Introducing NAC, an Open-Source Harness for Long-Running Agent Work

Arcee AI introduces Nac, an Apache-2.0 open-source agent harness for complex, long-running tasks that separates temporary worker context from persistent task state to reduce context degradation. Based on a thread-and-episode architecture, Nac uses a non-executing orchestrator to plan and dispatch bounded tasks to fresh worker processes, which return concise episodes containing durable handoffs, results, and relevant artifacts while their full execution contexts are discarded. Threads retain ordered episode histories, and selected episodes can be routed across threads as dependencies, enabling parallel execution through validated acyclic task graphs while preserving synchronization points and failure reporting. Nac is positioned as an inference runtime rather than merely a workflow tool because it governs context construction, scheduling, tool-driven effects, persistent state, capabilities, failure behavior, and task completion. It is intended for decomposable work such as research reproduction, large code migrations, parallel code changes, reviews, experiments, and infrastructure tasks, while simpler single-session tasks may be better handled directly. Nac also includes an MCP server that allows interactive agents to launch, monitor, and steer background jobs, supporting a model in which human-facing agents manage longer-running computations.

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