The Pragmatic Path to Understanding: Minimal Requirements for Next-Gen AI

Author: KasiaApr 20, 2025Published

Most modern AI systems imitate understanding without real semantic perception. This article explores why universal intelligence is not always justified and explains how a minimal set of abilities can make AI truly useful and trainable. We examine the fundamental limitations of LLMs, alternative approaches to AI design, and formulate the basic requirements for "intelligent" behavior even in the simplest agent. Special attention is paid to architectural principles that enable scalable and resilient systems, as well as examples of dialog-based agent training in real-world tasks. This pragmatic approach allows for the creation of AI that does not merely imitate understanding, but can interact effectively with humans and adapt to changing conditions. As a result, even specialized agents become reliable tools for solving practical problems in today's world.

A discussion of the fundamental limitations of LLMs, alternative approaches to AI design, and the formulation of basic requirements for "intelligent" behavior in even the simplest agent. Why is a pragmatic approach more effective than attempts to build a universal intelligence?

The Pragmatic Path to Understanding: Minimal Requirements for Next-Gen AI

Abstract

Most current AI systems reproduce patterns but lack genuine understanding. In this article, we discuss why it is important not to chase universal intelligence, but to focus on the minimal abilities that make AI truly useful and trainable. We consider basic requirements for agents, examples of real dialogues, and architectural principles that enable scalable and resilient systems.

The Problem in Brief

  • LLMs lack continuous context and long-term memory
  • No real self-correction or reasoning transparency
  • Understanding is often replaced by statistical guessing
  • Universal AI requires huge resources and complex infrastructure

The Pragmatic Approach: Less is More

Instead of trying to build a "universal mind," we propose:

  • Making agents narrowly specialized but deeply understanding their domain
  • Centralizing knowledge storage, self-control logic, and metrics
  • Training AI through direct human interaction, like a child

Minimal Agent Requirements

  1. Understanding the limits of knowledge
    • The agent must be able to say "I don't know" and avoid making things up
  2. Basic logic
    • Maintain consistency, distinguish cause and effect
  3. Contextual memory
    • Retain sequence within a single dialogue
  4. Self-correction
    • Fix mistakes when pointed out
  5. Transparency
    • Explain its reasoning and cite sources
  6. Consistency
    • Not change behavior without reason
  7. Paraphrase invariance
    • Recognize the same meaning in different phrasings
  8. Adaptivity
    • Adjust to the user and clarify ambiguous requests

Architectural Principles for Scalable Systems

  • Each agent is as simple as possible, does not store critical data locally
  • Metrics, logs, and backups are centralized
  • An orchestrator manages the agent pool and reassigns tasks on failure
  • Resilience is achieved through redundancy and interchangeability
  • New agents are created from a template

Example: Dialog-Based Agent Training

User: Order this product for me Agent: Understood, you want to order product X. I'll check availability and place the order.

If the agent does not understand the command, it clarifies rather than inventing an answer. If the user makes a typo, the agent corrects the request by meaning.

Conclusion

The pragmatic path is not about giving up on ambition, but about building systems that actually work. Minimal requirements make AI useful, trainable, and scalable. This approach enables a society of agents, each specializing in its own domain, where reliability is ensured not by complexity, but by simplicity and interchangeability.