Arcee AI released NAC today, an Apache-2.0 licensed agent harness the company built internally for its own long-running engineering work — and has been using since April. The pitch is specific and a little unusual: NAC’s central orchestrator is deliberately forbidden from executing commands or editing files itself. It can only plan, decompose work, and dispatch it to worker threads. All the actual doing happens somewhere else.
That constraint is the whole design, and it’s worth understanding why Arcee thinks it matters.
The Problem: Context Rot
Arcee’s blog post opens with a familiar complaint for anyone who’s run a coding agent on a genuinely long task: as an agent spends tens of thousands of tokens reading code, editing, debugging, and re-editing, it gradually loses track of the finer details from earlier in the conversation. In extreme cases, the agent ends up executing a task in a way the user never actually intended, because the original context has been diluted by everything that happened afterward. Researchers have a name for this: context rot, a documented phenomenon where model performance degrades over sufficiently long context windows — cited in the post via Chroma’s technical report on the subject.
Most agent systems, Arcee argues, don’t distinguish between different stages of work. Investigation, tool output, false starts, evolving plans, and the state of the broader task all pile into one growing transcript. When that transcript eventually needs to be compacted to fit the context window, the compaction is triggered by token pressure rather than task structure — meaning it can carelessly discard details that actually mattered.
NAC’s answer is to separate two things that usually get conflated: the temporary context needed to perform one action, and the persistent state needed to continue an entire workstream.
How It Actually Works: Threads and Episodes
NAC is built around what Arcee calls a thread-and-episode architecture, adapted from a pattern described in Random Labs’ “Slate” report. The mechanics, per the official blog post:
A central orchestrator plans and decomposes the overall task. When it decides work needs to happen, it dispatches a thread — a named, ordered collection of past episodes — with a bounded action for a fresh worker to execute. That worker is a genuinely new process: fresh model context, the worker system prompt, the requested action, available tools, and any relevant project or skill instructions. Critically, there’s no separate summarization step. Instead, the worker’s own system prompt tells it that its final response — the point where it stops calling tools — is the episode: a concise, structured handoff describing what it accomplished, useful files produced, and results.
Once that final response lands, the worker’s entire execution context is discarded and never touched again. Its changes to the environment persist, but the only thing that survives into future context is that one structured episode.
The next time the orchestrator wants that thread to do more work, it spins up a brand-new worker — fresh context again — but this time hands it the requested action plus every episode already accumulated in that thread’s history. The orchestrator can also pull in episodes from other threads as supplementary context for a new dispatch, a capability Arcee (following Slate’s terminology) calls “thread weaving.” Those borrowed episodes inform the new work but don’t get copied into the target thread’s permanent history — only the new worker’s own output does.
Dispatches happen in batches, and NAC builds an explicit dependency graph across each batch: if thread B needs thread A’s fresh output as a source, that’s a dependency edge, and NAC validates the whole graph is acyclic before running anything. Independent threads within a batch run in parallel. The orchestrator waits for the full batch to finish, receives back the successful episodes and any errors, and then plans its next move — no polling required, since results simply appear at the batch boundary.
Why Restrict the Orchestrator?
The obvious question: why not let the orchestrator just do the work itself when it’s simple enough? Arcee’s argument is that keeping the orchestrator strictly in a “decide and route, don’t act” role keeps it focused entirely on interpreting the user’s actual intent and decomposing the overall task — without that judgment getting polluted by execution details like package-manager retries, profiling noise, or files it had to read along the way just to understand the codebase.
As a concrete example from the post: optimizing a specific hot path in a web app. A single, undifferentiated agent would need to explore the whole repo, set up the environment, and locate the relevant code — all before it could even begin profiling and proposing fixes, while somehow still holding onto the user’s original intent from many turns earlier. NAC’s orchestrator instead dispatches separate threads to explore the codebase and set up the environment in parallel, in a single orchestrator turn, then routes profiling to another thread — receiving back only the measurements and options it actually needs, none of the intermediate noise.
Where NAC Fits — and Where It Doesn’t
Arcee is refreshingly direct about NAC’s limits: for a single focused change that fits comfortably in one coding-agent session, going direct is simpler and faster. NAC adds overhead precisely because the orchestrator can’t act itself — it always has to delegate, even for something trivial.
Where it earns that overhead, per the post, is complex-but-decomposable work with well-separated parts and a concrete definition of done: reproducing a machine learning paper’s results, porting a large codebase between languages while preserving behavior, running a long list of isolated changes that don’t need to be handled sequentially, or managing several long-running processes (implement → run experiments → evaluate) where returning to an earlier thread later doesn’t require reconstructing its history from scratch.
NAC also ships an MCP server, letting tools like Claude Code or Codex dispatch, monitor, and steer NAC jobs the way they’d use any other tool — positioning an interactive coding agent as a “meta-orchestrator” that spots decomposable work mid-conversation, hands it off to NAC as a background job, and keeps the human-facing session responsive while the heavier work runs asynchronously.
Getting Started
NAC is available now under Apache-2.0 at github.com/arcee-ai/nac. Arcee provides a one-line install for the current edge build:
curl -fsSL https://raw.githubusercontent.com/arcee-ai/nac/main/scripts/install.sh | sh
For teams already frustrated by agents losing the plot on multi-hour tasks, NAC’s core bet — separate the transcript you need right now from the state you need to keep — is a genuinely different architecture than most harnesses currently ship with. Worth a look if context rot is costing you real engineering time.
Sources
- Introducing NAC, an Open-Source Harness for Long-Running Agent Work — Arcee AI Blog
- arcee-ai/nac — GitHub Repository
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260813-2000
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