Many engineering teams treat AI agents as standard microservices, which works until an agent requires long-running execution or complex tool chains. When an agent evolves from a single request into a multi-step workflow, it behaves more like a long-lived job than a stateless function.
Misclassifying these agents leads to common production failures, including lost state during container restarts, duplicate tool calls, and observability gaps. Addressing these issues requires shifting focus from prompt engineering to workflow orchestration.
In short
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Production agents require dedicated task queues to prevent fast tasks from being blocked by long-running processes.
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In-process memory is insufficient for persistent agents; state must be offloaded to durable storage like Postgres or Redis to survive restarts.
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Idempotency is mandatory for retry logic, as replaying a failed step without it often results in duplicate side effects or tool calls.
The Workflow Problem
An agent that runs for hours cannot rely on in-process Python dictionaries for memory. If a container restarts, the agent loses its context, forcing a complete restart of the workflow. This is inefficient and costly for complex tasks.
Teams must implement durable state persistence. While Redis offers speed, it can be lossy. Postgres provides the necessary durability for long-running jobs, though it introduces latency on every step. Architects must balance these trade-offs based on the specific requirements of the agent's task duration.
Retry Semantics and Idempotency
When a multi-step agent fails at step 12 of 30, the recovery strategy determines the system's reliability. A naive retry of the entire workflow is often destructive if the agent has already performed side effects.
True production resilience requires idempotency. Each tool call must be designed so that repeating it does not cause duplicate actions. Without this, the system cannot safely replay individual steps, forcing developers to choose between manual intervention or unreliable automated retries.
Source
Task Queues, State, and Retries: AI Agent Workflow Orchestration Production Guide | GMI Cloud
https://gmicloud.ai/en/blog/ai-agent-workflow-orchestration-production-2026
