We broke our agents, so you don't have to

2024-09-10 · Source: Towards AI Newsletter · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Intermediate, quick

Summary

Towards AI has launched "Agentic AI Engineering," a new course co-created with Paul Iusztin, designed to teach developers how to build, evaluate, and deploy autonomous AI systems reliably. The course, developed over nine months and refined with 180 alpha testers, focuses on addressing the challenges of agent reliability in production environments, such as tool failures, messy inputs, and latency issues. Participants will build two agent systems: a Research Agent that uses iterative loops and human-in-the-loop checkpoints, and a Writing Workflow Agent that generates multi-modal outputs using evaluator-optimizer patterns. A core component of the curriculum emphasizes designing eval datasets, implementing LLM judges, adding observability with tracing, and setting up monitoring for debugging and deliberate system improvement. The course is priced at $499 for the next 100 seats, offering lifetime access, Discord community access, and a 30-day refund policy.

Key takeaway

For AI Engineers building autonomous systems, prioritizing operational reliability is critical for production success. This course offers a structured approach to designing, evaluating, and deploying agents that can withstand real-world challenges. You should consider this training to gain practical skills in building robust agent systems, implementing effective monitoring, and debugging regressions quickly, ensuring your AI solutions are dependable in 2026 and beyond.

Key insights

Reliable AI agent development requires robust evaluation, monitoring, and debugging strategies for production readiness.

Principles

• Operational reliability is key for production AI agents.
• Design for failure modes in agent systems.
• Controlled autonomy is crucial for agent performance.

Method

The course curriculum was built by pushing a system to its breaking point, then converting identified failure modes into lessons, refined through alpha testing.

In practice

• Build Research Agents with human-in-the-loop checkpoints.
• Develop Writing Workflow Agents using evaluator-optimizer patterns.
• Implement LLM judges and pass/fail checks for agent evaluation.

Topics

• AI Agents
• Agentic AI Engineering
• AI System Reliability
• LLM Evaluation
• MLOps

Best for: AI Engineer, MLOps Engineer, Software Engineer

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• Why Good AI Agents Look Less Like Chatbots and More Like Workshops — Reliable AI agents prioritize constraint and human governance over unbridled autonomy to counter "context rot."
• The 7 Skills You Need to Build AI Agents — Building production-ready AI agents demands a broad engineering skillset beyond basic prompt crafting.
• Your Next Hire Doesn't Sleep: An Engineer's Honest Take on Agentic AI in 2026 — The primary challenge for AI agents is not capability, but the significant gap between demo performance and reliable production deployment.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI Newsletter.