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.
IoT stands for Internet of Things. IoT sensors are physical measuring devices that capture environmental data and transmit it via the internet or a local network to central systems. They are small, often battery-powered, and can be attached directly to goods, containers, vehicles, or installed in warehouses.
Common sensor types in the context of goods tracking include temperature sensors, humidity sensors, vibration sensors (accelerometers), pressure sensors, light barriers, and GPS modules for geographic positioning. Each of these sensor types delivers a specific data point which, in combination, provides a comprehensive status picture of a shipment.
Apache Kafka is a distributed event streaming platform originally developed at LinkedIn and donated to the Apache Software Foundation as an open-source project in 2011. Kafka was specifically designed to reliably receive, store, and forward extremely large volumes of events in real time – with minimal latency even under high load peaks.
Simply put, Kafka works like a highly scalable, durable message log: producers write data into so-called topics, consumers read that data at their own pace. The data is retained in the system for a configurable period of time – unlike traditional message queues, which discard messages after they have been read.
Apache Airflow is an open-source platform for orchestrating, scheduling, and monitoring data pipelines. Originally developed at Airbnb in 2014, it is today one of the most widely used tools in modern data engineering. The key advantage: workflows are defined not as configuration files, but as Python code – meaning they can be versioned, tested, and reused like software.
DAG stands for Directed Acyclic Graph. The term originates from graph theory, but has a central practical significance in data engineering: it describes the dependency structure of tasks or transformation steps that must be executed in a defined order.
Modern data architectures are shifting to the cloud. Classic on-premises data warehouses can reach their limits when faced with growing data volumes, variable load peaks, and the demand for fast deployment cycles. Cloud DWH platforms decouple compute and storage resources, scale elastically, and significantly reduce operational overhead.