Building Scalable AI Agent Architectures: Lessons Learned

When building AI agents for production, the difference between a prototype and a scalable system lies in the architectural decisions you make early on. This post shares our learnings from building and scaling AI agents at Toffu.

The Challenge of State Management

One of the biggest challenges in AI agent architectures is managing state across multiple interactions and tools. Unlike traditional applications where state is often ephemeral, AI agents need to maintain context across:

• Multiple conversation turns
• Tool executions and their results
• Error recovery scenarios
• Concurrent user sessions

Our Approach: Event-Driven State

We've found that treating agent state as a series of events works incredibly well:

interface AgentEvent {
  id: string;
  type: 'tool_call' | 'llm_response' | 'user_input' | 'error';
  timestamp: Date;
  data: any;
  metadata?: Record<string, any>;
}

class AgentState {
  private events: AgentEvent[] = [];
  
  addEvent(event: AgentEvent) {
    this.events.push(event);
    // Persist to storage
    this.persistState();
  }
  
  getContext(): string {
    return this.events
      .filter(e => e.type !== 'error')
      .map(e => this.formatEventForContext(e))
      .join('\n');
  }
}

Tool Orchestration Patterns

The real power of AI agents comes from their ability to use tools effectively. We've identified several key patterns:

1. Sequential Tool Chains

For predictable workflows where tools have clear dependencies:

const workflow = new SequentialChain([
  new DataExtractionTool(),
  new ValidationTool(),
  new TransformationTool(),
  new StorageTool()
]);

2. Parallel Tool Execution

For independent operations that can run concurrently:

const results = await Promise.allSettled([
  webSearchTool.search(query),
  databaseTool.query(filters),
  apiTool.fetchData(params)
]);

Error Recovery and Resilience

Production AI agents must handle failures gracefully. Our error recovery strategy includes:

Circuit Breakers

Prevent cascade failures when external tools are down:

class ToolCircuitBreaker {
  private failureCount = 0;
  private lastFailureTime?: Date;
  private readonly threshold = 5;
  private readonly timeout = 60000; // 1 minute
  
  async execute<T>(operation: () => Promise<T>): Promise<T> {
    if (this.isOpen()) {
      throw new Error('Circuit breaker is open');
    }
    
    try {
      const result = await operation();
      this.onSuccess();
      return result;
    } catch (error) {
      this.onFailure();
      throw error;
    }
  }
}

Graceful Degradation

When tools fail, provide alternative responses:

async function executeWithFallback(primaryTool: Tool, fallbackResponse: string) {
  try {
    return await primaryTool.execute();
  } catch (error) {
    logger.warn(`Primary tool failed: ${error.message}`);
    return fallbackResponse;
  }
}

Performance Optimization

Streaming Responses

For better user experience, stream responses as they're generated:

async function* streamAgentResponse(input: string) {
  const context = await buildContext(input);
  
  for await (const chunk of llm.stream(context)) {
    if (chunk.type === 'text') {
      yield chunk.content;
    } else if (chunk.type === 'tool_call') {
      const result = await executeTool(chunk);
      yield `\n\nTool Result: ${result}\n\n`;
    }
  }
}

Caching Strategies

Implement smart caching for expensive operations:

class LRUCache<T> {
  private cache = new Map<string, { value: T; timestamp: number }>();
  private readonly maxSize: number;
  private readonly ttl: number;
  
  set(key: string, value: T): void {
    if (this.cache.size >= this.maxSize) {
      const firstKey = this.cache.keys().next().value;
      this.cache.delete(firstKey);
    }
    
    this.cache.set(key, { value, timestamp: Date.now() });
  }
  
  get(key: string): T | null {
    const item = this.cache.get(key);
    if (!item) return null;
    
    if (Date.now() - item.timestamp > this.ttl) {
      this.cache.delete(key);
      return null;
    }
    
    return item.value;
  }
}

Monitoring and Observability

Production AI agents need comprehensive monitoring:

Metrics to Track

• Response latency (p50, p95, p99)
• Tool success/failure rates
• Token usage and costs
• User satisfaction scores

Structured Logging

Use structured logging for better debugging:

logger.info('Agent execution started', {
  sessionId,
  userId,
  inputLength: input.length,
  tools: availableTools.map(t => t.name)
});

logger.info('Tool execution completed', {
  sessionId,
  toolName,
  duration: Date.now() - startTime,
  success: true,
  tokensUsed
});

Key Takeaways

1. State management is critical - Treat it as a first-class concern
2. Design for failure - Things will go wrong, plan for it
3. Monitor everything - You can't improve what you don't measure
4. Stream when possible - Better UX leads to better adoption
5. Cache intelligently - Reduce costs and improve performance

Building production-ready AI agents is challenging but incredibly rewarding. The patterns we've shared here have served us well, but remember that every use case is different. Start simple, measure everything, and iterate based on real user feedback.

What architectural patterns have you found effective for AI agents? Share your experiences in the comments below.