In my first article from this NLP series, I wrote about what neuro linguistic programming is, how and where it can be helpful and what all scientifically proven concepts it is based on. In this article, I am going to delve into a specific topic ‘A Learning State’.  While the concept of Leaning State is not only confined to NLP, a lot of neurological research has gone into backing the benefits of attaining the Learning State. Most of these states come very naturally to us, just that we are not aware of those. So what is Learning State?

This article proposes five practitioner frameworks for specifying agentic systems: the Agentic Requirements Stack, the Autonomy Boundary Canvas, Tool Contract Specification, the Escalation and Handoff Matrix, and the Agentic Traceability Ledger. Each is illustrated through a running hypothetical case. None requires the analyst to write code; all require the analyst to think differently about what a requirement is.

Have you ever wondered, what is a learning state? Or what connection does it have with peripheral vision? Have you ever tried to model excellence through role models? Can you remember when was the last time you successfully came of a stuck state through bringing your mind into an alert state and take an action in a positive direction? Have you ever worked on language by utilising it to your fullest vocabulary and improve it by consciously using the right words, nouns and phrases? No, I am not talking computer engineering, process modelling, or about training AI models, or about human bots or AI agents. So what am I talking about? Ready, Steady, Go?

User stories can describe what an AI agent should accomplish—but they rarely define how much authority it should have, when it must stop, or who is accountable when it gets a decision wrong. This article introduces the AI Decision Contract, a practical Business Analysis artifact for defining an agent’s permissions, limits, evidence requirements, escalation rules, and human oversight.

Good software design does more than support the happy path—it helps prevent users from making mistakes and makes recovery easier when they do. This article shows how clear messages, confirmations, undo options, saved progress, and thoughtful workflow design can make systems more usable, forgiving, and frustration-free.