arXiv:2606.19812v1 Announce Type: cross Abstract: Autonomous Large Language Model (LLM) agents are increasingly deployed in electronic discovery (e-discovery), where compounding errors across multi-step reasoning chains can constitute legal malpractice. Unlike single-turn retrieval, agentic workflows operating over privileged document corpora exhibit a class of failure we term "trajectory collapse": an early misclassification silently propagates, rendering an entire privilege review invalid. This paper makes three contributions. First, we propose a structured taxonomy of agentic failures in le
The increasing deployment of autonomous LLM agents in critical workflows like legal e-discovery is highlighting novel failure modes, requiring immediate attention to ensure reliability and avoid malpractice.
This development underscores the critical need for robust human oversight and error correction mechanisms in AI-assisted professional services to prevent significant financial and reputational damage.
The focus shifts from simply deploying AI agents to actively orchestrating human-on-the-loop workflows that mitigate complex, cascading AI failures in sensitive applications.
- · AI orchestration software providers
- · Legal tech firms adopting robust human-in-the-loop solutions
- · Legal professionals skilled in AI oversight
- · Specialized AI audit and compliance firms
- · AI providers offering unmonitored autonomous agents for critical tasks
- · Law firms relying solely on unverified autonomous AI
- · Traditional manual legal discovery services
Increased demand for frameworks and tools to manage agentic AI workflows with human intervention.
Development of industry standards and regulations specifically addressing human-on-the-loop requirements for AI in professional services.
The emergence of new liability models for AI failures, differentiating between autonomous and human-orchestrated systems.
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Read at arXiv cs.LG