AI agents are increasingly moving beyond code generation to executing tasks independently in production systems. However, when agents modify files, interact with external services, and trigger real operations, failures can have serious consequences. A crashed worker, outdated approval, or lost confirmation can lead to duplicate actions or unauthorized changes. This webinar explores how durable execution, idempotency, and clear permission boundaries help engineers build AI agents that can recover from failures while keeping their actions safe and predictable.
Speaker
Maksim Zayats is a Staff Software Engineer at Constructor, an AI-powered ecommerce product discovery platform that helps companies improve search, recommendations, and personalization to drive revenue and conversions. He focuses on building AI features that work under real production constraints in large-scale systems.
Why Idempotency Alone Is Not Enough
Idempotency allows operations to be retried without creating duplicate effects, but it does not solve every reliability challenge in AI agent execution.
An agent may crash after completing a task, attempt to reuse an outdated approval, or repeat an operation whose outcome is unknown. Even when retrying is technically safe, the system must verify that the action remains authorized and its inputs have not changed.
Reliable execution therefore requires more than preventing duplicates. It also means preserving completed work, tracking operation identities, and ensuring that every action is based on valid evidence and permissions.
Making Agent Workflows Survive Failures
When an AI agent’s worker process crashes, completed work should not disappear with it.
Durable execution systems such as Temporal record workflow progress and the results of completed operations. This allows another worker to resume the task without unnecessarily repeating previous steps.
For example, an agent repairs a Python service, successfully runs tests, and then its worker process crashes while waiting for approval. Because the results were recorded before the crash, a replacement worker can continue from the saved state without rerunning completed activities.
The key distinction is whether the completed work was durably recorded. Only recorded results can be safely reused during recovery.
Keeping Permissions Connected to the Right Actions
Approvals must remain tied to the specific actions and artifacts they authorize.
If an agent receives approval to submit a code change but modifies the code afterward, the original permission should no longer apply. Before submission, the system must verify that the current artifact matches the approved version.
Permissions can be tied to a specific operation, patch digest, and expiration time. If the prepared code changes, the system rejects the submission before the request reaches the target system.
This approach allows agents to perform routine tasks autonomously while maintaining strict control over actions that produce effects in external systems.
Handling Retries and Uncertain Outcomes
One of the most challenging scenarios occurs when an external system successfully completes an operation but its confirmation is lost.
For example, a change request is successfully stored, but the acknowledgement never reaches the sender. The system retries the submission using the same operation identity. This allows the target system to return the original result instead of creating a duplicate.
However, this protection only works when the receiving system supports reliable operation identification and result recovery.
If the outcome cannot be verified, the correct approach is to preserve an UNKNOWN state, prevent a replacement write from being treated as a new operation, and investigate what happened rather than assume success or failure. Retry limits must also survive worker restarts to prevent uncontrolled repeated execution.
Conclusion
Reliable AI agents need more than idempotent operations. They require durable workflows, precise authorization mechanisms, stable operation identities, and clear rules for handling failures.
By preserving completed work and verifying permissions before each action, engineering teams can prevent duplicate operations, recover interrupted tasks, and safely handle uncertain outcomes.
As AI agents become increasingly capable of executing real operations, the key challenge is not simply making them more autonomous, but ensuring that their actions remain recoverable, authorized, and verifiable.
