Why AI Agents Fail in Production (And How Engineering Teams Are Fixing It in 2026)

A write-up that frames agent production failures around infrastructure and orchestration rather than model quality. The argument is that the typical failure modes — context window mismanagement, flaky tool calls, missing retry logic, no observability — are boring engineering problems dressed up in AI clothes. The fixes it walks through are sensible: deterministic fallbacks, structured logging, separation of planning and execution concerns. What it adds beyond a typical blog post is specificity around 2025-era toolchains — references to real orchestration patterns that teams are actually using rather than toy examples. The reservation is that this is editorial content, not a tool. It will not show up in a dependency file or save a Saturday. Treat it as a pre-read before an architecture session rather than a tool evaluation. If the team is already running agents in production and has burned their fingers on these exact failure modes, most of the content will land as confirmation rather than revelation. -> Best for: technical PM or AI engineer scoping a new agent system.