I build AI agents that go beyond chatbots -- autonomous systems that plan, reason, use tools, and take actions to achieve business goals. From customer support agents that actually resolve issues to operations agents that process documents and update systems.
A chatbot answers questions. An AI agent gets things done. The difference is fundamental: an agent can plan a sequence of steps, use tools (APIs, databases, search), take actions in external systems, and adapt its approach based on results. It does not just respond -- it works.
Imagine an AI that receives a customer complaint, looks up the order in your system, checks the return policy, processes the refund, sends a confirmation email, and updates the CRM -- all autonomously. Or an agent that monitors your competitors' pricing, generates a report, and alerts your team when action is needed. These are not science fiction scenarios -- they are production-ready solutions I build today.
I am Kirill Strelnikov, a freelance AI engineer based in Barcelona. I specialize in building custom AI agents using LangChain, LangGraph, OpenAI, and Claude. AI agents represent the next evolution of business automation -- and early adopters are gaining a significant competitive advantage. For simpler conversational AI needs, see my AI chatbot development service.
Support agents that resolve issues end-to-end (refunds, cancellations, account changes). Sales agents that qualify leads, check availability, and book meetings. Onboarding agents that guide new users through setup with personalized steps.
Document processing agents that extract, validate, and route data from invoices, contracts, and forms. Data analysis agents that query databases and generate insights. Monitoring agents that watch for anomalies and trigger alerts.
Market research agents that gather and synthesize competitive intelligence. Content generation agents that produce drafts based on your brand guidelines. Code review agents that analyze pull requests and suggest improvements.
Building reliable AI agents requires more than calling an API. It requires orchestration frameworks, tool management, memory systems, and robust error handling.
Agent framework: LangChain and LangGraph provide the orchestration layer -- planning loops, tool selection, memory management, and state machines that control agent behavior. The agent reasons about which tools to use, in what order, and how to handle unexpected results.
Tool system: Each agent is equipped with custom tools -- functions that interact with your APIs, databases, email systems, CRMs, and file storage. Tools are type-safe, logged, and rate-limited. The agent selects and combines tools dynamically based on the task.
Memory and context: Agents maintain short-term (conversation) and long-term (persistent) memory. RAG with vector search provides access to your knowledge base. This means agents improve over time and maintain context across interactions.
I analyze the task you want to automate, identify the tools the agent needs, define success criteria, and design the agent's reasoning flow. This phase produces a clear architecture document with a fixed-price estimate.
I design the agent's tool set, state machine, memory architecture, and error handling strategy. Each tool is specified with inputs, outputs, and side effects. You approve the design before development starts.
I build the agent iteratively -- starting with core tools and basic reasoning, then adding complexity. Prompt engineering ensures the agent reasons correctly about when and how to use each tool. Weekly demos with real scenarios.
Comprehensive testing with production-like scenarios. I evaluate reasoning quality, tool selection accuracy, error recovery, and edge case handling. The agent is tested against adversarial inputs to ensure safety and reliability.
Production deployment with monitoring for reasoning quality, tool usage patterns, and success rates. I set up feedback loops so the agent improves based on real usage. Ongoing optimization packages available.
Built a Telegram-based AI system that autonomously routes user requests to the optimal AI model (text, image, video generation). The system manages model selection, handles billing, processes concurrent requests through task queues, and scales automatically. Reached 500 paying users in 3 months with minimal manual intervention.
Built an agent for an e-commerce platform that understands customer preferences through conversation, searches the product catalog, generates personalized recommendations, and handles follow-up questions. The agent uses RAG to ground responses in actual product data and adapts its recommendations based on conversation context.
AI agents are the highest-impact AI investment a business can make in 2026. Book a free consultation to explore what an agent can do for your business.
Transparent pricing based on agent complexity. All prices include development, tool building, testing, deployment, documentation, and post-launch support.
Need a custom scope? Get in touch for a tailored proposal within 24 hours.
Kirill Strelnikov is a freelance AI engineer based in Barcelona, Spain. He specializes in AI agent development, building autonomous systems with LangChain, LangGraph, OpenAI, and Claude. He has delivered over 15 commercial AI projects including multi-model AI platforms, intelligent chatbots, and automated business systems for clients across Europe.
He works with fixed-price contracts. Communication in English, Spanish, and Russian.
Learn more on the about page or explore the full portfolio.
Tell Kirill about the task you want to automate. He will design an agent architecture, estimate the timeline, and give you a fixed price -- within 24 hours. Free consultation.
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