Create Your First AI Agent: Complete Step-by-Step Guide

Building AI agents feels like magic when it works - and like wrestling with a black box when it doesn't. If you're among the 63% of developers who struggle with unpredictable AI behavior, this tutorial will transform your approach. We'll build a real AI agent from scratch, implement planning modes, integrate tools, and most importantly, make it debuggable and reliable.

Prerequisites

Before we dive in, you'll need:

• Basic understanding of TypeScript/JavaScript
• Familiarity with async/await patterns
• Claude Fast installed and configured
• A project ready for agent integration

Understanding AI Agents

An AI agent isn't just a chatbot - it's an autonomous system that can:

• Understand complex objectives
• Break down tasks into steps
• Use tools to accomplish goals
• Validate its own work
• Learn from context

Let's build one that automates code reviews.

Step 1: Define Your Agent's Purpose

Every great agent starts with a clear purpose. We'll build a "Code Review Agent" that:

• Analyzes code changes
• Checks for common issues
• Suggests improvements
• Validates against project standards
• Generates review summaries

interface CodeReviewAgent {
  name: "code-reviewer"
  purpose: "Automated code review with actionable feedback"
  capabilities: [
    "Static analysis",
    "Pattern detection", 
    "Security scanning",
    "Performance analysis",
    "Style checking"
  ]
  tools: ["read", "grep", "analyze", "report"]
}

Step 2: Implement the Planning Mode

The key to avoiding the "black box" problem is making your agent explain its thinking before acting. This is called Planning Mode.

// agent-config.ts
export const codeReviewAgent = {
  name: "code-reviewer",
  systemPrompt: `You are a senior code reviewer. When given code to review:

  1. PLAN your review approach before starting
  2. Explain what you'll check and why
  3. Execute the review systematically
  4. Provide actionable feedback
  
  Always think step-by-step and explain your reasoning.`,
  
  planningPrompt: `Before reviewing code, create a plan that includes:
  - What aspects you'll review (security, performance, style, etc.)
  - What patterns you'll look for
  - How you'll prioritize issues
  - What tools you'll use`
}

Step 3: Create the Agent Class

Now let's build the actual agent implementation:

// code-review-agent.ts
import { ClaudeFastAgent } from '@claude-fast/core';

export class CodeReviewAgent extends ClaudeFastAgent {
  constructor() {
    super({
      name: 'code-reviewer',
      description: 'Performs comprehensive code reviews',
      model: 'claude-3-opus',
    });
  }

  async plan(request: ReviewRequest): Promise<ReviewPlan> {
    // Force the agent to plan before acting
    const plan = await this.think({
      prompt: `Plan a code review for these changes:
        Files: ${request.files.join(', ')}
        Context: ${request.context}
        
        Create a detailed review plan.`,
      mode: 'planning'
    });

    return this.parsePlan(plan);
  }

  async execute(plan: ReviewPlan): Promise<ReviewResult> {
    const results = {
      issues: [],
      suggestions: [],
      summary: ''
    };

    // Execute each planned check
    for (const check of plan.checks) {
      const checkResult = await this.performCheck(check);
      results.issues.push(...checkResult.issues);
      results.suggestions.push(...checkResult.suggestions);
    }

    // Generate summary
    results.summary = await this.generateSummary(results);
    
    return results;
  }

  private async performCheck(check: PlannedCheck): Promise<CheckResult> {
    // Use tools to analyze code
    const fileContent = await this.tools.read(check.file);
    
    switch (check.type) {
      case 'security':
        return this.securityCheck(fileContent);
      case 'performance':
        return this.performanceCheck(fileContent);
      case 'style':
        return this.styleCheck(fileContent);
      default:
        return this.generalCheck(fileContent);
    }
  }
}

Step 4: Integrate Tool Usage

Agents become powerful when they can use tools. Let's add tool integration:

// tools/agent-tools.ts
export class AgentTools {
  async read(filePath: string): Promise<string> {
    // Read file content
    return await fs.readFile(filePath, 'utf-8');
  }

  async grep(pattern: string, filePath: string): Promise<GrepResult[]> {
    // Search for patterns in code
    const content = await this.read(filePath);
    const lines = content.split('\n');
    const results = [];

    lines.forEach((line, index) => {
      if (new RegExp(pattern).test(line)) {
        results.push({
          file: filePath,
          line: index + 1,
          content: line,
          match: pattern
        });
      }
    });

    return results;
  }

  async analyze(code: string, rules: AnalysisRules): Promise<AnalysisResult> {
    // Perform static analysis
    return {
      complexity: this.calculateComplexity(code),
      issues: this.findIssues(code, rules),
      patterns: this.detectPatterns(code)
    };
  }
}

Step 5: Implement Validation and Feedback Loops

To ensure reliability, agents must validate their own work:

// validation/agent-validator.ts
export class AgentValidator {
  async validateReview(
    review: ReviewResult,
    originalRequest: ReviewRequest
  ): Promise<ValidationResult> {
    const validation = {
      isComplete: true,
      missingChecks: [],
      confidence: 0
    };

    // Check if all requested files were reviewed
    const reviewedFiles = new Set(review.issues.map(i => i.file));
    const missingFiles = originalRequest.files.filter(
      f => !reviewedFiles.has(f)
    );

    if (missingFiles.length > 0) {
      validation.isComplete = false;
      validation.missingChecks.push(...missingFiles);
    }

    // Calculate confidence score
    validation.confidence = this.calculateConfidence(review);

    return validation;
  }

  private calculateConfidence(review: ReviewResult): number {
    let score = 0;
    
    // More detailed feedback = higher confidence
    score += Math.min(review.issues.length * 10, 40);
    score += Math.min(review.suggestions.length * 5, 30);
    score += review.summary.length > 100 ? 30 : 15;
    
    return Math.min(score, 100);
  }
}

Step 6: Make It Observable

The biggest complaint about AI agents? "I don't know what it's doing!" Let's fix that:

// observability/agent-observer.ts
export class AgentObserver {
  private listeners: ObserverCallback[] = [];

  subscribe(callback: ObserverCallback) {
    this.listeners.push(callback);
  }

  emit(event: AgentEvent) {
    this.listeners.forEach(cb => cb(event));
  }
}

// In your agent
class ObservableCodeReviewAgent extends CodeReviewAgent {
  private observer = new AgentObserver();

  async execute(plan: ReviewPlan): Promise<ReviewResult> {
    this.observer.emit({
      type: 'execution_started',
      data: { plan }
    });

    for (const check of plan.checks) {
      this.observer.emit({
        type: 'check_started',
        data: { check }
      });

      const result = await this.performCheck(check);

      this.observer.emit({
        type: 'check_completed',
        data: { check, result }
      });
    }

    // ... rest of execution
  }
}

Step 7: Add Error Handling and Recovery

Robust agents handle failures gracefully:

// error-handling/agent-errors.ts
export class AgentErrorHandler {
  async executeWithRetry<T>(
    operation: () => Promise<T>,
    options: RetryOptions = {}
  ): Promise<T> {
    const maxRetries = options.maxRetries || 3;
    const backoff = options.backoff || 1000;

    for (let attempt = 1; attempt <= maxRetries; attempt++) {
      try {
        return await operation();
      } catch (error) {
        if (attempt === maxRetries) {
          throw new AgentExecutionError(
            `Failed after ${maxRetries} attempts`,
            error
          );
        }

        await this.delay(backoff * attempt);
        
        // Log retry attempt
        console.log(`Retry attempt ${attempt}/${maxRetries}`);
      }
    }
  }

  private delay(ms: number): Promise<void> {
    return new Promise(resolve => setTimeout(resolve, ms));
  }
}

Putting It All Together

Let's see our agent in action:

// example-usage.ts
async function runCodeReview() {
  const agent = new ObservableCodeReviewAgent();
  
  // Subscribe to events for visibility
  agent.observer.subscribe((event) => {
    console.log(`[${event.type}]`, event.data);
  });

  // Create review request
  const request: ReviewRequest = {
    files: ['src/api/users.ts', 'src/components/UserForm.tsx'],
    context: 'Adding user management features',
    standards: ['security', 'performance', 'react-best-practices']
  };

  try {
    // Plan the review
    console.log('🤔 Planning review...');
    const plan = await agent.plan(request);
    console.log('📋 Review plan:', plan);

    // Execute the review
    console.log('🔍 Executing review...');
    const result = await agent.execute(plan);

    // Validate results
    console.log('✅ Validating results...');
    const validation = await validator.validateReview(result, request);

    if (validation.confidence > 80) {
      console.log('📊 Review complete with high confidence');
      return result;
    } else {
      console.log('⚠️ Low confidence, requesting human review');
      return { ...result, needsHumanReview: true };
    }
  } catch (error) {
    console.error('❌ Review failed:', error);
    throw error;
  }
}

Advanced Patterns

Pattern 1: Multi-Agent Coordination

class ReviewCoordinator {
  async comprehensiveReview(request: ReviewRequest) {
    // Run specialized agents in parallel
    const [security, performance, style] = await Promise.all([
      this.securityAgent.review(request),
      this.performanceAgent.review(request),
      this.styleAgent.review(request)
    ]);

    // Merge and prioritize results
    return this.mergeResults([security, performance, style]);
  }
}

Pattern 2: Learning from Feedback

class LearningAgent extends CodeReviewAgent {
  private feedbackStore = new FeedbackStore();

  async incorporateFeedback(
    review: ReviewResult,
    feedback: UserFeedback
  ) {
    // Store feedback for pattern learning
    await this.feedbackStore.save({
      review,
      feedback,
      timestamp: Date.now()
    });

    // Update agent prompts based on patterns
    if (feedback.type === 'false_positive') {
      this.adjustSensitivity(feedback.issue);
    }
  }
}

Pattern 3: Context-Aware Reviews

class ContextAwareReviewAgent extends CodeReviewAgent {
  async loadProjectContext(projectPath: string) {
    const context = {
      dependencies: await this.analyzeDependencies(projectPath),
      patterns: await this.detectProjectPatterns(projectPath),
      standards: await this.loadProjectStandards(projectPath)
    };

    this.setContext(context);
  }

  async performCheck(check: PlannedCheck): Promise<CheckResult> {
    // Use project context for more accurate reviews
    const projectAwareRules = this.adaptRulesToProject(
      check.rules,
      this.context
    );

    return super.performCheck({
      ...check,
      rules: projectAwareRules
    });
  }
}

Testing Your Agent

describe('CodeReviewAgent', () => {
  it('should create detailed review plans', async () => {
    const agent = new CodeReviewAgent();
    const plan = await agent.plan(mockRequest);
    
    expect(plan.checks).toHaveLength(3);
    expect(plan.checks[0].type).toBe('security');
  });

  it('should handle tool failures gracefully', async () => {
    const agent = new CodeReviewAgent();
    agent.tools.read = jest.fn().mockRejectedValue(
      new Error('File not found')
    );

    const result = await agent.execute(mockPlan);
    expect(result.errors).toContain('File not found');
  });

  it('should provide observable execution', async () => {
    const events = [];
    agent.observer.subscribe(e => events.push(e));
    
    await agent.execute(mockPlan);
    
    expect(events).toContainEqual({
      type: 'execution_started',
      data: expect.any(Object)
    });
  });
});

Common Pitfalls and Solutions

Pitfall 1: Over-Engineering

Problem: Creating overly complex agents that are hard to debug Solution: Start simple, add complexity only when needed

Pitfall 2: Insufficient Logging

Problem: "Black box" behavior when things go wrong Solution: Log every decision, use structured observability

Pitfall 3: Rigid Patterns

Problem: Agents that can't adapt to edge cases Solution: Build in flexibility and fallback behaviors

Pitfall 4: Poor Error Messages

Problem: Cryptic failures that don't help users Solution: Provide context-rich, actionable error messages

Best Practices Checklist

What's Next?

Congratulations! You've built a production-ready AI agent that:

• Plans before acting
• Uses tools effectively
• Validates its own work
• Provides visibility into its process
• Handles errors gracefully

Next Steps:

1. Extend Your Agent: Add more specialized checks
2. Build Agent Teams: Coordinate multiple agents
3. Add Learning: Implement feedback-based improvements
4. Scale Up: Handle larger codebases and parallel execution
5. Share Your Work: Contribute to the agent ecosystem

Resources and Community

• Claude Fast Agent SDK Documentation
• Example Agents Repository
• Agent Development Best Practices
• Community Discord

Building AI agents is both an art and a science. The key is starting simple, making things observable, and iterating based on real usage. Happy building!