Charles HudsonFounder and Managing Partner, Precursor Ventures

Who is Accountable? The Truth About AI Agents vs. Human Processes

11 Aug 2026 · 1 min

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In this brief dialogue, Charles Hudson and Speaker A discuss the accountability challenges posed by AI agents that orchestrate processes through code, API calls, and language models. Speaker A highlights that agents can generate a detailed, traceable paper trail, potentially surpassing human record‑keeping, yet admits that agents can become confused when asked to explain specific actions. Charles raises the core question of responsibility: when an API call to a model yields a result, who is answerable? He contrasts this with human processes where a person can be approached for clarification. The conversation underscores the unpredictable interplay of code, APIs, and models within agents, making attribution of responsibility and clear accountability difficult.

The big pros that you get from agents running these processes is potentially having the most accountable paper trail that you've ever had in comparison to people. I've had multiple instances where I've asked how you as the agent came to a specific action, and not that it lies, but it gets confused.
If an API call to a model returned a result, who's responsible? At least with the, at least with the human processes, there's like a person you could go talk to. Here, in many cases, the agent is some combination of code, API calls, and a model, which all work together in sometimes unpredictable ways.