Building AI Agents That Survive Production

2026-05-14 · Source: MLOps.community · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, extended

Summary

Hayam, CTO of Union AI and co-founder of the Flyte open-source project, presented on building production-ready AI agents at the AI Agents 2026 conference in Seattle. He highlighted that while many experiment with agents, few successfully deploy them to production due to issues like crashes, memory limits, API throttling, and scaling challenges. Hayam emphasized that agents will inevitably fail in production, advocating for building agents that tolerate and recover from failures rather than trying to prevent them entirely. He introduced three core design principles for robust agent platforms: dynamic execution, durability, and defensibility. The presentation included a case study of Dragonfly, a company using a four-tiered agent system built on Union AI's platform to catalog over 250,000 software products reliably at scale.

Key takeaway

For AI Engineers and MLOps teams deploying AI agents, prioritize building systems that anticipate and gracefully recover from failures. Your focus should shift from preventing failures to designing agents with dynamic, durable, and defended architectures, ensuring long-running sessions maintain context and provide a reliable user experience even when underlying infrastructure or tools encounter issues.

Key insights

Production-ready AI agents must be designed for dynamic execution, durability, and defensibility to tolerate inevitable failures.

Principles

• Agents will fail in production; design for tolerance and recovery.
• Declare infrastructure needs directly within agent code.
• Record all agent actions for deterministic recovery and caching.

Method

Build agents with dynamic code execution, ensuring durability through crash recovery and caching of non-deterministic actions, and defensibility via sandboxed execution environments and human-in-the-loop capabilities.

In practice

• Implement retry mechanisms for external failures.
• Utilize shared caching for common agent calls.
• Employ sandboxed environments for untrusted code execution.

Topics

• AI Agent Production
• Failure Tolerance
• Agent Durability
• Secure Code Execution
• Human-in-the-Loop

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

Related on AIssential

• We broke our agents, so you don't have to — Reliable AI agent development requires robust evaluation, monitoring, and debugging strategies for production readiness.
• 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.
• The Agent Stack Bet — Current agent deployments suffer from a "stack ceiling" requiring fundamental architectural shifts for enterprise-grade reliability and governance.
• Agentic Programming: A Roadmap — Agentic programming enables AI to execute goal-driven workflows, requiring robust engineering beyond simple prompting.
• What AI Agent Developers Should Consider When Designing Agents for High-volume Environments — Scaling AI agents for enterprise requires a production-first architectural approach, focusing on reliability, efficiency, and security.
• 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.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by MLOps.community.