Building AI Agents: Complete Guide to Challenges, Processes, Problems & Solutions Core Challenges in AI Agent Development Hallucination: Agents generate confident but false information, worsening in reasoning chains where errors compound across steps [web:1][web:3]. Context Management: Long-term memory fails in multi-turn interactions, causing inconsistent decisions [web:2]. Tool Integration: Reliable API calls and error handling break under edge cases or rate limits. Scalability: Local LLMs like TinyLlama struggle with complex workflows on consumer hardware. Evaluation: Measuring agent success requires custom benchmarks beyond simple accuracy [web:5]. Standard Process to Build AI Agents Define Goals: Specify tasks (e.g., medical diagnosis workflow) and success metrics like 95% task completion. Select Architecture: Choose LLM backbone (GPT-4o-mini, L...