Development and implementation of AI agents, virtual assistants, chatbots, and autonomous systems for businesses.
A local AI agent can run directly on a regular Windows computer, so you don't have to pay for every request to OpenAI, DeepSeek, or another external API. The model runs locally through Ollama, while DeepSeek Harness turns it from a regular chat into a working AI agent that can read…
Ollama is a program that runs AI models directly on your computer and provides a local API for them. Instead of sending every request to cloud-based ChatGPT, Claude, or DeepSeek, you can download a compatible model and interact with it, connect it…
In short: Model Context Protocol, or MCP, is an open standard that helps an AI application connect to external data and tools using common rules. With MCP, an agent does not have to guess the structure of each integration, but can get an exact description of available actions: find a record in…
DeepSeek Harness is an open environment for running AI agents on your computer. Unlike a regular chat with an AI model, Harness can work with project files, run commands in the terminal, modify code, verify results, use tools, and delegate individual tasks to subagents. When…
Prompt engineering isn’t going away, but it’s no longer the main way to manage AI. Instead of long instructions that have to be reinserted and fixed each time, companies can package workflows into AI skills — structured sets of rules, templates, examples, and tools. The key change is not in a new…
Agent harness is a software environment in which a language model receives instructions, tools, and the ability to complete a task in multiple steps. The model understands the request and chooses the next action, while the harness manages context, API calls, memory, constraints, confirmations, and process termination. Specifically…
AI agent memory is not a hidden capability of the language model, but an external system for storing, retrieving, and updating context. It allows the agent to continue working between sessions, take user preferences into account, use accumulated facts, and avoid repeating the same mistakes. A practical architecture…
Short answer: LLM Wiki does not replace RAG and does not guarantee 100% accuracy. But for a small core of critically important knowledge — regulations, contracts, technical documentation, and complex research — a connected semantic map can be more reliable than naive RAG with mechanical text chunking.…
In short: an AI agent retains context not because it constantly keeps the entire conversation, documents, and instructions in memory. A reliable architecture separates operating rules, process state, and knowledge base search. The agent receives only the data needed for the current step, and then…
