Your AI Agent Isn’t Hallucinating. It’s Failing By Design.

2026-05-16 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

Most failures in agentic AI systems are engineering failures, not model hallucinations, stemming from issues in the surrounding system rather than the LLM itself. These failures are often predictable and preventable. The article identifies three common failure modes: infinite loops (the "hamster wheel"), where agents repeat tasks without progress due to lack of termination conditions, action tracking, or progress signals; "hallucinated planning," where agents create unexecutable plans by assuming non-existent tools or capabilities; and "unsafe tool use," where agents perform catastrophic actions due to over-privileged tools. The author, Faisal Feroz, a Chief Technical Architect, emphasizes that effective agent design requires robust engineering discipline, focusing on system architecture rather than just the LLM.

Key takeaway

For AI Architects and MLOps Engineers designing agentic AI systems, prioritize robust system engineering over solely focusing on LLM capabilities. Implement strict termination conditions, detailed action tracking, and clear progress signals to prevent infinite loops. Crucially, separate planning from execution with validation gates and apply the principle of least privilege to all agent tools to mitigate unsafe actions and ensure system reliability and security.

Key insights

Agentic AI failures are primarily engineering design flaws, not LLM hallucinations, and are largely preventable.

Principles

• An AI agent is a system, not just an LLM with tools.
• Apply least privilege to agent tools.
• Separate planning from execution in agent workflows.

Method

Implement guardrails like max steps/retries, track agent actions, define clear progress signals, document tool schemas thoroughly, and build approval workflows for high-risk actions.

In practice

• Cap max steps, retries, and runtime for agents.
• Write detailed tool schemas for clarity.
• Tier tools by capability: read, write, delete.

Topics

• AI Agent Failures
• Engineering Discipline
• Large Language Models
• Agent System Design
• Tool Use Security

Best for: AI Engineer, MLOps Engineer, AI Architect

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• AI Agent Tool Design: What Works and What Doesn’t — Effective AI agent performance hinges on robust tool design, not just advanced model capabilities.

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.