Storing Successful AI Workflows to Avoid Repetition
The article discusses the problem of AI models forgetting successful workflows and repeating the same conversations. It introduces the Agent Experience Protocol (AEP) as a solution to store the essence of successful tasks, including intent, constraints, preferences, workflow, and more.
Why it matters
AEP addresses a key challenge in working with AI - the inability to retain and reuse successful workflows, leading to repeated conversations and inconsistent results.
Key Points
- 1AI models do not remember what actually worked in previous conversations
- 2AEP stores the success structure of a task, not just the prompts or chat logs
- 3AEP captures intent, constraints, preferences, workflow, failure traps, and success checks
- 4AEP allows the AI agent to start aligned and avoid past mistakes
- 5AEP is stored in the project repository, making it version-controlled and team-visible
Details
The article explains that the real problem with working with AI is not the model or the prompts, but the fact that AI does not remember what actually worked in previous conversations. Every session is a reset, and the valuable patterns and workflows discovered during a successful task are lost. The Agent Experience Protocol (AEP) is introduced as a solution to this problem. AEP captures the essence of a successful task, including the intent, constraints, preferences, workflow, failure traps, and success checks, instead of just storing the prompts or chat logs. This allows the AI agent to start aligned with the desired workflow and avoid past mistakes, getting to the desired result faster. AEP is designed to be stored directly in the project repository, making it version-controlled, team-visible, and portable.
No comments yet
Be the first to comment