# Are most agent errors caused by the agent's own logic?

No, most of what shows up as an error span isn't the agent reasoning badly. [Datadog's State of AI Engineering 2026 report](https://www.datadoghq.com/state-of-ai-engineering/) found that in February 2026, 5 percent of all LLM call spans reported an error, and 60 percent of those errors were exceeded rate limits, nothing in the agent's own logic.

Framework and infrastructure code makes up a growing share of what runs between a prompt and a response, since agent framework adoption nearly doubled year over year in the same report. A rate limit, a provider timeout, or a dependency bump can produce a failure that looks identical to a reasoning error on a dashboard that only tracks pass or fail. Treating every error span as a model problem sends the investigation at the wrong layer. Check what actually threw before assuming the agent got the task wrong; a good share of the time, it never got the chance to.

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Sources:
- Datadog, State of AI Engineering 2026: https://www.datadoghq.com/state-of-ai-engineering/ (fetched 2026-08-25)

Source: https://tessary.ai/answers/failure-modes/are-most-agent-errors-caused-by-the-agents-own-logic
More on Failure modes: https://tessary.ai/answers/failure-modes
From Tessary, agent reliability for AI agents in production: https://tessary.ai