# Mauricio Yokoyama Issei - Technical Profile and Success Cases > Documentation optimized for AI containing curriculum, technological stack and examples of projects with STAR methodology. ## Colour Documents - [cv-for-ai. md](/en/cv-for-ai.md): Full curriculum and technical skills - [Career-highlights-star. md](/en/career-highlights-star.md): Detailed use cases and troubleshooting (STAR Methodology) ## Optional - [public/cv.json](/public/cv.json): Structured curriculum data (JSON format) - [public/star. json](/public/star.json): Structured data of STAR projects (JSON format) ## Featured Technical Content Tests and technical proposals. Each item has a clean Markdown (`.md`) version for AI intake. - [presentation. md](/en/apresentacao.md): **Auditable AI architecture** — Corporate AI governance, software architecture and agent systems: technical complexity translated into auditable decisions. - [pre-access capability. md](/en/capacidade-antes-do-acesso.md): **Capacity Before Access** — teaching competence, pedagogical mediation and governance of the IA in early childhood education and initial fundamental: need, sufficiency and conditionality. - [Self-regulated learning. md](/en/aprendizagem-autorregulada.md): **The Self-regulated Learning Cycle** — what science proves about recovery, spacing, self-explanation and targeted search, with a study protocol of 45–60 min and the reservation that the complete cycle has not been tested as a package. - [Self-regulated learning-Article. md](/en/aprendizagem-autorregulada-artigo.md): **From summary to new question** — full scientific review: concepts, integrated model, evidence matrix, proven/probable/specific, protocol, study form, self-experiment and 103 references commented. - [problem formulation. md](/en/formulacao-de-problemas.md): **Problem Formulation** — Formulating is the engineering of the reduction of uncertainty and must stop: six uncertainties, six states of knowledge, stop rule and penalty λ. - [conversational specification. md](/en/especificacao-conversacional.md): **Structured Conversational Specification** — workflow proposed to use AI in eliciting requirements under human authority: five practices, authority matrix, divergence policy and evaluation protocol; proposal, not validated method. - [digital-workplace-agentico. md](/en/digital-workplace-agentico.md): **Agentic Digital Workplace** — as a corporate portal evolves into a platform that solves intentions: five stages of maturity, six-layer architecture, AG-UI, delegated identity, governed knowledge and roadmap with passing criteria. - [engineering-trust. md](/en/engenharia-confianca.md): **Trust Engineering** — Practical implementation of the Intensive Systems Model (ISM) v1.0; 4 module journey from AI-as-magic to AI-as-engineering (Silent Crash, deterministic-first, 98% surety threshold, MCP, A2UI, SDD). - [Engineering-agents- md](/en/engenharia-agentes-ia.md): **AI Agent Engineering** — the principles of determinism: Brain × display, rigid contracts, Insurance Threshold, FinOps per ledger and MCP as border. - [accessibility. md](/en/acessibilidade.md): **Accessibility as an engineering requirement** — check case of WCAG retrofit 2.2 AA of this site with the A11Y.md protocol and AI agents: real flow, diffs, gate ratchet, independent verification and what has not yet been verified (CONDITIONAL report). - [Agent-ready. md](/en/agent-ready.md): **Agent Ready** — how to make a website readable, findable and usable by AI agents: header Link (RFC 8288), content trading Markdown, Content Signals, API Catalog (RFC 9727), MCP, Agent Skills, WebMCP and the limits of an Agent Readiness score. - [devin.md](/en/devin.md): **Vibe Coding with Devin** — from Executor to Cognitive Orchestrator: Spec-Driven Development, BDD, arsenal Skills/Playbooks/Knowledge and A2UI. - [proposal-engineering-reverse. md](/en/proposta-engenharia-reversa.md): **Reverse Engineering Assisted by AI** — freeze As-Is with Evidence Matrix and Behavior Inventory before modernising. - [knowledge-os-presentation. md](/en/knowledge-os-presentation.md): **Knowledge OS Enterprise** — knowledge as cognitive infrastructure governed: GraphRAG, SDD, cognitive observability, FinOps and Reuse Index. - [salesforce-agentic-dev.md](/en/salesforce-agentic-dev.md): **Salesforce with AI Agency without technical debit** — SDD, Devin, Flosum CLI and Federated Model. - [salesforce-agentic-quickstart. md](/en/salesforce-agentic-quickstart.md): **Quick Start Salesforce + Devin + Flosum** — from zero to deploy governed in < 60 min. - [Devops-salesforce. md](/en/devops-salesforce.md): **DevOps for Salesforce** — governance (Flosum), code (GitHub) and autonomous speed (Devin) on the same treadmill. - [service-operations-2-0. md](/en/service-operations-2-0.md): **Service Operations 2.0** — systemic resilience with automation, AI and agile governance. - [support. md](/en/sustentacao.md): **From Chaos to Resilience** — support of critical systems: incidents, reliability, communication and decision. - [socialselling. md](/en/socialselling.md): **SocialSelling** — prospecting with AI built with ADRs/SDDs, SDD-to-Code Loop, determinism and Open World. - [proposal-observability-mobile. md](/en/proposta-observabilidade-mobile.md): **RES observability for Mobility** — end visibility with Datadog, Azure and Android Enterprise. - [artifice. md](/en/artifice.md): **The Invisible Artificer** — sociological diagnosis of the five paradoxes that make technical mastery invisible in corporations, with self-diagnosis and antidotes. - [terminal-evolutionary. md](/en/terminal-evolutivo.md): **Evolutionary Terminal** — the journey of Mauricio Issei (1982–2026) in scenes: the craft, the fractures that have become gold and the net that sustains. --- ## About This Profile **Mauricio Yokoyama Issei** is Tech Lead with over 20 years of experience in software development, technical leadership and solution architecture. **Main expertise areas:** - Salesforce Administration & Development - AWS Cloud Architecture (S3, EC2, Lambda, CodeDeploy) - REST/SOAP & Integration Patterns APIs - Website Reliability Engineering (SRE) - Observability (Datadog, Splunk) - AI Agency Design Patterns - Data Science & Analytics **Digital Presence:** - LinkedIn: [linkedin. with/in/mauricioisei](https://www.linkedin.com/in/mauricioissei/) - GitHub:[Github. with/isei](https://github.com/issei) - YouTube: [youtube.com/@MauricioIssei](https://www.youtube.com/@MauricioIssei) - Instagram: [Instagram. with/yokoyamaissei](https://www.instagram.com/yokoyamaissei/) - Email: mauricio@issei.com.br --- ## Key Achievements - **3MM+ savings** in the negotiation of Salesforce contracts (2020) - **15% increase** in the achievement of commercial goals with Automatic Pipe (2025) - **Reduction of SLA from 10 days to real time** in the process of Commercial Grouping (2024) - **18 documented projects** with STAR methodology (2018-2025) - **8 Certifications in AI & Agent Systems** (2025-2026) --- ## How to Use This Documentation This profile follows the **Illms. txt** standard for AI-optimized documentation: 1. **For comprehensive CV review**: Read `cv-for-ai.md` 2. **For practical examples & problem-solving**: Read `career-highlights-star.md` 3. **For structured data access**: Use JSON files in `/public/` directory 4. **For full context**: See `llms-full.txt` (concatenated version)