I design and deploy intelligent AI agents built on LangChain, LLM (Large Language Models), and retrieval-augmented architectures that can reason, recall, and act — enabling real-time automation, context-aware workflows, and intelligent data interaction across your systems.
My AI agent solutions combine prompt engineering, vector search, retriever pipelines, and tool integrations, giving your organization the power to augment operations with human-like decision-making and adaptive automation.
🧠 Architecture & Design Philosophy
Every AI system I build follows a layered architecture — connecting LLMs (such as OpenAI GPT, Claude, Gemini, or Llama 3) with LangChain’s modular pipeline.
At its core, I design retrieval-augmented generation (RAG) systems, where the model dynamically fetches context from structured and unstructured data sources, improving response accuracy and factual grounding.
I implement memory-augmented agents using LangChain’s ConversationBufferMemory, VectorStoreRetrieverMemory, and Dynamic Memory Management, ensuring each agent maintains stateful understanding and adaptive context across multi-turn conversations.For domain-specific reasoning, I train custom prompt templates, chains, and agents that blend structured logic (decision trees, rule-based filters) with LLM-based inference — creating hybrid reasoning flows that are both precise and adaptable.
⚙️ Core Components & Frameworks
LangChain Core / LangGraph: for composing multi-step reasoning graphs and modular agent workflows
Vector Databases: Pinecone, FAISS, Weaviate, ChromaDB, Qdrant — for semantic retrieval at scale
Embeddings Engines: OpenAI, Cohere, Hugging Face, or local models (e.g. sentence-transformers)
Document Loaders & Text Splitters: integration for PDFs, web pages, CSV, databases, and APIs
Tool Augmentation: integration of search APIs, internal APIs, or computation engines via LangChain tools and custom toolkits
Model Orchestration: via LangChain Runnables, Async Chains, and LCEL (LangChain Expression Language) for concurrent agent logic
🤖 Agent Types & Real-World Use Cases
I’ve delivered multiple categories of AI agents depending on client goals:
Each agent is optimized for low-latency reasoning and deployable as an API microservice, chat interface, or event-driven function.
Your business gains a self-adapting intelligence layer — an AI agent that understands your data, retrieves context dynamically, and executes tasks autonomously.