Infrastructure as Code (IaC) is often perceived as a topic for large tech corporations – with massive cloud environments and dedicated DevOps teams. Yet mid-sized companies in particular benefit significantly from automated infrastructure management, often with a manageable entry effort.
Kubernetes is now considered the quasi-standard for orchestrating containerized applications. For many mid-sized IT departments, however, the technology remains a mystery – there’s often a noticeable gap of uncertainty between the hype and the real benefit. It’s worth taking a look at the fundamentals before an implementation is planned.
Companies today collect more data than ever before – from ERP systems, CRM applications, IoT sensors, and countless other sources. But data alone doesn’t create value. Only a properly operated data warehouse (DWH) turns the flood of data into a reliable basis for decision-making. In practice, however, it becomes clear: many DWH environments don’t suffer from a lack of tools, but from a lack of operational discipline.
Artificial intelligence has entered many enterprises first in creative and analytical areas — text generation, data analysis, code assistance. IT operations is now following with a force that is fundamentally changing how IT teams work. The combination of Agentic AI, large language models, and standardized integration protocols such as MCP makes it possible to automate routine tasks in IT operations that previously required mandatory human intervention.
Using AI language models via cloud services is quick and straightforward — but it comes with a fundamental problem: data leaves the corporate network. For many use cases — internal documents, customer data, source code, financial data — this is not an acceptable option. Local AI models provide a solution: the model runs on your own infrastructure, and no token leaves your own environment.
Large Language Models (LLMs) are AI models trained on enormous volumes of text that can understand, generate, and process natural language. They form the core of modern AI assistants, automated text systems, and — as described in the article on Agentic AI — autonomous AI agents.
The Model Context Protocol (MCP) is an open standard for communication between AI models and external data sources or tools. Developed and published by Anthropic in November 2024, MCP addresses a fundamental problem of modern AI systems: language models are inherently isolated — they have no knowledge of corporate data, cannot call APIs, and have no access to current information. MCP creates a standardized bridge between the AI model and the outside world.
Jira and Confluence are among the most widely used collaboration and project management platforms in organisations. Yet despite their extensive feature sets, many teams eventually hit the limits of the standard configuration. This is exactly where ScriptRunner comes in: a powerful plugin by Adaptavist that enables deep customisation and automation directly within Atlassian products.
Agentic AI describes AI systems that do not merely respond to individual inputs, but independently plan and execute multi-step tasks while flexibly adapting to changing conditions. Unlike traditional AI assistants, which receive a request and deliver a response, agentic systems act in a goal-oriented manner: they break complex tasks down into sub-steps, select appropriate tools, execute them, and verify the results – all without human intervention at every individual step.
The term derives from the English word “agent” (one who acts, a representative). An AI agent is a system that perceives, decides, and acts within its environment – continuously and aligned toward an overarching goal.
RFID stands for Radio Frequency Identification – a method for the contactless automatic identification of objects using radio waves. An RFID system consists of two basic components: the transponder (also called a tag or label), which is attached to the object to be identified, and the reader, which wirelessly reads the stored information.
RFID tags can be passive or active. Passive tags have no power supply of their own – they draw their energy from the electromagnetic field of the reader and have a virtually unlimited lifespan. Active tags have their own battery, continuously transmit signals, and can be read over significantly greater distances.