AGENTIC AI ARCHITECTURES

Technical Reference for Cloudflare Workers & Durable Objects

OVERVIEW

Four production-tested agentic AI architectures implemented with Cloudflare Workers and Durable Objects. Each pattern provides concrete implementation strategies for edge-distributed AI systems.

Two-tier hierarchies outperform complex multi-level systems. Stateless subagents enable horizontal scaling. MapReduce patterns excel at parallel processing. Consensus mechanisms improve decision quality at the cost of latency.

ARCHITECTURE PATTERNS

SYSTEM DIAGRAM

TWO-TIER MODEL

Primary agent orchestrates stateless subagents

USE CASE:

General-purpose agent systems with clear task delegation

ADVANTAGES:

+ Simple to implement
+ Easy to debug
+ Predictable behavior

DISADVANTAGES:

- Limited complexity handling
- Single point of failure

CLOUDFLARE IMPLEMENTATION:

Primary agent runs in Worker, subagents as separate Worker invocations

CORE PRINCIPLES

STATELESS BY DEFAULT

Subagents are pure functions. Same input produces same output. No memory, context, or state between calls.

CLEAR BOUNDARIES

Explicit task definitions and structured responses. Clear boundaries between agents with no ambiguity.

FAIL FAST

Quick failure detection with graceful degradation. Always return useful results, even when operations fail.

OBSERVABLE EXECUTION

Track all operations. Monitor success rates and performance metrics. Debug with data-driven insights.

TWO-TIER MODEL

PRIMARY AGENTS

Handle conversations and maintain context
Break down complex tasks into simple ones
Orchestrate subagent communication
Act as orchestrators that coordinate subagent execution

SUBAGENTS

Execute single, well-defined tasks
Get task → Complete task → Return results
No memory or context between calls, pure function execution
Can run in parallel without conflicts

User → Primary Agent (maintains context)
 ├─→ Research Agent (finds relevant feedback)
 ├─→ Analysis Agent (processes sentiment)  
 └─→ Summary Agent (creates reports)

ORCHESTRATION PATTERNS

SEQUENTIAL PIPELINE

Each output feeds the next input. Suitable for multi-step processes.

Agent A → Agent B → Agent C → Result

Use for: Report generation, data processing chains, multi-step analysis

MAPREDUCE PATTERN

Split work across multiple agents, combine results. Suitable for large-scale analysis.

 ┌→ Agent 1 ─┐
Input ─┼→ Agent 2 ─┼→ Reducer → Result
 └→ Agent 3 ─┘

Use for: Processing 1000+ feedback items, bulk data analysis, parallel computation

CONSENSUS PATTERN

Multiple agents solve the same problem, compare answers. Suitable for critical decisions.

 ┌→ Agent 1 ─┐
Task ─┼→ Agent 2 ─┼→ Voting/Merge → Result
 └→ Agent 3 ─┘

Use for: Sentiment analysis, quality checks, critical business decisions

COMMUNICATION PROTOCOLS

PRIMARY → SUBAGENT

{
  "task": "Analyze sentiment in 50 items",
  "context": "Focus on mobile app feedback", 
  "data": [...],
  "constraints": {
    "max_processing_time": 5000,
    "output_format": "structured_json"
  }
}

SUBAGENT → PRIMARY

{
  "status": "complete",
  "result": {
    "positive": 32,
    "negative": 8, 
    "neutral": 10,
    "top_themes": ["navigation", "performance"]
  },
  "confidence": 0.92,
  "processing_time": 3200
}

CRITICAL RULES

No conversation or context sharing between calls
Task in, result out. Pure function behavior
Always include status, confidence, and metadata
Structured data only. No natural language responses

PERFORMANCE & MONITORING

OPTIMIZATION

Model Selection: Haiku for simple, Sonnet for complex, Opus for critical
Parallel Execution: Run 5-10 agents simultaneously
Caching: Cache by prompt hash, saves 40% of API calls
Batching: Process 50 items in one call vs 50 separate calls

MONITORING

Task Success Rate: Are agents completing tasks?
Response Quality: Confidence scores, validation rates
Performance: Latency, token usage, cost
Error Patterns: What's failing and why?

EXECUTION TRACE FORMAT

Primary Agent Start [12:34:56]
 ├─ Feedback Analyzer Called
 │ ├─ Time: 2.3s
 │ ├─ Tokens: 1,250  
 │ └─ Status: Success
 ├─ Sentiment Processor Called
 │ ├─ Time: 1.8s
 │ ├─ Tokens: 890
 │ └─ Status: Success
 └─ Total Time: 4.5s, Total Cost: $0.03

COMMON PITFALLS

THE "SMART AGENT" TRAP

Agents that attempt to "figure out" what to do. Be explicit about all requirements and constraints.

STATE CREEP

Adding state incrementally leads to complexity and system failures.

DEEP HIERARCHY

Deep agent hierarchies create debugging and maintenance challenges.

CONTEXT EXPLOSION

Passing excessive context to every agent increases costs and complexity.

IMPLEMENTATION PRINCIPLES

STATELESS DESIGN

• Subagents as pure functions
• No shared memory between calls
• Predictable input/output behavior
• Horizontal scaling capability

EDGE DISTRIBUTION

• Workers handle orchestration
• Durable Objects maintain state
• Regional data locality
• Sub-100ms response times

ERROR HANDLING

• Graceful degradation chains
• Exponential backoff retry
• Partial result recovery
• Circuit breaker patterns

MONITORING

• Task completion rates
• Response quality metrics
• Latency distribution
• Cost per operation

CLOUDFLARE WORKERS IMPLEMENTATION

// Primary Agent Worker

export default {
  async fetch(request, env) {
    const orchestrator = new AgentOrchestrator(env);
    const task = await request.json();
    
    // Route to appropriate architecture
    switch(task.pattern) {
      case 'two-tier':
        return await orchestrator.executeTwoTier(task);
      case 'pipeline':
        return await orchestrator.executePipeline(task);
      case 'mapreduce':
        return await orchestrator.executeMapReduce(task);
      case 'consensus':
        return await orchestrator.executeConsensus(task);
    }
  }
}

// Durable Object State Management

export class AgentState {
  constructor(state, env) {
    this.state = state;
    this.env = env;
  }
  
  async handleRequest(request) {
    const data = await request.json();
    
    // Store execution context
    await this.state.storage.put('context', data.context);
    
    // Track agent performance
    const metrics = await this.state.storage.get('metrics') || {};
    metrics[data.agentId] = {
      lastRun: Date.now(),
      successRate: data.success ? 
        (metrics[data.agentId]?.successRate || 0) + 1 : 
        metrics[data.agentId]?.successRate || 0
    };
    
    await this.state.storage.put('metrics', metrics);
    return new Response(JSON.stringify({ status: 'stored' }));
  }
}

PERFORMANCE BENCHMARKS

ARCHITECTURE	LATENCY (P95)	THROUGHPUT	COST/1K OPS
Two-Tier	150ms	2,000 ops/min	$0.12
Sequential	450ms	800 ops/min	$0.18
MapReduce	200ms	5,000 ops/min	$0.08
Consensus	300ms	1,200 ops/min	$0.24

CONCLUSIONS

Two-tier architectures provide the optimal balance of simplicity and capability for most production use cases. The primary/subagent pattern maps naturally to Cloudflare's Worker/Durable Object model.

Stateless subagents are non-negotiable for horizontal scaling. Any attempt to maintain state within subagents introduces complexity that outweighs benefits.

MapReduce patterns excel when processing large datasets in parallel. The edge distribution of Cloudflare Workers makes this particularly effective for geographically distributed workloads.

Consensus mechanisms should be reserved for critical decisions where accuracy outweighs latency concerns. The 2x latency penalty is significant.