorithms, eliminate duplication, and modernize patterns. They enhance existing codebases efficiently.

Combining different agent types in coordinated workflows creates powerful development accelerators.

Designing the Message Contract — Reliable Hand-offs and Progress Tracking

A dependency diagram tells you the order of work, but it says nothing about the shape of the data agents actually pass to one another. When that shape drifts between steps, the workflow fails quietly. Running my own multi-step blog automation for Dolice as an indie developer, this was the first thing that bit me: the "research result" handed to the next step looked slightly different each time, and it became impossible to tell where information had gone missing.

The fix is to pin the message to a single, predictable contract. Every message carries its sender, recipient, type, payload, and a tracking ID.

// Minimal inter-agent message contract
const message = {
  from: "researcher",
  to: "writer",
  type: "request",          // request / response / error
  requestId: "req_12345",   // carried across every step for tracing
  payload: {
    topic: "Antigravity multi-agent",
    keyPoints: ["definition", "communication protocol", "example"],
    constraints: { minSources: 3, accuracy: 0.95 }
  },
  metadata: { priority: "high", createdAt: new Date().toISOString() }
};

Carrying requestId through every step lets you see, at a glance, which request is currently at which stage. When something fails, you can replay only that step instead of the whole chain.

An orchestrator that resumes only the stuck steps

Run each step once its dependencies have succeeded, and record the result and state per requestId.

async function runWorkflow(steps, agents, requestId) {
  const results = {};
  const progress = {};   // ledger keyed by step, scoped to requestId
 
  for (const step of steps) {
    const blocked = step.depends.find((d) => progress[d] !== "done");
    if (blocked) {
      progress[step.id] = "skipped";   // upstream not finished yet
      continue;
    }
 
    try {
      progress[step.id] = "running";
      results[step.id] = await agents[step.agent].execute({
        requestId,
        input: step.depends.map((d) => results[d])
      });
      progress[step.id] = "done";
    } catch (err) {
      progress[step.id] = "failed";   // record it instead of halting everything
      console.error(`[${requestId}] ${step.id} failed: ${err.message}`);
    }
  }
 
  return { results, progress };
}

With this ledger you no longer restart from zero. Pull out the steps marked failed or skipped, rerun just those, and reuse the results that already succeeded — which also trims wasted tokens.

What helped most in practice was keeping progress in the logs. Reading it back later showed me which steps tend to fail, and that became the material for rethinking how I split responsibilities in the first place.

Practical Workflow Patterns

Feature Development Workflow

When implementing a complex feature, orchestrate agents as follows:

1. Code Generation Agent creates the main feature implementation (executes immediately)
2. Testing Agent writes unit tests for the feature (executes immediately, in parallel)
3. Review Agent analyzes code quality and security (waits for generation to complete)
4. Documentation Agent writes feature documentation (waits for generation to provide specifications)
5. Consolidation step integrates outputs, resolves any issues

This pattern reduces feature implementation time significantly while ensuring quality, testing, and documentation occur simultaneously rather than sequentially.

API Expansion Workflow

Building comprehensive REST APIs involves multiple parallel tasks:

• Endpoint Agent creates API route handlers
• Schema Agent generates database schemas and migrations
• SDK Generator creates client libraries for different languages
• Integration Agent connects components and writes integration tests
• API Documentation Agent generates complete API reference

Running these in parallel, with intelligent dependency management, produces fully integrated, tested, and documented APIs much faster than traditional sequential development.

Codebase Refactoring Workflow

Large refactoring projects can be parallelized:

1. Analysis Agents examine different modules, identifying refactoring opportunities
2. Refactoring Agents work on different modules concurrently
3. Testing Agents verify each refactored module maintains correct behavior
4. Integration Agent ensures refactored modules integrate properly
5. Documentation Agent updates architectural documentation

This approach distributes refactoring load across multiple agents, completing large projects in days rather than weeks.

Setting Up Your First Multi-Agent Workflow

Begin with a manageable project:

Define clear objectives: Write specific, measurable goals for the workflow.

Sketch task decomposition: On paper, outline major tasks and their dependencies.

Select agents: Choose agents whose specializations align with your tasks.

Configure data flow: Specify what outputs from one agent become inputs to others.

Start small: Run the workflow, monitor progress closely, and learn what works for your patterns.

Iterate and optimize: Refine task decomposition and agent allocation based on results.

Advanced Patterns & Techniques

Approval gates: Insert manual review points where you approve outputs before downstream agents process them, maintaining quality control while enabling parallelization.

Conditional branching: Create workflows where different paths execute based on previous agent decisions. For example, "If testing agent finds security issues, trigger security review agent before code is finalized."

Iterative refinement: Use review agent feedback to automatically trigger code generation agent improvements, creating refinement loops without manual intervention.

Context libraries: Maintain shared context libraries (architectural patterns, code style guides, API specifications) that all agents reference, ensuring consistency across generated code.

Monitoring & Optimization

The Manager Surface provides detailed metrics showing:

• Agent utilization rates
• Task completion times
• Token consumption per agent
• Quality metrics from review agents
• Bottleneck identification

Use this data to optimize workflows: reassign agents from underutilized tasks, add parallelization where bottlenecks exist, and refine task decomposition based on actual performance.

Team Development Patterns

For team environments, multi-agent workflows provide significant advantages:

Standardization: Workflows enforce consistent patterns across team members' work.

Asynchronous collaboration: Team members can trigger workflows and review outputs at their convenience, enabling asynchronous development patterns.

Knowledge distribution: Complex development patterns are encoded in workflows, becoming team knowledge rather than individual expertise.

Scaling without hiring: Teams accomplish more with existing members by leveraging parallel agent workflows.

Challenges & Solutions

Coordination complexity: Large workflows with many agents can become complex. Address this by starting simple and gradually introducing sophistication.

Quality consistency: Agent outputs vary in quality. Use review agents and approval gates to maintain standards.

Cost management: Parallel execution increases token consumption. Monitor costs and optimize task definitions to reduce unnecessary context passing.

Conclusion

Multi-agent workflows represent the future of AI-assisted development. By understanding task decomposition, leveraging parallelization, and orchestrating specialized agents effectively, you can dramatically accelerate development while maintaining quality and enabling sophisticated development patterns previously requiring large teams. Start experimenting with multi-agent workflows today to discover the possibilities within your development practices.