Reliable AI Agents: Context, Tools and Evals
Design AI agents with bounded execution, durable state, explicit permissions and outcome-based evals. A practical architecture for production workflows.
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Technical articles on LLM inference, retrieval architectures and reliable AI agents. Engineering decisions, worked examples and primary sources.
In a far far away galaxy…
On New Year’s Eve in 2008, in Chișinău, Nikba Creative Studio began its work.
The studio started with an ambition: to change the world for the better. To make everyday life more convenient and practical. To simplify the tasks that take up our time. To help companies organise their work, reach their customers and improve their services.
Graphic design came first. Websites, applications and business systems followed. Each new field gave us more ways to solve the problems our clients brought to us.
Today, Nikba develops its own products and explores artificial intelligence through experiments and real projects.
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RAG vs Fine-Tuning: Architecture for Production AI
Choose RAG, fine-tuning or a combined architecture using data freshness, access control, retrieval quality and measurable failure modes.
Read technical articleHow LLM Inference Works: Attention, KV Cache and Reasoning
Inside LLM inference: attention, prefill, decoding, KV-cache memory and reasoning budgets, with a worked calculation and a practical measurement plan.
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