There's a moment in every Antigravity project where a single-agent workflow stops being sufficient. The task grows complex, the context window fills up, and output quality starts drifting. That's when you have to decide whether to move to multi-agent architecture.
But more agents doesn't automatically mean better results. Each additional agent adds coordination overhead, makes debugging harder, and introduces new failure modes. This guide covers when multi-agent design is actually the right call — and how to build systems that hold together once you get there.
Recognizing the Limits of Single-Agent Workflows
Before splitting into multiple agents, check whether you've actually hit a real ceiling. Most problems that look like multi-agent requirements can be solved with better prompting and incremental processing.
Three signs that suggest a real migration need:
Context window pressure: You're processing enough information that output quality degrades halfway through a run. Resetting mid-process produces the same result — the agent is simply being asked to hold more than it can.
Mixed responsibilities: A single agent is handling "fetch → analyze → format → notify" in one run. As these responsibilities grow, the prompt becomes unmanageable and behavior gets unpredictable. Separating responsibilities makes each agent's behavior easier to reason about.
Real parallelization benefit: Independent subtasks are running sequentially when they could run in parallel, and the wait time is noticeable. This is worth fixing — but only if the coordination cost is lower than the time you'd save.
If none of these apply, keep the single-agent design and refine it.
The Orchestrator-Worker Pattern
This is the foundation of most multi-agent designs: one orchestrator plans the work, workers execute it.
// Orchestrator agent definition (Antigravity workflow)
const orchestrator = {
name: "research_orchestrator",
prompt: `
You are the orchestrator for a research project.
Receive the user's request and delegate tasks to these workers:
Available workers:
- web_researcher: Web search and information gathering
- data_analyzer: Analysis and structuring of collected data
- report_writer: Final report generation
Return task assignments as JSON:
{
"tasks": [
{ "worker": "web_researcher", "task": "...", "priority": 1 },
{ "worker": "data_analyzer", "task": "...", "priority": 2 }
]
}
`
};The critical rule: the orchestrator does no actual processing. It decides who does what — nothing else. Workers handle all execution.
When orchestrators do real work alongside coordination, responsibilities blur and debugging becomes painful. Keep the separation strict.
State Management: The Most Common Failure Point
State is where multi-agent systems break most often. Agent A produces intermediate results, Agent B consumes them, Agent C depends on B's output — when state gets corrupted anywhere in that chain, the final output is silently wrong.
Immutable intermediate state
Write each agent's output to its own slot instead of overwriting:
// Wrong: state gets overwritten
workflow.state.data = await researcher.run(query);
workflow.state.data = await analyzer.run(workflow.state.data); // original gone
// Right: each step persists separately
workflow.state.research_results = await researcher.run(query);
workflow.state.analysis_results = await analyzer.run(workflow.state.research_results);This approach lets you trace every agent's inputs and outputs after the fact. When something goes wrong, you can inspect exactly what each agent received and produced.
Validation at handoff points
Validate data before passing it between agents:
const researchOutput = await researcher.run(query);
if (\!researchOutput.sources || researchOutput.sources.length === 0) {
throw new WorkflowError(
"researcher_output_invalid",
"No sources returned — adjust the query and retry",
{ query, output: researchOutput }
);
}
const analysisInput = {
sources: researchOutput.sources,
context: researchOutput.summary,
};Without validation, errors surface several steps later and tracing them back to the source is tedious.
Error Recovery Design
In multi-agent systems, the right recovery strategy depends on why something failed. Retrying everything uniformly is wasteful at best and misleading at worst.
async function runWithRecovery(agent, input, options = {}) {
const {
maxRetries = 3,
retryDelay = 1000,
fallbackAgent = null,
onFailure = null,
} = options;
let lastError;
for (let attempt = 1; attempt <= maxRetries; attempt++) {
try {
return await agent.run(input);
} catch (error) {
lastError = error;
// Don't retry input errors — retrying won't help
if (error.type === "invalid_input") break;
// Rate limits: wait longer before retrying
if (error.type === "rate_limit") {
await sleep(retryDelay * attempt * 2);
continue;
}
if (attempt < maxRetries) await sleep(retryDelay * attempt);
}
}
if (fallbackAgent) {
try {
return await fallbackAgent.run(input);
} catch (_) {
// fallback also failed
}
}
if (onFailure) return onFailure(lastError, input);
throw lastError;
}The fallback agent pattern is particularly effective: pair a high-accuracy but slow agent with a faster, lower-accuracy one. On timeout, fall back to the faster option. Users get a result rather than an error, and you can flag the lower-quality output for review.
Parallel Execution and Aggregation
When running independent subtasks in parallel, decide upfront how you'll handle partial failures.
async function runParallelResearch(topics) {
const tasks = topics.map(topic => ({
id: topic.id,
promise: runWithRecovery(
researchAgent,
{ topic: topic.query },
{ maxRetries: 2, fallbackAgent: lightResearchAgent }
)
}));
// allSettled allows partial success
const results = await Promise.allSettled(tasks.map(t => t.promise));
const successful = [];
const failed = [];
results.forEach((result, index) => {
if (result.status === "fulfilled") {
successful.push({ id: tasks[index].id, data: result.value });
} else {
failed.push({ id: tasks[index].id, error: result.reason });
}
});
// Require a minimum success threshold
if (successful.length < Math.ceil(topics.length * 0.7)) {
throw new WorkflowError(
"insufficient_results",
`Not enough parallel results (success: ${successful.length}/${topics.length})`
);
}
return { successful, failed };
}Promise.allSettled over Promise.all: in a real research workflow with ten topics, one or two failures shouldn't abort everything. Partial results with clear failure reporting are more useful than demanding 100% success.
Debugging Multi-Agent Systems
Multi-agent bugs tend to fall into recognizable patterns.
Silent degradation: Output isn't fully broken — it just slowly gets worse. A specific agent is misinterpreting context, but downstream agents partially compensate, masking the problem.
How to diagnose: log each agent's inputs and outputs individually. Define one or two quality metrics and track them per agent. You'll find exactly where degradation begins.
Non-deterministic failures: The same input fails roughly one in five times. Usually tied to parallel execution timing or rate limits.
How to diagnose: run the same input 10–20 times and record failure rates and timing. If failures cluster around specific conditions (high load, particular input shapes), narrow from there.
Context contamination: Previous run state bleeds into the current run. Common in stateful agents.
How to diagnose: verify that state resets completely at workflow start. In tests, intentionally start from a "contaminated" state and confirm that the reset actually clears it.
The Three Principles Worth Keeping
After building and debugging a variety of multi-agent workflows in Antigravity, I keep coming back to the same rules:
-
Keep responsibility boundaries sharp. Orchestrators plan. Workers execute. Don't mix them.
-
Make state traceable. Never overwrite intermediate results. Validate at every handoff point.
-
Design for failure from the start. Retry logic, fallbacks, and partial-success tolerance are much harder to bolt on later.
Keeping complex workflows maintainable comes down to clarity more than clever design. Fewer agents with well-defined responsibilities outlast systems with more agents and blurry boundaries.
For deeper coverage — dynamic agent generation, self-healing workflows, production monitoring — that's in the Antigravity Lab membership articles. Worth checking if you're building something that needs to run reliably in production.