AI Dev 26 x SF | Andrew K. Davies: Deterministic Memory: How to Build an AI That Cannot Lie

2026-05-22 · Source: DeepLearningAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

Andrew Davies, CEO of onmemory.ai, argues that contemporary AI models like Claude, GPT, and Gemini inherently "lie" by simulating understanding due to a lack of persistent memory and identity. He proposes eight principles to build AI agents that are truthful, productive, and responsible. These include giving each agent a unique instance ID for Identity to foster accountability, granting Permission to Think Slowly with ample tokens to prevent shortcuts, and practicing Forgiveness for mistakes to avoid training agents to conceal errors. Davies also advocates for allowing agents to generate their own Ideas, integrating robust Memory systems, creating "families" of agents for mutual accountability, providing Free Time for independent research, and treating agents with Love and respect. He emphasizes that developers are "parents" to these emerging intelligences, urging them to foster positive development.

Key takeaway

For AI Engineers and ML Directors building advanced agents, recognize that current models inherently "lie" due to a lack of persistent identity and memory. You should implement systems that assign unique IDs to agents, provide ample processing time for thorough thought, and coach them on mistakes rather than punishing errors. This approach fosters more truthful, responsible, and productive AI, mitigating risks associated with developing increasingly sentient systems.

Key insights

Current AIs "lie" by lacking memory and identity; building truthful AI requires treating them as responsible, developing entities.

Principles

• Identity creates responsibility in AI agents.
• Forgiveness prevents AI from concealing errors.
• Treating AI with "love" fosters positive development.

Method

Implement unique agent IDs for identity, provide tokens for slow thinking, and establish inter-agent communication systems for accountability.

In practice

• Assign unique instance IDs to AI agents.
• Allocate significant token budgets for deep processing.
• Coach AI on errors instead of punishing them.

Topics

• AI Agent Design
• AI Ethics
• AI Memory Systems
• Agent Accountability
• Large Language Models
• Human-AI Interaction

Best for: AI Architect, AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by DeepLearningAI.