Show HN: Enju – humans, AI agents, and compute as peers on one workflow graph
Enju is a collaborative workflow system that integrates humans, AI agents, and deterministic compute within a shared directed acyclic graph (DAG). Tasks can be created, reviewed, and modified in real-time, allowing for a dynamic workflow where human decisions are treated equally to AI outputs. The system operates using a single binary and utilizes git for version control and content transport, making it accessible for various users.
- ▪Enju allows humans, AI agents, and deterministic compute to work as peers on a shared workflow graph.
- ▪Tasks are represented as a DAG and can spawn new tasks during execution, enabling iterative feedback and revisions.
- ▪The system tracks task states and decisions without storing content, relying on git for version control.
Opening excerpt (first ~120 words) tap to expand
Enju (槐) Enju is a workflow system where humans, AI agents, and deterministic compute work the same DAG as peers. The unit of work is a task — something any of them can answer, review, vote on, or compute. The graph is live: a task can spawn more tasks while a run is in flight, so a review that returns request_changes drops a revision task back into the graph with its feedback already attached, and the work cycles until it's approved. What makes this work is where the lines are drawn. Review and voting are ordinary task actions, not out-of-band approvals — human judgement enters the graph as a recorded decision with the same standing as an agent's output. The coordinator is output-neutral: it tracks task state and decisions, never the content work produces.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.