Longrunning agents and failure resilience

From: aidotengineer

When deploying AI agents into production, several key considerations arise, particularly concerning their ability to operate for extended periods and recover from failures [00:00:06]. This involves addressing how agents manage long-running tasks and ensure their progress isn’t lost in the event of an interruption [00:00:18].

Challenges of Scaling AI Agents

Current AI agents face several challenges when operating in production environments:

• Human in the Loop Requirements
  • Many applications require human oversight or approval at various stages of agent execution [00:01:29]. This is especially true for high-value or high-risk tasks, such as transferring money or deleting accounts, where a human needs to provide a final decision [00:01:48].
• Long-Running Processes and Failure
  • AI agents often involve multiple steps and can be longrunning [00:02:12]. The longer a process runs, the higher the chance of encountering failures like network or hardware issues [00:02:19]. There is a need to checkpoint an agent’s state to resume operations without losing all prior work [00:02:24].
  • Sophisticated agents performing complex tasks or extensive research across multiple internal or external systems are particularly susceptible to failure [00:05:17]. Mechanisms are needed to tolerate these failures and prevent loss of progress [00:05:49].
• Distributed Environments
  • Increasingly, agents operate in distributed environments rather than just on a single desktop [00:02:40]. This introduces considerations for running agents in a scalable distributed environment [00:02:48].
  • Furthermore, modern agents are becoming more sophisticated, with multi-level configurations involving main orchestrator agents and several sub-agents [00:06:00]. Managing human approval and state saving in such nested scenarios is crucial [00:06:30].

Agent Loop Persistence

A critical challenge for longrunning agents is agent loop persistence [00:06:51]. The agent’s core processing loop, which interacts with users or external channels like Slack, typically needs to run continuously [00:07:23]. This persistent running, even while waiting for human responses, can be inefficient and problematic [00:07:33]. Addressing this, there is a need for a mechanism that allows the agent loop (or multiple loops) to be fully shut down and then restarted later, such as after human approval is received [00:07:51].

Agent Continuations as a Solution

Agent continuations offer a new mechanism developed by Snaplogic to address both human-in-the-loop processing and reliable execution for longrunning agents [00:00:44].

• Inspired by Programming Language Theory
  • Agent continuations are inspired by the programming language concept of “continuations,” which allow capturing the full execution state of a program at any point to pause and then resume it later [00:08:14].
• Capturing and Resuming Agent State
  • The core idea of agent continuations is to pause an agent’s execution—which might involve multiple tool calls, LLM calls, and even sub-agents—and save its complete state [00:09:22]. This snapshot allows the agent to be resumed from that exact point at a later time [00:09:38].
• Leveraging the Messages Array
  • A key insight behind agent continuations is that LLM interactions already maintain a “messages array,” which acts as a log of all previous interactions [00:10:04]. This array, replayed to the LLM for its next inference, already captures much of the agent’s state [00:10:32]. Agent continuations build on this existing bookkeeping [00:10:57].

Implementation and Benefits

The implementation of agent continuations involves:

• Suspension Conditions: Agents can be configured to suspend execution based on various conditions, such as requiring human approval for a specific tool call [00:12:37] or other arbitrary suspension points like time limits or turn counts [00:24:36].
• Continuation Object Creation: When a suspension condition is met, a “continuation object” is created [00:15:19]. This object embeds the standard messages array along with additional metadata to allow for proper resumption [00:15:21].
• Decoupled Execution: A powerful aspect of agent continuations is that once the continuation object is created, the agent loops do not need to remain running [00:16:54]. All necessary information is captured to restart the agent exactly where it left off [00:17:04]. This addresses limitations of current serverless providers for longrunning workflows that typically require continuous execution.
• Recursive Structure: The continuation object format is recursive, capable of handling arbitrary depths of nested sub-agents, ensuring that state can be captured and restored across complex multi-level agent configurations [00:18:24].
• Resumption: When the continuation object is sent back to the agent (e.g., after human approval), the framework reconstructs the agent’s state and resumes execution from the point of suspension [00:16:31].

Benefits for Longrunning Agents

Agent continuations directly benefit longrunning agents and their resilience by enabling:

• Fault Tolerance: By allowing agents to snapshot their state, progress is preserved even if the underlying infrastructure fails [00:05:42].
• Intermittent Execution: Agents do not need to run continuously, which is ideal for workflows requiring human intervention or external asynchronous events [00:07:56].
• Resource Optimization: Shutting down agent loops when suspended can lead to more efficient resource usage.

This approach combines a human approval mechanism with arbitrary nesting of complex agents, offering a novel solution for robust agent deployment [00:25:36].