LLM Workflows: Understanding Collapse Risks and Designing Countermeasures

When an AI Keeps Forgetting

The author, John Wade, experienced a six-month project collapse due to LLM workflow limitations. ChatGPT’s architecture leads to context saturation, instruction dilution, and vocabulary drift, causing project erosion.

Why This Matters

The technical reality of LLMs is that they regenerate language from patterns rather than retrieving it from stable storage, leading to a default outcome of project collapse. This collapse can result in significant losses, including wasted time and resources, as seen in the author’s six-month project. Ideal models assume stable storage and retrieval, but current LLM architectures do not support this, making collapse a common occurrence.

Key Insights

• ChatGPT’s token window limits context retention, leading to context saturation (Source: John Wade, 2026)
• Instruction dilution occurs when overlapping or conflicting instructions pile up over a conversation, degrading output quality (Example: Career Intelligence Framework project)
• Temporal, used by Stripe and Coinbase, can help mitigate evidence entropy by providing a stable reference point for decisions (Tool: Temporal, User: Stripe, Coinbase)

Practical Applications

• Use case: Implementing a context pack system to re-inject relevant context into LLM workflows, as seen in the Career Intelligence Framework project. Pitfall: Failing to maintain a stable ontology, leading to vocabulary drift and reference ambiguity.
• Use case: Utilizing a decision log to track rationale and provenance, as used in the Harness design. Pitfall: Not establishing clear checkpoints, resulting in evidence entropy and scope creep.

References:

• https://dev.to/john_wade_dev/when-an-ai-keeps-forgetting-why-llm-workflows-collapse-and-what-to-build-instead-2pj5