Shepherd: A Runtime Substrate Empowering Meta-Agents with a Formalized Execution Trace
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913161718792192 |
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| author | Yu, Simon Chong, Derek Nandi, Ananjan Soylu, Dilara Sun, Jiuding Manning, Christopher D Shi, Weiyan |
| author_facet | Yu, Simon Chong, Derek Nandi, Ananjan Soylu, Dilara Sun, Jiuding Manning, Christopher D Shi, Weiyan |
| contents | As LLM agent systems take on more complex tasks, they increasingly rely on meta-agents: higher-order agents that operate on other agents, much as managers supervise employees. Whatever a meta-agent does: coordinating agents, halting risky actions before execution, or repairing failed runs, requires manipulation of agentic execution at runtime. Existing agentic substrates make this hard: they give meta-agents only plain transcripts and environment snapshots, requiring it to build it's own tooling to reconstruct and orchestrate execution state. Therefore, we introduce Shepherd, a Python substrate grounded in functional programming principles, where an agent's execution is itself a first-class object that a meta-agent can inspect and transform. Every model call, tool call, and environment change becomes a structured event in a Git-like execution trace, where any past state can be forked 5x faster than docker commit and replayed. Three example use cases show Shepherd's versatility: (1) a supervisor agent prevents conflicts among parallel coding agents, lifting CooperBench performance from 28.8% to 54.7%; (2) a counterfactual optimizer repairs agent workflows by proposing edits and replaying runs from the point of changed behavior, outperforming MetaHarness on TerminalBench-2 with 58% lower wall-clock; (3) a meta-agent picks fork points during rollouts to improve credit assignment in long-horizon agentic RL, doubling GRPO's gains on TerminalBench-2. We open-source Shepherd to empower future meta-agents with principled and efficient operations over agentic execution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10913 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Shepherd: A Runtime Substrate Empowering Meta-Agents with a Formalized Execution Trace Yu, Simon Chong, Derek Nandi, Ananjan Soylu, Dilara Sun, Jiuding Manning, Christopher D Shi, Weiyan Artificial Intelligence Programming Languages Software Engineering As LLM agent systems take on more complex tasks, they increasingly rely on meta-agents: higher-order agents that operate on other agents, much as managers supervise employees. Whatever a meta-agent does: coordinating agents, halting risky actions before execution, or repairing failed runs, requires manipulation of agentic execution at runtime. Existing agentic substrates make this hard: they give meta-agents only plain transcripts and environment snapshots, requiring it to build it's own tooling to reconstruct and orchestrate execution state. Therefore, we introduce Shepherd, a Python substrate grounded in functional programming principles, where an agent's execution is itself a first-class object that a meta-agent can inspect and transform. Every model call, tool call, and environment change becomes a structured event in a Git-like execution trace, where any past state can be forked 5x faster than docker commit and replayed. Three example use cases show Shepherd's versatility: (1) a supervisor agent prevents conflicts among parallel coding agents, lifting CooperBench performance from 28.8% to 54.7%; (2) a counterfactual optimizer repairs agent workflows by proposing edits and replaying runs from the point of changed behavior, outperforming MetaHarness on TerminalBench-2 with 58% lower wall-clock; (3) a meta-agent picks fork points during rollouts to improve credit assignment in long-horizon agentic RL, doubling GRPO's gains on TerminalBench-2. We open-source Shepherd to empower future meta-agents with principled and efficient operations over agentic execution. |
| title | Shepherd: A Runtime Substrate Empowering Meta-Agents with a Formalized Execution Trace |
| topic | Artificial Intelligence Programming Languages Software Engineering |
| url | https://arxiv.org/abs/2605.10913 |