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Open Source•2-month experimentation
AI Search Agent
Agentic AI combining search, reasoning, and tool calling
Built an AI agent that combines semantic search, multi-step reasoning, and external tool calling to answer complex information requests beyond traditional document retrieval.
AI AgentsSemantic SearchTool CallingLangChainGPT-4
Business Problem
Traditional search engines return documents, but users increasingly expect conversational answers and autonomous task execution requiring multi-step reasoning.
Architecture
- User query → Planner → Search Tool → Retriever → LLM Reasoning → Response Generation
- LangChain/LlamaIndex framework for agent orchestration
- Vector search for semantic retrieval
- Web search API integration for real-time information
- REST API deployment with Docker support
Technical Decisions
- Designed agent workflows where LLM decides when to search versus answer directly, reducing unnecessary API calls.
- Balanced retrieved context against token limits through query reformulation and context compression.
- Used tool calling to ground factual responses in external knowledge rather than relying solely on LLM parametric memory.
Production Challenges
- Monitored API latency, tool success rate, token usage, and search accuracy.
- Evaluated quality through human evaluation, retrieval precision, and task completion rate.
- Designed stateless architecture enabling horizontal scaling for production deployment.
Outcomes
Demonstrates modern Agentic AI design patterns
Provides foundation for enterprise search assistants
Extensible with MCP servers and external tools