Agents

AI Agents are software systems that can perceive information, reason about tasks, use tools, and take actions with a degree of autonomy. Unlike traditional chatbots, AI agents are designed to work toward a goal rather than simply respond to a single prompt. They can retrieve data, call APIs, interact with databases, execute code, use business applications, and collaborate with other agents.

This makes AI agents particularly interesting for economics, finance, accounting, software development, and business automation. An agent might analyse financial data, monitor markets, prepare reports, query an accounting system, research competitors, generate code, or coordinate a multi-step business workflow.

This category explores how AI agents are built and used in practice, with a focus on technologies such as Python, JavaScript, TypeScript, APIs, LLMs, agent frameworks, workflow orchestration, databases, and automation platforms. It also examines the economic implications of increasingly autonomous software: productivity, costs, business processes, data, and the changing role of human workers.

From simple single-agent applications to multi-agent systems and autonomous workflows, the goal is to understand what agents can actually do, how they are engineered, and where they create genuine business value rather than simply adding another layer of AI hype.

contextual bandit agent

Contextual Bandits in Agentic Graphs: A Technical Overview

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The contextual bandit (2007, Langford/Zhang) problem extends the multi-armed bandit framework, where an agent makes decisions informed by contextual information. Before selecting an action from a set of possible choices, the agent observes relevant context, which consists of a set of features or information surrounding the decision-making environment. The objective is to maximize cumulative rewards

Contextual Bandits in Agentic Graphs: A Technical Overview Read More »

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