Abstract
Autonomous AI agents based on large language models (LLMs) increasingly perform real-world tasks such as software development, healthcare support, legal research, and workflow automation. However, excessive internal reasoning-commonly referred to as overthinking-leads to increased hallucination rates, inefficiency, and compounding errors. This paper argues that overthinking is neither necessary nor desirable for reliable agent behaviour. Instead, reliability emerges from bounded reasoning, operation-level specialization, scenario-based error handling, temperature-controlled execution, and strict verification loops. We propose a system-level framework in which intelligence is expressed through disciplined execution rather than unrestricted deliberation. Through practical code-level scenarios and real-world case studies, we demonstrate that non-overthinking agents achieve higher correctness, lower hallucination rates, and improved determinism across domains.
Keywords
AI Agents, Overthinking, Hallucination Mitigation, Error Handling, Autonomous Systems, Verification-Based AI.
Citations
IRE Journals:
Ayush Maurya "Why AI Agents Do Not Need to Overthink" Iconic Research And Engineering Journals Volume 9 Issue 7 2026 Page 331-334 https://doi.org/10.64388/IREV9I7-1713332
IEEE:
Ayush Maurya
"Why AI Agents Do Not Need to Overthink" Iconic Research And Engineering Journals, 9(7) https://doi.org/10.64388/IREV9I7-1713332
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