How Massively Decomposed Agentic Processes achieve 75% cost reduction with intelligent caching.
Massively Decomposed Agentic Processes (MDAPs) represent a breakthrough in AI system architecture. Instead of relying on a single LLM to solve complex problems, MDAPs break tasks into millions of tiny subtasks, each handled by specialized "microagents."
Recent research (arxiv.org/abs/2511.09030) demonstrated the first MDAP system to solve a 1 million step task with zero errors. The system uses:
The Challenge: While MDAPs solve the accuracy problem, they create a massive cost problem. Running even a single task can cost tens of thousands of dollars.
Microagents are specialized and focused. The same subtask patterns repeat thousands of times across different workflows.
When 3-5 agents vote on the same decision, they make identical LLM calls. Perfect for immediate cache hits.
Each microagent performs a well-defined atomic operation. Same input → same output, every time.
When error correction reruns a failed step, it makes the exact same LLM call. Caching eliminates retry costs.
A Fortune 500 company uses an MDAP system to optimize global supply chain logistics in real-time. The system processes 1M+ decision points per optimization run.
// Microagent with caching
class CachedMicroagent {
async execute(subtask) {
// Check cache first
const cached = await agentcache.get({
provider: 'openai',
model: 'gpt-4',
messages: subtask.prompt,
namespace: `mdap-${this.agentType}`
});
if (cached.hit) {
return cached.response; // 45ms, $0 cost
}
// Cache miss - call LLM
const response = await openai.chat.completions.create({
model: 'gpt-4',
messages: subtask.prompt
});
// Store for future use
await agentcache.set({
provider: 'openai',
model: 'gpt-4',
messages: subtask.prompt,
response: response.choices[0].message,
namespace: `mdap-${this.agentType}`
});
return response;
}
}
// Multi-agent voting with cache
async function multiAgentVote(subtask, agents) {
const votes = await Promise.all(
agents.map(agent => agent.execute(subtask))
);
// After first agent calls LLM, others hit cache
// 3 agents × 1M steps = 2M free cached calls
return consensus(votes);
}Key Insight: By namespacing cache keys by agent type, we ensure specialized agents benefit from their own cache while allowing cross-workflow reuse.
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