As enterprise AI agents transition into production, organizations are facing significant reliability challenges. Success depends not just on large language model (LLM) performance, but on ensuring long-running AI workflows can survive interruptions, maintain state, recover from failures, and manage costs while coordinating across APIs and tools.
After an initial period of rapid deployment, companies are now revisiting early agent designs to incorporate workflow orchestration, governance, observability, and recovery mechanisms, according to Preeti Somal, Senior VP Engineering at Temporal Technologies. Many are rebuilding their AI systems on more robust foundations to avoid crashes and costly restarts.
This shift highlights the complexity of agentic AI, which often involves multi-step processes spanning various models and services. Handling state—which tracks workflow progress—and memory—which captures ongoing context—is crucial for long-duration business processes, such as healthcare workflows where multiple stages of data processing occur.
Temporal’s approach centers on a “deterministic spine” that manages reliable workflow execution even when AI outputs vary. This helps enterprises avoid failures that would otherwise force expensive, time-consuming reruns. Additionally, orchestration provides clear visibility into where AI-related expenses accumulate, helping companies control inference costs.
Governance and internal frameworks that offer guardrails without stifling flexibility are also increasingly important as enterprises mature in their use of agentic AI. Temporal’s platform, already part of many enterprises’ infrastructure, naturally extends to support the evolving needs of AI-driven workflows.
Summary:
Enterprises deploying AI agents in production face complex reliability and cost challenges that require robust architecture beyond the AI models themselves. Solutions focusing on workflow orchestration, failure recovery, state management, and cost visibility are essential for long-running agentic AI processes. Leaders are rebuilding earlier implementations, emphasizing durable systems that can recover from interruptions and provide governance, driving more effective and efficient AI operations.
