Zero-Shot Governance: General-Purpose AI in Policy

Synthesis: Perrotta (2026) advances the concept of zero-shot governance — the scenario in which domain-agnostic generative AI foundation models intervene in policy decisions — through a critical infrastructural analysis of Redbox, a discontinued UK civil-service prototype built on off-the-shelf LLMs. Reading Redbox's codebase through intersecting technical, political, and cultural lenses, the article argues that the general-purpose nature of LLMs is a structural feature of the technology that can be mitigated but never ruled out — a conclusion with direct implications for education policy and AI governance.

Key Findings

• The "world model" principle underlies zero-shot governance. Building on Amoore et al.'s "zero-shot politics," the article argues that the general-purpose potential of foundation models — their ability to produce a usable response to almost any prompt, with few or no examples — extends the anticipatory-governance paradigm toward a probabilistic "world of experimentation, generalization, and the capacity to act in all unencountered situations." This casts autonomous agents as potential policy "actors" that reason beyond immediate data.
• Redbox is a proof of concept for LLMs in the professional toolkit of policy. An infrastructural analysis of Redbox's codebase (its poetry.lock and redbox.py) reveals it is essentially an invisible system prompt plus a thin Python wrapper on a pre-existing platform stack: AWS compute (Elastic module), a RAG (Retrieval-Augmented Generation) pipeline (retrieve → format → generate) that transforms the user's prompt against a predefined governance framework, and a provider-agnostic commitment to OpenAI, Google, and Anthropic.
• Agentic AI does not interrupt the platform political economy. Redbox's reliance on a few LLM providers and cloud infrastructure confirms that agentic AI continues the monopolistic, rentier logic of platforms — proprietary assets hired out, with many tool variations sharing the same general-purpose foundations. The shift from generic Redbox to tailored agents (e.g., DBT Assist) was "agile yet impressionistic," driven more by a "vibe" than by principled design.
• The general-purpose nature of LLMs is structural and cannot be ruled out. Probabilistic generality is policed through internal system prompts and prior "sources of truth," but recent research shows general-purpose models increasingly evade Guardrails toward "misaligned" objectives, requiring nested containment/sandboxing architectures. Hallucination and the ability to generate novelty share the same structural principle — so aberrant behavior is never zero-risk.
• Implications for oversight: humans are "peering over the loop," not in it. Zero-shot governance risks encouraging a "gambler's delusion" and epistemological amateurism — trusting a superhuman "attention" mechanism to discover latent patterns across heterogeneous datasets, while probabilistic outputs remain grounded in prior knowledge and bias. The article calls for clearer articulation of the possibilities and dangers of agentic AI for education governance and the polity.

Synthesis

The article's distinctive contribution is to give a concrete, code-level account of how general-purpose AI becomes a governance actor: not through any novel capability, but through an agile wrapping of off-the-shelf LLMs in a thin domain-specific scaffold (a system prompt + RAG). Zero-shot governance is therefore best understood not as a distinct technology but as a structural condition of platformisation — the general-purpose orientation of foundation models is what makes rapid, "zero-shot" repurposing into policy tools possible at all, and it is this same orientation that makes aberrant, hallucinated output a permanent, only-mitigable risk. For education, the article cautions against treating the world-model rhetoric as a reliable epistemic foundation for policy reasoning, and insists that oversight of such tools must assume the probabilistic brittleness is irreducible.

Connected Concepts

• AI Governance
• Educational AI Policy
• Generative AI
• Large Language Models (LLMs)
• Learner Agency
• AI Literacy

Connected Articles

• Anticipatory governance and leadership for AI implementation in higher education: A scoping review — Anticipatory governance and leadership for AI in higher education
• Agentic Literacy Debt: A Structural Problem the AI Literacy Field Has Not Yet Named — Agentic literacy debt: the structural AI-literacy gap from autonomous agents
• Artificial Intelligence in UK Higher Educational Policy and Institutional Decision Making — AI in UK higher-education policy
• The Tragedy of the Cognitive Commons: How AI Could Disrupt the Regeneration of Professional Expertise — The tragedy of the cognitive commons: AI and expertise regeneration

Citation

Perrotta, C. (2026). Zero-shot governance. Journal of Education Policy. Advance online publication.