# Development note: a personal navigation system **Status:** Emerging application of the Legant Method. First implementation in Chris Blachut’s own system; intended for adaptation to other individuals. This note describes the development direction, not a validated general standard. The Legant Method helps a person understand how they operate, establish direction, and refine it through experience. The personal navigation system being developed around it makes that understanding usable in ongoing decisions with AI agents. Its purpose is to help an individual choose their next consequential move from where they actually stand, act on it, and learn from the result. ## What the system brings together **An Operating Manual:** a concise, revisable account of the person’s recurring patterns and chosen direction. The current implementation uses nine questions: 1. **Core Tension:** What draws me in? 2. **Verb:** How do I operate at my best? 3. **Fuel:** What makes the work rewarding? 4. **Context:** Under what conditions do I work best? 5. **Traps:** Where do I reliably go wrong? 6. **Contribution:** What am I using myself for? 7. **Sagrada:** What am I building over time? 8. **Waypoint:** Where am I heading toward now? 9. **Bearing:** What life do I want this to belong within? The first five answers are hypotheses about the person. The remaining four express their choices. Evidence can challenge either, while the individual retains ownership of their direction. **A Baseline:** the person’s current circumstances, constraints, commitments and open questions. It connects the Operating Manual and desired direction to present reality. **The methodology:** the reasoning framework for understanding the person, evaluating fit, shaping action and interpreting results. **Operating instructions:** how agents retrieve and refresh context, investigate uncertainty, compare alternatives, obtain authorization and maintain records. These components form the working **ontology**: a model of the individual in context, together with the processes that use and revise it. ## Simple answers, traceable depth Each concise answer sits above supporting detail: its sources, reasoning, history, counterevidence and unresolved questions. Agents descend into that detail when a decision warrants it. For example, Chris’s Operating Manual describes a recurring sequence: start with real material, notice where the obvious explanation fails, build a better explanation, make it usable, and test it in real work. Supporting records allow that description to be examined and challenged. It becomes useful when comparing actual opportunities and working conditions. ## From understanding to action and learning For a consequential decision, the system brings the person-model, direction and refreshed Baseline together. It compares feasible moves and helps the individual choose a bounded action. The chosen move records responsibility, when to examine the result, and what would justify reconsideration. Outcomes return to the relevant records. Facts, interpretations and chosen direction have different update rules. Audits also examine whether the system retrieved the right evidence and incorporated what happened. ## Where development is heading The next step is to test complete decision-and-learning cycles with other individuals and identify what transfers. Reliable follow-through, decision benefit and maintenance cost remain open questions. The existing [Client Agent Navigation Standard](https://legantlabs.com/methodology/library/protocols/navigation/client-agent-navigation-standard.html) describes the shared guidance. This development work explores how to put it into sustained practice.