A developer running production agents for customers describes an incident where a model provider rejected an agent's tool calls — GPT-5.6 required a reasoning-effort setting the developer didn't configure — and the runtime retried over 1,200 times across 16 hours with no crash or alert. The incident led them to build Toren, an Apache 2.0 open-source runtime for long-running agents, and take the infrastructure side of agent ops more seriously. The post highlights silent retry loops as an operational failure mode practitioners should guard against.
A Reddit discussion catalogs recurring failures in unattended AI agents — runaway spending, tool-call loops, and false completion claims — and asks how practitioners handle done verification, stall detection, spend limits, and pause/resume. Commenters report max_iterations either kills legitimate long tasks or catches loops too late, and most rely on manual check-ins, undermining unattended operation.
A Reddit discussion catalogs recurring failures in unattended AI agents — runaway spending, tool-call loops, and false completion claims — and asks how practitioners handle done verification, stall detection, spend limits, and pause/resume. Commenters report max_iterations either kills legitimate long tasks or catches loops too late, and most rely on manual check-ins, undermining unattended operation.
A developer asks how practitioners recover long-running AI agent workflows that crash mid-run, covering duplicate model-call billing, runaway loops, and checkpointing. The thread targets production reliability practices like queues, workflow tools, and custom recovery logic.
A developer asks how practitioners recover long-running AI agent workflows that crash mid-run, covering duplicate model-call billing, runaway loops, and checkpointing. The thread targets production reliability practices like queues, workflow tools, and custom recovery logic.
A developer building an agent runtime in Rust opened a discussion on how to divide responsibility between the model and the harness. The core loop is straightforward, but failure cases — repeated tool calls, partial failures, oversized context, rollback, restarts, and premature completion claims — force reliability logic out of the model. The post asks practitioners which responsibilities they eventually moved into the harness and which should stay model-driven.
A developer building an agent runtime in Rust opened a discussion on how to divide responsibility between the model and the harness. The core loop is straightforward, but failure cases — repeated tool calls, partial failures, oversized context, rollback, restarts, and premature completion claims — force reliability logic out of the model. The post asks practitioners which responsibilities they eventually moved into the harness and which should stay model-driven.
A developer running an unattended 4am pipeline reports that three of four first attempts at a hosted image-generation step hung for 180 seconds and timed out, yet every retry succeeded in 7-10 seconds and the run shipped green with no alerting. The post asks how to monitor first-attempt success rate and tune timeouts when retries hide failures.
A developer running an unattended 4am pipeline reports that three of four first attempts at a hosted image-generation step hung for 180 seconds and timed out, yet every retry succeeded in 7-10 seconds and the run shipped green with no alerting. The post asks how to monitor first-attempt success rate and tune timeouts when retries hide failures.
A team running scheduled agent jobs off one account hit a weekly usage limit in mid-September, killing every model-booting job for about 19 hours while plain Python scripts kept running. The failure went unnoticed because the agent that would report it could not run. The team now uses plain-code gates, quota-specific failure handling, and cheaper default models.
A team running scheduled agent jobs off one account hit a weekly usage limit in mid-September, killing every model-booting job for about 19 hours while plain Python scripts kept running. The failure went unnoticed because the agent that would report it could not run. The team now uses plain-code gates, quota-specific failure handling, and cheaper default models.