https://datatweets.com/courses/ai-agents/reliability-and-evaluation/Recent content in Reliability and Evaluation on DATATWEETSHugoenCopyright (c) 2025 DatatweetsSun, 28 Jun 2026 09:00:00 +0200https://datatweets.com/courses/ai-agents/reliability-and-evaluation/lesson-1-why-reliability-and-evaluation/Fri, 06 Feb 2026 09:00:00 +0200https://datatweets.com/courses/ai-agents/reliability-and-evaluation/lesson-1-why-reliability-and-evaluation/The gap between ‘works when I try it’ and ‘works for every user, every time, affordably’ is where agents fail in the real world. This lesson maps that gap to four pillars — guardrails, retries, cost control, and evaluation — and sets up the module that closes it.https://datatweets.com/courses/ai-agents/reliability-and-evaluation/lesson-2-guardrails/Fri, 06 Feb 2026 09:00:00 +0200https://datatweets.com/courses/ai-agents/reliability-and-evaluation/lesson-2-guardrails/Guardrails protect both ends of the agent: an input check refuses out-of-scope or unsafe requests before you spend anything, and an output check validates the answer and repairs it if it’s empty or incomplete. This lesson builds both in Python around the loop you already have, extending the validate-then-repair-or-refuse discipline from Module 3 to the agent’s whole boundary.https://datatweets.com/courses/ai-agents/reliability-and-evaluation/lesson-3-retries-timeouts-and-cost-control/Fri, 06 Feb 2026 09:00:00 +0200https://datatweets.com/courses/ai-agents/reliability-and-evaluation/lesson-3-retries-timeouts-and-cost-control/Real agents run over flaky networks and on a budget. This lesson builds two small, verified patterns that wrap the agent loop: retries with exponential backoff and an attempt cap for robustness, and a token budget for affordability — so no single run can crash on a hiccup or run away with your bill.https://datatweets.com/courses/ai-agents/reliability-and-evaluation/lesson-4-evaluating-agents/Fri, 06 Feb 2026 09:00:00 +0200https://datatweets.com/courses/ai-agents/reliability-and-evaluation/lesson-4-evaluating-agents/You can’t improve what you don’t measure. This lesson builds a small evaluation harness — a fixed test set of cases, an LLM-as-judge that returns PASS or FAIL against a rubric, and a pass rate you track over time. Run it on every change and regressions stop hiding until users find them.https://datatweets.com/courses/ai-agents/reliability-and-evaluation/lesson-5-guided-project-production-ready-atlas/Fri, 06 Feb 2026 09:00:00 +0200https://datatweets.com/courses/ai-agents/reliability-and-evaluation/lesson-5-guided-project-production-ready-atlas/The course capstone: take the Atlas you built across seven modules — loop, tools, memory, planning, retrieval, multi-agent — and wrap it in Module 8’s four protective layers. Guardrails refuse or repair, retries survive blips, a budget caps spend, and an eval harness scores quality so you can ship changes with confidence.