AI Agents Fail in Production Environments

A team deploys a new agentic feature, and in the demo environment, it runs smoothly. A week later, in production, a routine cluster update triggers a breakdown. The agent forgets its task, abandons changes, and leaves systems in disarray. Customers grow frustrated. The feature, in reality, fails. This scenario is common. Engineering teams often spend excessive time fixing such issues rather than innovating. Organizations of all sizes face variations of this daily. Teams usually blame the LLM first, assuming hallucination or loss of context. But logs often show the model acted as intended. Once agents act, failure becomes harder to trace. The root issue lies beneath the agent’s surface.

Ensuring the Execution Layer: 4 Ways to Evaluate AI Agent Durability

The resilience of agentic systems depends on guarantees for inputs and execution order. When agents handle long-running, distributed, or consequential tasks, the key question becomes: What execution guarantees does the system provide? Teams can assess these capabilities using an execution maturity matrix across four axes. The matrix isn’t a linear path—real systems rarely mature cleanly. Its purpose is to highlight what limits the system. Enforcing these guarantees requires end-to-end automation: infrastructure provisioning, work routing, sandbox lifecycle management, scaling, environment cleanup, and recovery from infrastructure failures. For example, if a server crashes mid-task, the system automatically moves the agent to a healthy server to resume work, not restart it.

Execution Durability: State lives in memory at the primitive end. A crashed process destroys context and pending tool calls. At the mature end, every step persists. The system knows what happened, what’s in flight, and what must happen next. Work Duration: Primitive systems handle tasks lasting seconds within a single session. Mature systems support durable timers, waiting without holding threads, polling, periodic jobs, human approvals, and resumable work. Agents and sub-agents can communicate across failures, and work can run safely for days or months. Hosting and Isolation: In mature systems, dangerous operations like shell commands run in provisioned, isolated environments with lifecycle management. Quality of Service (QoS): Systems without flow control face unpredictable slowdowns. Mature systems manage backpressure, priorities, fairness, rate limits, quotas, and predictable degradation. The system decides who gets capacity, when, and how much.

Other dimensions, like security, identity, observability, and cost, intersect all four axes. Enforcement matters where stakes are high. If an agent moves money, the approval path must be structurally enforced. A prompt suggestion isn’t enforcement. Most production agentic systems excel at hosting and isolation. Many teams use harnesses, cloud execution, sandboxes, and lifecycle management. But execution reliability is rare. An agentic system isn’t just a loop—it’s workflows: control plane code that coordinates tools, manages state, and connects steps. Whether engineers write the code or agents generate it, much of it is throwaway, relying on temporary sessions. A durable execution layer turns workflows into durable automation, regardless of authorship. Completion is guaranteed or failure is explicit. Execution resumes after crashes, timers are durable, and sub-agents communicate across failures.

Agentic systems evolve rapidly. If every application carries its own durability, retries, timers, recovery, versioning, and coordination logic, teams will either move too slowly or fail in production. Often, they do both. Teams should stop rebuilding reliability primitives in every agent codebase. Focus on product behavior, not machinery required to sustain it. The agent framework defines what the agent does. A durable execution layer ensures that work recovers and scales when infrastructure fails. As models take on more consequential tasks, the execution layer determines whether they can do it safely and meet required guarantees.

By Max Fateev, CTO and Co-Founder of Temporal. Max is a 20-year veteran of AWS, Google, and Uber, with engineering leadership experience. He led the development of SQS replicated message store and Simple Workflow service at AWS, then co-created Cadence (Temporal’s predecessor) at Uber. Today, millions of Temporal workflows run daily for high-reliability and high-scalability workloads from Stripe to Datadog to Snapchat.