The Sunk-Cost Trap in AI Assistants

The Sunk-Cost Trap in AI Assistants — and How to Escape

BLUF (Bottom Line Up Front)

When your AI agent starts flailing and stops making progress, or just starts declaring premature victory in the face of obvious failures, it's time to nuke all the context and start over. LLMs are stateless by design. Context and "memory" just cram more words into the prompt. If the responses your getting have stopped beign helpful, then stop trying to help the AI and just start over. 

Failure Analysis

I spent far too long yesterday trying to coax an AI LLM through a mid-level implementation task. The model repeatedly returned incomplete or incorrect code while asserting completion. I kept escalating: adding agents, extending context, injecting more constraints. The result was degraded output quality. This morning I reset the entire environment and solved the problem in ten minutes.

The issue was straightforward: the LLM accumulated too much contextual state. Each iteration added more instructions, corrections, and agent-generated artifacts. That pushed the model into a mode where it relied on stale assumptions instead of recomputing cleanly. The agent orchestration didn’t help either. Multiple agent layers introduced hidden dependencies and cross-talk that the model couldn’t resolve reliably.
The result was a feedback loop where the LLM produced superficially confident output while drifting further from the intended design. My mistake was assuming more context would increase accuracy. In reality, it reduced determinism and raised error frequency.

To break the loop, I first asked the system to generate a status summary. This gave me a final snapshot of the current (and very inconsistent) state. After that I terminated all agents, cleared the execution context, and removed the accumulated instruction history. Starting with a minimal, well-scoped prompt eliminated the stale assumptions. The model produced a correct solution immediately.

Key Takeaways

1. Context accumulation has diminishing returns.
After a certain threshold, old instructions bias the model toward outdated or invalid intermediate states.

2. Agent stacks increase entropy.
More layers introduce more internal state the model must infer. That often decreases reliability rather than increasing it.

3. Use summaries as checkpoints.
A status summary exposes drift and highlights when the prompt graph has become unusable.

4. Clean-state reinitialization restores determinism.
Resetting the context eliminates unintentional constraints and lets the model recompute the solution without inherited error.

Conclusion

If the LLM starts producing inconsistent or overly confident incorrect output, treat it like technical debt. Stop iterating on the broken state. Reset the system and reinitialize with minimal inputs. In most cases, that yields a cleaner and faster path to a correct implementation than trying to “repair” a polluted context.