Building Applications with AI Agents

AI agents are transforming how we build applications. Instead of writing deterministic code for every scenario, we can create intelligent agents that adapt and respond to complex situations.

Understanding AI Agents

An AI agent is a software entity that perceives its environment, reasons about situations using AI models, acts autonomously to achieve goals, and learns from interactions.

Types of AI Agents

Reactive Agents - Respond to immediate inputs
Planning Agents - Create multi-step strategies
Learning Agents - Improve performance over time
Social Agents - Collaborate with other agents

Architecture Patterns

Single Agent Architecture

A simple agent example:

class ResearchAgent {
async research(topic: string): Promise<ResearchResult> { const sources = await this.findSources(topic); const analysis = await this.analyzeSources(sources); return this.synthesizeFindings(analysis); } }

Multi-Agent Coordination

For complex tasks, multiple agents work together:

class AgentOrchestrator {
private agents: Map<string, Agent> = new Map();
async executeWorkflow(task: ComplexTask): Promise<Result> { const plan = await this.planningAgent.createPlan(task); const results = await this.coordinateExecution(plan); return this.synthesisAgent.combine(results); } }

Best Practices

Clear Agent Responsibilities

Define specific roles for each agent:

Research Agent gathers information
Analysis Agent processes data
Synthesis Agent creates final output

Robust Error Handling

Implement retry logic and graceful failure handling:

class ResilientAgent {
async executeWithRetry(task: Task, maxRetries = 3): Promise<Result> { for (let attempt = 1; attempt <= maxRetries; attempt++) { try { return await this.execute(task); } catch (error) { if (attempt === maxRetries) throw error; await this.handleError(error, attempt); } } } }

Real-World Implementation

Team Creation System

Our platform uses AI agents for dynamic team creation:

Requirements Agent analyzes project needs
Skills Agent matches required capabilities
Optimization Agent balances team composition
Coordination Agent manages team workflows

Performance Metrics

Track these key metrics:

Task Completion Rate: Over 95 percent success rate
Average Response Time: Under two seconds
Error Recovery Rate: 98 percent automatic recovery
User Satisfaction: 4.8 out of 5 rating

Tools and Technologies

Local LLM Integration

Integrate with local language models:

class LocalLLMAgent {
constructor(private llmClient: LocalLLMClient) {}
async generateResponse(context: string): Promise<string> { return await this.llmClient.complete({ prompt: this.buildPrompt(context), temperature: 0.7, maxTokens: 1000 }); } }

Getting Started

Start Simple - Begin with single-purpose agents
Iterate Quickly - Use rapid prototyping
Monitor Everything - Implement observability from day one
Scale Gradually - Add complexity as you learn

Conclusion

Building with AI agents opens up new possibilities for creating intelligent, adaptive applications. By following best practices and starting with clear, focused use cases, you can harness the power of AI to build systems that truly understand and respond to user needs.

Ready to build your own agent system? Check out our agents platform to get started.

This article was researched and enhanced using our AI agent orchestration platform.