AI agents — autonomous systems that can reason, use tools, and take actions — are moving from research demos to production deployments. This guide covers the architecture patterns, implementation frameworks, and operational considerations for building real AI agents.
AI Agent Architecture Patterns
The three dominant patterns are: ReAct (Reason + Act) for general-purpose agents, Plan-and-Execute for complex multi-step tasks, and Tool-Use agents for specific API integrations. Most production agents combine these patterns with fallback strategies and human-in-the-loop checkpoints.
Building with LangChain & Tool Use
LangChain provides the most mature agent framework with built-in tool integration, memory management, and chain composition. Key implementation steps: define tools with clear descriptions, implement structured output parsing, add conversation memory (buffer or summary), and configure retry logic with exponential backoff.
📥 무료 다운로드: 2026 베트남 오프쇼어 개발 비용 가이드
실제 개발자 단가, 프로젝트 비용 내역, 예산 기획 템플릿. 200명 이상의 스타트업 창업자가 사용 중.
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NKKTech은 AI Development 프로젝트를 $30K부터 제공합니다.
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Production Deployment Considerations
Production AI agents need: token budget management to prevent runaway costs, observability (LangSmith, Helicone) for debugging agent reasoning, rate limiting per user/session, graceful degradation when LLM APIs are unavailable, and comprehensive logging of all tool calls and decisions. If you'd rather skip the trial-and-error, our team works as an AI agent development company that handles architecture through production deployment.
📥 무료 다운로드: 2026 베트남 오프쇼어 개발 비용 가이드
실제 개발자 단가, 프로젝트 비용 내역, 예산 기획 템플릿. 200명 이상의 스타트업 창업자가 사용 중.
구축할 준비가 되셨나요?
NKKTech은 AI Development 프로젝트를 $30K부터 제공합니다.
고정 범위. 베트남 시니어 엔지니어. 14일 킥오프.
10+ years building AI systems for Toyota, Sony, and Rakuten in Japan. Founded NKKTech in 2018 with a senior-only engineering model.
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