OpenTiger – Autonomous dev orchestration that never stops
Hi HN. I've been running AI coding agents (Claude Code, Codex, etc.) on real repos for a while now. The dirty secret of "autonomous coding" is that agents stop all the time — quota limits, test failures, policy violations, bad judgement calls. You end up babysitting them. So I asked a different question: what if the system was designed around the assumption that agents WILL fail, and the job of the infrastructure is to never let that failure become a dead end? openTiger is a "non-human-first" orchestration system that runs multiple AI agents in parallel — planner, workers, testers, judge —…
In plain words
OpenTiger is an orchestration system that manages multiple AI coding agents working in parallel on software development tasks. It addresses the practical problem of AI agents stopping mid-task due to quota limits, test failures, or errors by designing the system to treat agent failure as inevitable and route around it automatically. The platform uses specialized agents in a pipeline—a planner that breaks down requirements, workers that execute tasks concurrently, testers that validate results, and a judge that evaluates outcomes and directs rework. It is designed for developers who want to run autonomous coding agents on real repositories without constant manual intervention.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN. I've been running AI coding agents (Claude Code, Codex, etc.) on real repos for a while now. The dirty secret of "autonomous coding" is that agents stop all the time — quota limits, test failures, policy violations, bad judgement calls. You end up babysitting them. So I asked a different question: what if the system was designed around the assumption that agents WILL fail, and the job of the infrastructure is to never let that failure become a dead end? openTiger is a "non-human-first" orchestration system that runs multiple AI agents in parallel — planner, workers, testers, judge — each with a dedicated role. The planner decomposes requirements into tasks, the dispatcher fans them out to worker agents concurrently, and the judge evaluates results and feeds back rework decisions. It's not one agent doing everything; it's a pipeline of specialized agents running simultaneously. The entire architecture is built on one principle: no state is terminal. Every failure is a blocked state with a reason, and every reason has a recovery path. If the same failure repeats, the system escalates to a different strategy instead of retrying the same thing. The interesting philosophical bit: optimizing for recovery turns out to be more effective than optimizing for first-attempt success. When you stop fearing failure, you can let agents be more aggressive. Early stage, lots to improve. Feedback and contributions welcome. Docs: https://opentiger.dev/docs/
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