Articles/Agents & Manager

◈ Agents & Manager/2026-04-09Intermediate

Building Multi-Agent Collaboration Systems in Antigravity: From Design to Production

A comprehensive guide to designing and implementing multi-agent collaboration systems in Antigravity. Covers architecture patterns, agent communication, error recovery, state management, and production monitoring.

multi-agent⁴⁸ Antigravity²⁷⁴ AgentKit²² AI automation agent orchestration

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What Is a Multi-Agent System?

A multi-agent system (MAS) is an architecture in which multiple specialized AI agents collaborate to complete complex tasks — each one handling a defined role rather than a single monolithic model attempting to do everything.

The practical advantages are significant:

Specialization: Each agent can be tuned and prompted for its specific job
Scale: Tasks that exceed a single context window can be split across agents
Parallelism: Independent subtasks run simultaneously, cutting total wall time
Resilience: When one agent fails, others can compensate or retry

Antigravity provides strong first-class support for multi-agent development. With AgentKit 2.0 integration, agent-to-agent communication, state management, and task distribution are now substantially easier to implement.

Why Single Agents Hit a Ceiling

Before diving into implementation, it's worth understanding where single agents break down:

Context window limits — Even Gemini 2.5 Pro's 1M-token window overflows on large codebases or long-running projects.

Generalist performance — A general-purpose agent does everything at average quality; specialized agents approach expert quality in their domains.

Sequential processing — A single agent works through tasks one at a time; a multi-agent system runs parallel workstreams.

No failover — If a single agent gets stuck or fails, the entire pipeline stops.

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WHAT YOU'LL LEARN

✦Developers who've been stuck on multi-agent design can now architect agent roles, communication flows, and task delegation in Antigravity from day one

✦Get working code for task decomposition, parallel execution, and error recovery patterns — real implementations you can adapt for your own production system

✦Learn production-grade monitoring, debugging, and scaling strategies that take your automation from prototype to a system that runs reliably at scale

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Core Multi-Agent Architecture Patterns

Pattern 1: Hierarchical (Most Common)

An orchestrator agent manages the overall task and delegates to worker agents.

Orchestrator Agent
├── Researcher Agent   (information gathering)
├── Analyst Agent      (pattern extraction and insight)
├── Writer Agent       (document creation)
└── Reviewer Agent     (quality assurance)

Best for: Content pipelines, research report generation, automated code review

Pattern 2: Pipeline

Each agent processes the output of the previous one in a sequential chain.

Input → [Preprocessor] → [Transformer] → [Validator] → Output

Best for: Data transformation, multi-stage translation, test generation pipelines

Pattern 3: Parallel

Multiple agents tackle the same problem from different angles simultaneously; an aggregator synthesizes the results.

             ┌→ Agent A (approach 1) →┐
Master   →   ├→ Agent B (approach 2) → → Aggregator → Output
             └→ Agent C (approach 3) →┘

Best for: Multi-perspective document evaluation, A/B experiment execution, distributed data collection

Implementation in Antigravity: Step by Step

Step 1: Design Your Project Structure

my-multi-agent-project/
├── agents/
│   ├── orchestrator.md     ← Master agent instructions
│   ├── researcher.md       ← Research agent instructions
│   ├── analyst.md          ← Analysis agent instructions
│   └── writer.md           ← Writing agent instructions
├── tools/
│   ├── web_search.py
│   ├── file_reader.py
│   └── data_processor.py
├── prompts/
│   └── task_templates/
└── main.py

Step 2: Define Agent Roles in AGENTS.md

# Multi-Agent System Configuration
 
## Orchestrator Agent
Role: Coordinate overall execution, decompose tasks, manage quality
Authority: Assign tasks to all worker agents, query status
Tools: task_manager, status_monitor
 
## Researcher Agent
Role: Information gathering, data retrieval, raw source organization
Authority: Read-only access to external data sources
Constraint: No write operations to external systems
Tools: web_search, url_fetch, file_reader
 
## Analyst Agent
Role: Analyze collected information, extract patterns, generate insights
Input: Data from Researcher Agent
Tools: data_processor, chart_generator
 
## Writer Agent
Role: Create final report, handle formatting, tailor for audience
Authority: Write access to designated output directories only
Input: Insights from Analyst Agent
Tools: document_writer, markdown_formatter

Step 3: Implement Agent Communication

from antigravity_sdk import AgentOrchestrator, Agent, Task
 
# Define agents
orchestrator = Agent(
    name="orchestrator",
    instruction_file="agents/orchestrator.md",
    model="gemini-2.5-pro-latest",  # High-quality reasoning required
)
 
researcher = Agent(
    name="researcher",
    instruction_file="agents/researcher.md",
    model="gemini-2.5-flash",       # Speed prioritized
    tools=["web_search", "url_fetch"]
)
 
analyst = Agent(
    name="analyst",
    instruction_file="agents/analyst.md",
    model="gemini-2.5-pro-latest",
    tools=["data_processor"]
)
 
writer = Agent(
    name="writer",
    instruction_file="agents/writer.md",
    model="gemini-2.5-flash",
    tools=["document_writer"]
)
 
# Register all agents with the orchestrator
orch = AgentOrchestrator(
    master=orchestrator,
    workers=[researcher, analyst, writer]
)
 
# Execute the task
result = orch.run(
    task="Research 2026 generative AI market trends and write an investor briefing report",
    output_format="markdown",
    max_iterations=10
)
 
print(result.content)

Step 4: Parallel Task Execution

import asyncio
from antigravity_sdk import ParallelAgentRunner
 
async def run_parallel_analysis(topic: str) -> dict:
    """
    Analyze a topic from three perspectives simultaneously.
    Reduces total execution time by ~3x compared to sequential.
    """
    runner = ParallelAgentRunner()
    
    tasks = [
        runner.submit(
            agent="analyst",
            task=f"Technical analysis of {topic}",
            context={"perspective": "technical"}
        ),
        runner.submit(
            agent="analyst",
            task=f"Business and market analysis of {topic}",
            context={"perspective": "business"}
        ),
        runner.submit(
            agent="analyst",
            task=f"Risk and challenge analysis of {topic}",
            context={"perspective": "risks"}
        )
    ]
    
    # Run all three in parallel
    results = await asyncio.gather(*tasks)
    
    # Synthesize with aggregator agent
    aggregator = runner.get_agent("aggregator")
    final_report = await aggregator.synthesize(results)
    
    return final_report
 
report = asyncio.run(run_parallel_analysis("Quantum computing commercialization"))

Error Handling and Recovery

Production multi-agent systems need robust error handling at every layer.

from antigravity_sdk import AgentOrchestrator, AgentError, RetryPolicy
 
class ResilientOrchestrator:
    """
    Fault-tolerant multi-agent orchestrator with error recovery.
    """
    
    def __init__(self):
        self.orchestrator = AgentOrchestrator(...)
        self.retry_policy = RetryPolicy(
            max_retries=3,
            backoff_factor=2.0,
            retry_on=[AgentError.TIMEOUT, AgentError.RATE_LIMIT]
        )
        self.fallback_agents = {
            "researcher": "researcher_backup",
        }
    
    async def run_with_recovery(self, task: str) -> dict:
        """Execute task with automatic error recovery."""
        for attempt in range(self.retry_policy.max_retries):
            try:
                return await self.orchestrator.run(task)
                
            except AgentError.CONTEXT_OVERFLOW as e:
                # Context exceeded — split the task and retry
                subtasks = self._split_task(task, e.failed_agent)
                return await self._run_subtasks(subtasks)
                
            except AgentError.AGENT_FAILURE as e:
                # Agent failure — swap in the fallback agent
                failed_name = e.agent_name
                if failed_name in self.fallback_agents:
                    self.orchestrator.replace_agent(
                        failed_name,
                        self.fallback_agents[failed_name]
                    )
                    
            except AgentError.TIMEOUT:
                wait_time = self.retry_policy.backoff_factor ** attempt
                print(f"Timeout. Retrying in {wait_time}s...")
                await asyncio.sleep(wait_time)
        
        raise Exception("Max retries exceeded")
    
    def _split_task(self, task: str, failed_agent: str) -> list[str]:
        """Split an overflowing task into semantically coherent subtasks."""
        ...

Shared State and Memory

Sharing information between agents requires a well-designed state layer.

from antigravity_sdk import SharedMemory
 
shared_memory = SharedMemory(
    backend="redis",   # or "sqlite" / "in-memory"
    namespace="project-x"
)
 
class ResearchAgent:
    def __init__(self, memory: SharedMemory):
        self.memory = memory
    
    async def research(self, topic: str) -> None:
        results = await self._web_search(topic)
        # Write results to shared memory for other agents to consume
        await self.memory.set(
            key=f"research:{topic}",
            value=results,
            ttl=3600  # 1-hour cache
        )
 
class AnalystAgent:
    def __init__(self, memory: SharedMemory):
        self.memory = memory
    
    async def analyze(self, topic: str) -> str:
        # Read from shared memory — no direct coupling to ResearchAgent
        research_data = await self.memory.get(f"research:{topic}")
        if not research_data:
            raise ValueError(f"Research data for '{topic}' not found in shared memory")
        
        return await self._analyze(research_data)

Production Monitoring and Observability

Structured Logging

from antigravity_sdk import AgentLogger
 
agent_logger = AgentLogger(
    output="./logs/agent_runs.jsonl",
    include_prompts=True,
    include_responses=True,
    include_tool_calls=True,
)
 
# Analyze cost from logs
from antigravity_sdk import CostAnalyzer
 
analyzer = CostAnalyzer("./logs/agent_runs.jsonl")
report = analyzer.generate_report()
print(f"Total tokens consumed: {report.total_tokens:,}")
print(f"Estimated cost: ${report.estimated_cost:.4f}")
print(f"Most expensive agent: {report.most_expensive_agent}")

Performance Optimization Checklist

Model selection: Use high-capability models only where judgment is required; use fast/small models for routine tasks
Parallelize aggressively: Any tasks without data dependencies should run concurrently
Cache aggressively: Identical queries within a session should never make redundant API calls
Minimize agent context: Pass only the information each agent actually needs

Real-World Use Cases

Use Case 1: Automated Code Review Pipeline

Pull request opened
  → [Syntax Agent]   Check for syntax errors
  → [Security Agent] Scan for vulnerabilities         (parallel)
  → [Perf Agent]     Detect performance issues        (parallel)
  → [Style Agent]    Enforce coding standards          (parallel)
  → [Aggregator]     Synthesize feedback from all agents
  → Post consolidated review comment to PR

Use Case 2: Competitive Intelligence Report

Analysis request
  → [Data Collector] Gather public info per competitor (parallel × N)
  → [Analyst Agent]  Analyze each company's data
  → [Trend Agent]    Identify industry-wide trends
  → [Strategy Agent] Extract strategic implications
  → [Writer Agent]   Produce executive briefing document
  → Auto-distribute via Slack or email

A Note from an Indie Developer

Key Takeaways

Building a multi-agent system in Antigravity follows a clear progression:

Choose a pattern — hierarchical, pipeline, or parallel based on your task structure
Define roles clearly — use AGENTS.md to specify each agent's responsibilities and constraints
Prototype incrementally — start with 2–3 agents, verify the handoffs work, then expand
Implement error recovery — retry logic, fallback agents, and task splitting are non-optional in production
Monitor continuously — structured logs, cost tracking, and anomaly alerts keep you in control

Multi-agent systems look complex from the outside, but with the right architecture patterns and an incremental approach, they're well within reach for individual developers and small teams.

For advanced orchestration patterns, the multi-agent orchestration guide covers production-scale deployments in depth.

Thank You for Reading

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