How to handle tool call errors in the agent (retry, fallback)?

When handling errors from tool calls in an agent, such as in systems with Large Language Models (LLMs), it is important to implement retry and fallback strategies to improve reliability.

Retry:

• For temporary errors (e.g., network failures, timeouts), the agent should retry the tool call after certain intervals.
• Exponential backoff is often used between retries to reduce load.
• The maximum number of retries is limited to avoid infinite loops.

Fallback:

• If retries fail, the agent can switch to an alternative tool or method.
• For example, if the primary API is unavailable, use a local model or cached data.

Pseudo-code example:

max_retries = 3
for attempt in range(max_retries):
    try:
        result = call_tool()
        break
    except TemporaryError:
        wait_time = 2 ** attempt
        sleep(wait_time)
else:
    result = fallback_tool()

This approach ensures agent robustness against failures and enhances user experience.