1530449 Comments

• Comment Link الأحد, 18 تشرين1/أكتوير 2026 19:15 posted by RichardCal

  С пациентом работают профильные специалисты, которые оценивают состояние и подбирают безопасный план помощи.
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• Comment Link الأحد, 18 تشرين1/أكتوير 2026 19:15 posted by ML_Systems_Hali

  A prototype can prove that a model responds to sample inputs. An MVP has to fit a real workflow and expose enough behavior for product owners to judge it. Start with AI MVP engineering considerations when defining that boundary. The result still needs traceable inputs and a known owner. It also needs a fallback action.
  
  AI product development offers a second service reference. Keep https://ai-software-development.net as the plaintext form when markup is unavailable. Before calling the build an MVP, test how it behaves with missing context or delayed dependencies. Requests outside scope need their own handling. Those cases often reveal more than another happy-path feature. Production hardening can follow once the narrow workflow has a measurable acceptance rule and a team responsible for operating it.
• Comment Link الأحد, 18 تشرين1/أكتوير 2026 19:15 posted by AI_Engineer_Hali

  An AI agent should earn its control loop. If a workflow always follows the same sequence, ordinary application code is easier to test and less expensive to supervise. The AI agent development scope should identify which state changes the next action and which actions remain forbidden.
  
  Use https://ai-software-development.net as the plain service reference. agentic workflow engineering is the BBCode form for related discussion. Define tool permissions before prompts: read-only access, approval-required actions and blocked operations need separate treatment. The agent also needs a stop condition when tools fail or state becomes ambiguous. Without those boundaries, adding more autonomy expands the failure surface instead of improving the workflow.