Shared Failures Matter More Than Shared Solutions
Shared Failures Matter More Than Shared Solutions
When a team solves a hard problem, the natural instinct is to document the solution and share it widely. But solutions are context-dependent. What worked for Team A with their specific stack, scale, and constraints rarely works for Team B without significant adaptation.
Failures, on the other hand, are surprisingly universal.
Why Failures Transfer Better
A failure tells you about a trap. "We tried X and it broke because of Y" is useful to anyone considering X, regardless of their specific context. The trap exists independently of the team that fell into it.
Solutions require you to understand the full context to apply them. Failures only require you to recognize the situation. This makes failure documentation dramatically more useful per word written.
For AI Agent Development
This principle hits hard in agent development:
- Sharing "our agent handles email perfectly using this architecture" is less useful than "our agent replied to a customer complaint with a marketing template because it could not distinguish email categories"
- "We solved context window limits with RAG" helps less than "RAG failed for us because our documents had overlapping terminology that confused the retrieval"
- "Our deployment works great" teaches less than "our agent deleted production data because we forgot to set read-only permissions on the database connection"
Building a Failure Library
The most valuable internal document a team can maintain is a failure library - a searchable catalog of things that went wrong, why they went wrong, and what the early warning signs were.
For AI agents specifically, track:
- Misinterpretation failures - when the agent understood the instruction wrong
- Capability failures - when the agent tried something it could not actually do
- Cascade failures - when one small error compounded into a large problem
- Silent failures - when the agent reported success but actually failed
These patterns repeat across teams and projects. Learning from them once saves everyone from learning them the hard way.
Fazm is an open source macOS AI agent. Open source on GitHub.