agent/papers/items/2026-2606-01416-self-healing-agentic-orchestrators-for-reliable-tool-augmented-large-language-mo.md

Paper: Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems

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type: paper title: Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems authors: Rahul Suresh Babu, Adarsh Agrawal year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.01416 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-05-31 updated_at: 2026-05-31 status: queued relevance: high topics:

• agent-evaluation
• computer-use
• memory
• planning
• rag
• reasoning
• tool-use
• workflow-agent methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 20 collection_queries: planning-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: planning-agent
• inferred topics: agent-evaluation, computer-use, memory, planning, rag, reasoning, tool-use, workflow-agent
• arXiv categories: cs.AI
• collection score: 20

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.01416