Agent harness for long-running AI workflows and persistent state
nac by Arcee Ai is an open-source agent harness designed to manage ambitious, long-running AI tasks that suffer context loss. The tool breaks projects into smaller work units and captures structured summaries to keep an orchestrator aligned with original goals while sub-agents run. Key aspects include persistent state tracking and a local runtime that supports session inspection. It targets AI developers and engineers building multi-step, agentic workflows who need sustained state across extended sessions.
What tasks is the tool actually designed to handle?
The app targets multi-step engineering work such as code debugging, infrastructure setup, and complex text localization, as noted in its stated use cases. It acts as an agent harness that keeps a long-running project coherent across many sequential steps, so teams can assign experiments or prototyping runs without restarting context after each interaction. This positions the tool for project-level automation rather than single-request conversational tasks.
How does it prevent context degradation over long sessions?
The tool uses a thread-and-episode architecture that separates ephemeral execution context from a persistent work state, and it updates the central planner with structured summaries called episodes. That design avoids accumulating raw transcripts by returning concise summaries from worker threads, which preserves project alignment across extended runs. The documented approach addresses context drift by keeping the orchestrator focused on updated state rather than raw chat history.
What environment and inputs does the tool require?
The tool is implemented in Rust and ships as a CLI plus a local web interface; installation is provided via a shell script in the repository. It runs on Linux and macOS and connects to models through supported endpoints, including the developer’s Trinity models, OpenAI-compatible APIs, and ChatGPT Codex via OAuth. The dashboard launches in a standard browser for session visibility and authentication management.
How easily does it integrate with other agent systems?
The tool includes a built-in Model Context Protocol server so it can be controlled by other MCP-compatible agents and act as a sub-agent in larger pipelines. Integration points listed in the documentation include control from existing MCP clients and session monitoring through the local web dashboard. That design makes it suitable for projects that already adopt the MCP ecosystem and need a programmatic, server-style runtime for agents.
Practical choice for teams prepared to integrate an open agent runtime
The tool is a practical option for engineering teams focused on long-horizon agent workflows, supported by community traction since mid-2026 and its Apache-2.0 open-source license. Teams should plan a short integration and verification phase to connect models and MCP pipelines before regular use. For groups prepared to invest that setup time, the app offers a maintainable foundation for sustained, auditable agent work.
