Building Durable AI Agents

AI agents are transitioning from fragile, local experiments to durable, production-ready enterprise systems, requiring a fundamental shift in how developers architect agentic workflows. Hamza Tahir, co-founder of ZenML, defines the "harness"—the software layer managing an LLM—as the critical component that provides agents with a body, enabling them to execute actions and manage state. Current agentic systems often fail due to insufficient observability and lack of robust infrastructure, making checkpointing, retries, and replayability essential for reliability. As model performance commoditizes, the competitive advantage for enterprises lies in building internal platforms that support these durable, scalable agentic architectures. By moving beyond simple script-based execution toward standardized, open-source runtimes like Kitaru, organizations can better manage the entropy of non-deterministic AI processes, ultimately transforming agents from experimental tools into reliable, long-running components of enterprise infrastructure.