[{"_1":2,"_73":-5,"_74":-5},"loaderData",{"_3":4,"_19":20},"root",{"_5":6},"ENV",{"_7":8,"_9":10,"_11":12,"_13":14,"_15":16,"_17":18},"COGNITO_AUTHORITY","https://cognito-idp.us-east-1.amazonaws.com/us-east-1_TPvOcaNdI","COGNITO_DOMAIN","https://us-east-1tpvocandi.auth.us-east-1.amazoncognito.com","COGNITO_CLIENT_ID","5jstnjeo3lo60ueea2m3d5nubb","COGNITO_REDIRECT_URI","https://orchestrate-ai.io/auth/callback","COGNITO_SCOPE","email openid phone","COGNITO_RESPONSE_TYPE","code","routes/blog.tag.$tag",{"_21":22,"_23":24,"_60":61},"tag","architecture","posts",[25],{"_26":27,"_28":29,"_46":47,"_48":49,"_50":51},"slug","building-with-ai-agents","frontmatter",{"_30":31,"_32":33,"_34":35,"_36":37,"_38":39,"_44":45},"title","Building Applications with AI Agents","date","2024-01-20","author","Engineering Team","description","A comprehensive guide to building production-ready applications using AI agents","tags",[40,41,42,43],"AI Agents","Development","Best Practices","Architecture","featured",true,"content","\n# Building Applications with AI Agents\n\nAI 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.\n\n## Understanding AI Agents\n\nAn 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.\n\n### Types of AI Agents\n\n1. **Reactive Agents** - Respond to immediate inputs\n2. **Planning Agents** - Create multi-step strategies \n3. **Learning Agents** - Improve performance over time\n4. **Social Agents** - Collaborate with other agents\n\n## Architecture Patterns\n\n### Single Agent Architecture\n\nA simple agent example:\n\n```typescript\nclass ResearchAgent {\n async research(topic: string): Promise {\n const sources = await this.findSources(topic);\n const analysis = await this.analyzeSources(sources);\n return this.synthesizeFindings(analysis);\n }\n}\n```\n\n### Multi-Agent Coordination\n\nFor complex tasks, multiple agents work together:\n\n```typescript\nclass AgentOrchestrator {\n private agents: Map = new Map();\n \n async executeWorkflow(task: ComplexTask): Promise {\n const plan = await this.planningAgent.createPlan(task);\n const results = await this.coordinateExecution(plan);\n return this.synthesisAgent.combine(results);\n }\n}\n```\n\n## Best Practices\n\n### Clear Agent Responsibilities\n\nDefine specific roles for each agent:\n- Research Agent gathers information\n- Analysis Agent processes data \n- Synthesis Agent creates final output\n\n### Robust Error Handling\n\nImplement retry logic and graceful failure handling:\n\n```typescript\nclass ResilientAgent {\n async executeWithRetry(task: Task, maxRetries = 3): Promise {\n for (let attempt = 1; attempt <= maxRetries; attempt++) {\n try {\n return await this.execute(task);\n } catch (error) {\n if (attempt === maxRetries) throw error;\n await this.handleError(error, attempt);\n }\n }\n }\n}\n```\n\n## Real-World Implementation\n\n### Team Creation System\n\nOur platform uses AI agents for dynamic team creation:\n\n1. Requirements Agent analyzes project needs\n2. Skills Agent matches required capabilities\n3. Optimization Agent balances team composition\n4. Coordination Agent manages team workflows\n\n### Performance Metrics\n\nTrack these key metrics:\n- Task Completion Rate: Over 95 percent success rate\n- Average Response Time: Under two seconds\n- Error Recovery Rate: 98 percent automatic recovery\n- User Satisfaction: 4.8 out of 5 rating\n\n## Tools and Technologies\n\n### Local LLM Integration\n\nIntegrate with local language models:\n\n```typescript\nclass LocalLLMAgent {\n constructor(private llmClient: LocalLLMClient) {}\n \n async generateResponse(context: string): Promise {\n return await this.llmClient.complete({\n prompt: this.buildPrompt(context),\n temperature: 0.7,\n maxTokens: 1000\n });\n }\n}\n```\n\n## Getting Started\n\n1. Start Simple - Begin with single-purpose agents\n2. Iterate Quickly - Use rapid prototyping\n3. Monitor Everything - Implement observability from day one\n4. Scale Gradually - Add complexity as you learn\n\n## Conclusion\n\nBuilding 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.\n\nReady to build your own agent system? Check out our agents platform to get started.\n\n---\n\n*This article was researched and enhanced using our AI agent orchestration platform.*","compiledContent","\"use strict\";\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nfunction _createMdxContent(props) {\n const _components = {\n a: \"a\",\n code: \"code\",\n em: \"em\",\n h1: \"h1\",\n h2: \"h2\",\n h3: \"h3\",\n hr: \"hr\",\n li: \"li\",\n ol: \"ol\",\n p: \"p\",\n pre: \"pre\",\n span: \"span\",\n strong: \"strong\",\n ul: \"ul\",\n ...props.components\n };\n return _jsxs(_Fragment, {\n children: [_jsx(_components.h1, {\n id: \"building-applications-with-ai-agents\",\n children: _jsx(_components.a, {\n href: \"#building-applications-with-ai-agents\",\n children: \"Building Applications with AI Agents\"\n })\n }), \"\\n\", _jsx(_components.p, {\n children: \"AI agents are transforming how we build applications. 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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.\"\n }), \"\\n\", _jsx(_components.p, {\n children: \"Ready to build your own agent system? Check out our agents platform to get started.\"\n }), \"\\n\", _jsx(_components.hr, {}), \"\\n\", _jsx(_components.p, {\n children: _jsx(_components.em, {\n children: \"This article was researched and enhanced using our AI agent orchestration platform.\"\n })\n })]\n });\n}\nfunction MDXContent(props = {}) {\n const {wrapper: MDXLayout} = props.components || ({});\n return MDXLayout ? _jsx(MDXLayout, {\n ...props,\n children: _jsx(_createMdxContent, {\n ...props\n })\n }) : _createMdxContent(props);\n}\nreturn {\n default: MDXContent\n};\n","readingTime",{"_52":53,"_54":55,"_56":57,"_58":59},"text","3 min read","minutes",2.435,"time",146100,"words",487,"allTags",[62,40,43,42,63,41,64,65,66,67,68,69,70,71,72],"AI","Cost Optimization","LLM","Local AI","Orchestration","Privacy","Tutorial","blog","development","mdx","testing","actionData","errors"]

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