Custom AI Agent Development
Custom AI agents for your business. Autonomous systems powered by LLMs, vector databases, and LangChain that automate workflows and process data intelligently.
AI Agent Development
AI agents are autonomous systems that can reason, make decisions, and take actions to complete complex tasks. Unlike simple chatbots that only respond to prompts, agents operate in loops, break problems into steps, use tools, and adapt their approach based on intermediate results.
What I can build for you
Custom AI Agents
Autonomous agents built on LLMs (GPT, Claude, open-source models) that handle specific business tasks. From data processing and report generation to customer support and internal knowledge retrieval.
RAG Systems
Retrieval-Augmented Generation pipelines that connect your data (documents, databases, APIs) with language models. Your agent answers questions accurately, grounded in your actual company knowledge.
Workflow Automation
Multi-step agent workflows that chain together tools, APIs, and decision logic. Agents that can read emails, extract data, update systems, and notify the right people.
Technology Stack
I work with proven frameworks and tools for building reliable AI agents:
LangChain / LangGraph
The most widely adopted framework for building LLM-powered applications. LangGraph enables complex agent architectures with state management, branching logic, and human-in-the-loop workflows.
Vector Databases
Pinecone, Qdrant, ChromaDB, or pgvector for storing and searching document embeddings. The backbone of any RAG system that needs fast semantic search over large datasets.
Agent Loops
ReAct, Plan-and-Execute, and custom loop architectures that give agents the ability to reason step by step, use tools, and self-correct when something goes wrong.
How Agent Development Works
Discovery & Design
We identify the problem your agent should solve, define inputs and outputs, choose the right LLM and tools, and design the agent architecture.
Prototype & Iterate
I build a working prototype quickly, test it against real scenarios, and iterate based on results. Agent behavior is tuned through prompt engineering and loop design.
Integration & Deployment
The agent is connected to your systems (APIs, databases, communication tools) and deployed with monitoring, logging, and fallback mechanisms.
Evaluation & Optimization
Agent performance is measured against defined metrics. Prompts, retrieval strategies, and tool usage are refined to improve accuracy and reliability.
When does an AI agent make sense?
- You have repetitive knowledge work that follows patterns but requires judgment
- Your team spends hours searching through documents or data
- You need to process and summarize large volumes of unstructured text
- You want to automate multi-step workflows that involve multiple tools or systems
- You need an internal assistant that understands your company-specific context
Get in touch - the first 30 minutes of consultation are free.