New Guide: 6 Mistakes Breaking Your Production Agents

2026-01-08 · Source: Learn AI Together · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

A new free 6-day email course, "Agentic AI Engineering Guide: 6 Mistakes Developers Make When Building Agents," has been released to address common production failures in agentic AI systems. Developed with Paul Iusztin (Decoding ML), the course identifies six predictable mistakes that cause agents to fail in production despite working in testing. These include mismanaging context, building complexity prematurely, defaulting to agents over workflows, using regex for output parsing, blindly copying patterns, and skipping evaluations. The course provides a repeatable framework to diagnose, understand, and fix these issues, offering daily lessons on failure patterns, root causes, and practical solutions with immediate examples.

Key takeaway

For AI Engineers struggling with agentic systems failing in production, you should enroll in the "Agentic AI Engineering Guide" to learn a structured framework for diagnosing and preventing common issues. This course will equip you with practical strategies for context management, output parsing, and evaluation, helping you build more reliable and predictable AI agents before deployment.

Key insights

Six common mistakes consistently break AI agents in production, but they are predictable and fixable.

Principles

• Context is a scarce resource.
• Earn complexity; start simple.
• Enforce structured output contracts.

Method

The course outlines a framework to diagnose agent failures by identifying common mistakes, understanding their root causes, and applying specific fixes with practical examples.

In practice

• Choose workflows vs. agents based on problem.
• Manage context strategically.
• Build evaluations from day one.

Topics

• Agentic AI Engineering
• Production Agents
• Context Management
• Output Parsing
• Agent Evaluation

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• 6 Mistakes Breaking Your Agents — Reliable agentic AI systems require understanding and avoiding six common engineering mistakes.
• Why Production AI Agents Fail in Ways You Won’t See Coming (Part 1) — AI agents fail silently in production due to unique structural properties, leading to high costs and undetected errors.
• Go from 'my agent runs' to 'it ships.' — Reliably building and deploying AI agents requires robust engineering practices, evaluation, and monitoring.
• Your Agent Loop Is Fine. Your Tools Are Why It Breaks. — Agent reliability hinges on well-defined tools, not complex orchestration loops.
• From Prompts to Production: 2 Hands-On AI Workshops This April — AI system failures often result from inconsistent usage and poor context design, not inherent model flaws.
• Why AI Agents Need Structure — AI agent failures stem from structural workflow issues, not model intelligence, requiring context isolation between distinct phases.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Learn AI Together.