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Trusted Mini-Agents

Engineering AI Errors Out of Agents

An introduction to trusted mini-agents: least-privilege AI systems that engineer AI errors out of agents.

Authors

Will Landau

Sam Parmar

Version 0.2.0

November 5, 2026

Introduction

For high-stakes work that needs to be correct, generative AI needs guaranteed safeguards. AI models produce errors that cannot be prevented through the engineering of prompts or context. In the life sciences, for example, AI model errors risk undermining the correctness of statistical deliverables, impacting the health and safety of millions of patients.

This guide proposes a strategy to achieve trust without sacrificing the convenience of AI models. We introduce trusted mini-agents: least-privilege AI systems that engineer out AI errors and excel in precision scenarios.

Definition

A trusted mini-agent is an AI agent that follows these three rules:

  1. Every output of the AI model is untrusted.
  2. Every result is the direct output of a tool trusted to produce it.
  3. Every input to a trusted tool has trusted human oversight.

Impact on human users

Instead of:

Are these results correct?

users ask:

Is the agent solving the right problem?

which a human can verify more easily.

When trusted mini-agents are useful

Compared with the sweeping power of coding agents and multi-agent RAG pipelines, trusted mini-agents may seem anticlimactic or underwhelming. However, they excel in precision scenarios where the task is narrowly defined and human oversight is feasible. The hardest part is framing the problem. The goal is to insert the AI model where:

  1. The AI model proposes solutions better than a human.
  2. A human easily checks those solutions.

Specialized, targeted problems can benefit: for example, clinical trial simulation, clinical data analysis, and Kaplan-Meier image digitization.

Who this guide is for

This guide is for quantitative scientists and programmers who implement AI tools for high-stakes work. Example agents in this guide use Shiny for R and Python, but the principles generalize to any programming language or framework, and no prior experience with R, Python, or Shiny is necessary.

Contents

Concepts. The abstract ideas behind trusted mini-agents.

  • Deconstructing agents. An overview of agents, their relevant components, and how they work together, laying the groundwork for trusted mini-agents in later chapters.
  • AI errors. How AI errors manifest in agents, and how to harden an example weather agent against them.
  • Defining trusted mini-agents. The three rules of a trusted mini-agent, the role of untrusted tools, and the forms that human oversight can take.

Implementation in R. Concrete example implementations of trusted mini-agents in R, building on the concepts above.

Implementation in Python. Concrete example implementations of trusted mini-agents in Python, building on the concepts above.

Appendices. Related work that adapts the ideas above.

Acknowledgements