Introduction: From ‘using AI’ to ‘building AI’ – a critical leap
In late 2025, an open-source AI agent project called OpenClaw burst onto the scene, attracting two million visitors and over 100,000 GitHub stars within a single week. ‘Your assistant, your machine, your rules’ – the slogan ignited enormous enthusiasm across the Global North’s technology communities. Almost simultaneously, several technology giants rushed to release their own ‘AI agent’ products, each claiming to revolutionise the very nature of knowledge work. For technology professionals in the Global North, this may represent yet another exciting cycle of technical iteration; but for researchers, movement leaders, and intellectuals in the Global South, a more fundamental question demands attention: when everyone is celebrating the technical details of AI agents, very few are asking who owns and controls these tools, and whose agenda they serve.
This is not a rhetorical question. When an African researcher uses the ChatGPT chat interface to assist her writing, she is a consumer of AI – consuming a product designed by others, working under rules set by others, on platforms owned by others, with data selected by others. She can, in principle, paste her methodology into the prompt and save the output as a file – but these are crude workarounds, not systemic capabilities. The system’s behavioural logic remains opaque to her, the embedding of methodology is ad hoc and fragile, and knowledge accumulation depends entirely on manual effort outside the platform. Each conversation is an island; nothing compounds.
I. A new form of software – the declarative multi-agent system
When people speak of ‘agentic AI’, the term is often shrouded in a fog of commercial hype. Once this fog is cleared, what emerges is a straightforward engineering reality: a multi-agent system (MAS) is, at its core, a form of software – not magic, not the embryonic stirring of general intelligence, but a new form of software with a clear conceptual framework, architectural principles, and engineering methodology. Grasping this point is the starting point for pulling AI back from hype into material reality.
1.1 From long prompts to multi-agent systems: why MAS is necessary
Consider a researcher attempting to use AI for a complex research task. She might write a lengthy prompt, cramming role definitions, research methods, theoretical frameworks, output formats, and quality 1standards into a single block of text. As the task grows more complex, this prompt swells to 30,000 or even 50,000 words – and then three structural problems become impossible to avoid.
1.2 Core concepts of MAS
Understanding how multi-agent systems work requires grasping five core concepts.
The agent blueprint is a design-time definition document. It is a text file written in natural language – the most common format is Markdown (a lightweight formatting syntax widely used for writing documents), though even a plain text file will do – describing what an agent should do: role definition, input specification, task description, output specification, and quality standards. A blueprint is to an agent what an architectural drawing is to a building – the drawing is not the building itself, but a complete description of it.
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