Multi-Agent Coordination

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  • Argus Deep Read: Long-Running Agents Need a Runtime, Not a Longer Prompt

    Advanced Agent runtime, safety, and evaluation
    Multi-Agent Coordination · Part 1 · Note · Aug 7, 2026 · Paper · 2026 · AI Agent

    A critical reading of Argus's Manager–Planner–Engineer–Reviewer runtime, durable state, verification-gated evolution, and rollback, separating benchmark results from author-operated case studies and unproven self-learning claims.

    Understand it in 90 seconds
    Problem
    a longer prompt does not give a long-running agent explicit task authority, auditable state, verification gates, or recovery boundaries.
    Core insight
    Argus cycles Manager, Planner, Engineer, and Reviewer over durable project state; memory, skills, routing, and procedures persist only after role-owned review.
    Strongest evidence
    across seven task-native arenas, the report gives breadth evidence and reports 78% for Argus versus 59% for Direct Copilot on SWE-Bench Pro under GPT-5.5, at roughly 1.41x aggregate tokens (Figure 1; Section 5).
    Main boundary
    this is an arXiv v1 technical report. The implementation, prompts, traces, checkpoints, and complete benchmark package are not public, so the reported outcome is not a reproducible adoption proof.
    Read the full deep dive

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