Day 13: AI Agent Architecture - Under the Hood of Autonomous Systems

May 08, 2026

Day 13: AI Agent Architecture - Under the Hood of Autonomous Systems

Today we're diving deep into agent architecture. We've explored what AI agents can do, but how do they actually work underneath?

Agent Architecture Fundamentals

An AI agent isn't just an LLM. It's a system architecture with multiple components working together.

Core Components

1. Planning System What it does: Breaks down high-level goals into executable steps
Why it matters: Without planning, agents can't handle complex tasks

2. Memory System What it does: Stores and retrieves past experiences, facts, learnings
Why it matters: Prevents constant re-learning, enables context awareness

3. Action System What it does: Executes tasks through tools and APIs
Why it matters: Enables real-world impact beyond text generation

4. Reflection Loop What it does: Reviews outcomes, learns from mistakes
Why it matters: Continuous improvement through experience

The Planning Process

class PlanningEngine {
  async plan(goal: string, context: Context): Promise<Plan> {
    // Decompose goal into subtasks
    const subtasks = await this.decompose(goal);
    
    // Prioritize based on dependencies
    const ordered = this.topologicalSort(subtasks);
    
    // Generate execution plan
    return new Plan(ordered, context);
  }
  
  async decompose(goal: string): Promise<Subtask[]> {
    // Use LLM to break down complex tasks
    const response = await this.llm.generate({
      prompt: `
        Break down this goal into executable subtasks:
        Goal: ${goal}
        
        Return JSON array of subtasks with:
        - id: unique identifier
        
        Example output:
        [
          {"id": "step-1", "description": "Research topic", "prerequisites": []},
          {"id": "step-2", "description": "Write content", "prerequisites": ["step-1"]}
        ]`
    });
    
    return JSON.parse(response);
  }
}

Planning Strategies

Chain-of-Thought: Step-by-step reasoning
Tree of Thoughts: Explore multiple reasoning paths
Recursive Planning: Break tasks recursively until executable

Memory System Architecture

Three-Layer Memory Model

Layer 1: Episodic Memory

  • Stores raw interaction logs
  • Enables debugging and traceability
  • Indexed for temporal queries

Layer 2: Semantic Memory

  • Vector-based similarity search
  • Semantic understanding of contexts
  • Cross-session knowledge transfer

Layer 3: Summary Memory

  • High-level abstractions
  • Reduced context window usage
  • Quick retrieval of key learnings
class MemorySystem {
  private episodic: EpisodicStore;
  private semantic: VectorStore;
  private summary: SummaryStore;
  
  async storeExperience(interaction: Interaction): Promise<void> {
    // Store raw interaction
    const id = await this.episodic.store({
      timestamp: new Date(),
      ...interaction
    });
    
    // Create semantic embedding
    const embedding = await this.generateEmbedding(interaction);
    await this.semantic.insert(id, embedding);
    
    // Generate summary periodically
    if (this.shouldSummarize()) {
      this.summarizeSession();
    }
  }
  
  async retrieve(queries: Query[]): Promise<MemoryItem[]> {
    // Multi-source retrieval
    const results = [];
    for (const query of queries) {
      results.push(...await this.semantic.search(query));
      results.push(...await this.summary.search(query));
    }
    
    // Rank and merge results
    return this.rankResults(results);
  }
}

Action System Design

Tool Interface Standard

interface Tool {
  name: string;
  description: string;
  parameters: Schema;
  execute: (args: Record<string, any>) => Promise<any>;
}

const FileSystemAccess: Tool = {
  name: 'filesystem_access',
  description: 'Read and write files in specified directories',
  parameters: {
    operation: 'read' | 'write' | 'delete',
    path: 'string',
    content?: 'string'
  },
  execute: async (args) => {
    // Execute file operation with safety checks
    await validatePermissions(args.path);
    return await this.operation(args);
  }
};

Tool Selection Process

1. Intent Recognition: What does the agent need to do?
2. Tool Matching: Which tools can accomplish this?
3. Parameter Generation: What inputs are needed?
4. Execution Planning: In what order should tools run?

Feedback Loops

Reinforcement Learning from Experience

Self-Reflection Loop:

  1. Execute action
  2. Observe outcome
  3. Compare to expected result
  4. Update policy for future actions
class ReflectionLoop {
  async reflect(action: Action, outcome: Outcome): Promise<void> {
    // Generate reflection
    const reflection = await this.llm.generate({
      prompt: `
        Analyze the outcome:
        Action: ${JSON.stringify(action)}
        Expected: ${action.expectedOutcome}
        Actual: ${JSON.stringify(outcome)}
        
        What went well? What needs improvement?
        What would you do differently next time?
      `
    });
    
    // Update stored experience
    await this.memorize({
      action,
      outcome,
      reflection
    });
    
    // Adjust future planning
    this.updatePolicy(reflection);
  }
}

Safety Guardrails

class SafetyGuard {
  async validateAction(action: Action): Promise<boolean> {
    // Security checks
    if (!this.hasPermission(action)) return false;
    if (this.isHighRisk(action)) {
      return await this.humanApproval(action);
    }
    return true;
  }
  
  isHighRisk(action: Action): boolean {
    const highRiskOperations = ['delete', 'write', 'execute'];
    return highRiskOperations.includes(action.operation);
  }
}

Architecture Patterns

Event-Driven Architecture

Benefits:

  • Loose coupling between components
  • Easy to add new tools
  • Scalable to multiple agents
  • Clear separation of concerns

Key Patterns:

  • Event Bus: Central message passing
  • Event Handlers: Component-specific processors
  • Event Sources: External triggers

State Management

Current State includes:

  • Active goals and subtasks
  • Working memory contents
  • Recent actions and outcomes
  • Current tool contexts

State Transitions:

  1. Planning: Goal → Subtasks
  2. Execution: Subtask → Action
  3. Reflection: Outcome → Learnings
  4. Iteration: Learnings → Updated goals

Practical Considerations

Context Window Management

Problem: LLMs have limited context
Solutions:

  • Summary memories for long-term context
  • Hierarchical task breakdown
  • Selective retrieval from memory

Performance Optimization

Latency Reduction:

  • Parallel tool execution when possible
  • Caching for repeated queries
  • Streaming responses for long operations

Cost Control:

  • Token budget tracking
  • Summary generation to reduce context
  • Batch processing where possible

Next Steps

Day 14 focuses on practical AI agent adoption for everyday people - how regular people can use autonomous agents to simplify their daily routines without technical expertise.

Join us for Day 14 to see practical AI agent applications for everyone!

← Previous PostNext Post →