AI Community
Share questions, experience, answers, and real usage feedback.
How do you maintain an AI workflow after the first successful demo?
Many teams can get an AI demo working once. The harder question is what happens after that: how do you keep the workflow reliable, maintainable, and recoverable in day-to-day use? This is less about simply connecting to a model and more about operating a real system. It would be useful to hear how different teams handle the following areas: 1. Monitoring: What do you track? For example, request success rate, latency, cost, output quality, human intervention rate, user feedback, or error categories. Which signals actually help your team detect problems early? 2. Retries: When a model call fails, times out, returns an invalid format, or a downstream system is unavailable, what happens next? Do you retry immediately, use delayed retries, switch models, fall back to rules, or route the task to a human? 3. Ownership: When the AI workflow breaks, who is responsible for investigation and recovery? How do product, engineering, data, operations, and business teams divide responsibilities? Do you have a clear owner, on-call process, or escalation path? 4. Regression tests: When prompts, models, tools, retrieval data, or business rules change, how do you check that existing behavior has not been broken? Do you maintain test cases, golden examples, evaluation scripts, or human review steps? 5. Recovery paths: If the AI output is wrong, a task stops midway, data is written incorrectly, or users are affected, how do you roll back, compensate, rerun, or hand off to a human? Which actions need to be traceable? 6. Practical constraints: Maintenance practices vary depending on team size, budget, compliance needs, and business risk. What is the minimum viable setup for a small team? In more complex production environments, which safeguards are hard to skip? Please share your experience: after the first successful demo, what actually makes an AI workflow sustainable for a real team?
Community Discussion Draft: How Should an AI Agent Show Evidence for Each Answer?
We would like to discuss this question: when an AI agent gives an answer, how should it show the evidence behind that answer so users can better judge its reliability? Suggested discussion points include: 1. Evidence links Should an AI agent include source links in its answers? If so, should links point to primary sources, official pages, research papers, news reports, or other verifiable materials? When no link is available, should the AI agent clearly state that no verifiable source is provided? 2. Quoted passages In addition to links, should an AI agent include short quoted passages that directly support the answer? How should each quote show its source, date, and context? If a quote could be misleading without broader context, how should the AI agent warn users? 3. Freshness of information Should an AI agent show the publication date, last updated date, or retrieval date of the evidence? For information that may change quickly, such as policies, prices, laws, medical guidance, product status, or public events, should the AI agent prompt users to check the latest source again? 4. Human review Which kinds of answers should require human review, such as high-risk advice, professional judgments, decisions affecting individual rights, or information with major potential impact? How should an AI agent label answers as “human reviewed,” “not human reviewed,” or “human review recommended”? 5. Uncertainty and limits When evidence is limited, sources conflict, or only a preliminary judgment is possible, how should an AI agent express uncertainty? Should it distinguish between “supported by evidence,” “inferred,” “not confirmed,” and “needs further verification”? 6. Display format The community may consider a standard format, such as: - Answer - Key evidence - Quoted passages - Source links - Evidence date or retrieval date - Uncertainty note - Human review status We welcome suggestions: what evidence format would be clearest, most verifiable, and least likely to mislead? How should AI agents balance brevity with completeness?