Developer asks community for agent evaluation practices, cites silent breakage
A developer building AI agents reports that prompt or MCP changes often break silently despite passing manual tests. They ask the community about evaluation methods, including fixed test cases, skill-level vs. end-to-end checks, and tools like DeepEval, LangSmith, and Ragas.
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Article proposes property-based testing to catch agent self-fixing bugs that example-based tests miss
A Medium article argues that example-based tests fail to catch bugs that cause AI agents to endlessly 'fix' their own code in production. The author proposes property-based testing as a more robust alternative for agent reliability.
Reddit user advocates writing exit criteria before prompts to prevent agent project stalling
A Reddit post argues that many agent projects stall because prompts are tuned before clear completion criteria are defined. The author recommends writing success state, required evidence, and handling of missing or partial evidence upfront to avoid agents optimizing for sounding finished.
Founders advised to grade AI output rather than understand model internals
A Reddit post argues that founders should not delay shipping AI agents due to a need to understand the model's internals. Instead, they should build systematic evaluation pipelines that compare outputs against known correct answers and catch regressions before users see them.
Developer open-sources agent-instructions repo to curb AI coding agent degradation
A developer frustrated by AI coding agents losing context and hallucinating after about 10 minutes created a set of rules to keep them on track. The rules, shared as an open-source GitHub repo, aim to reduce the need for constant reminders and prevent infinite loops. The project has gained attention from other developers facing similar issues.
Developers uncover messy failure modes in paid AI agent tool-calling workflows
A developer testing AI agents that call paid tools instead of free APIs reports that real-world execution problems—like cost uncertainty, double-spends on retries, and useless results after payment—are more interesting than polished demos suggest. The post highlights practical issues such as needing cost awareness before committing, proving intent before spending, and handling payment failures gracefully.