PLAYER 1CAREER EVIDENCEREADY

MISSION LOG

Proof for practical AI systems work.

Career evidence for practical AI systems, product infrastructure, engineering leadership, modernization, and delivery judgment. The goal is inspectable signal, not a resume pasted into a prettier box.

Level Map

Career chapters without the resume timeline.

Each level is framed by the problem, the systems involved, the hard constraints, and the signal it contributes for AI-space engineering roles.

Saguaro Agent

Mission: Build an open-source workflow, memory, and knowledge system that helps AI coding agents carry context across sessions and harnesses.
Systems: MCP servers, CLI installation, project-local configuration, workflow schemas, durable memory, knowledge retrieval, and multi-harness plugin packaging.
Constraints: The public product has to stay provider-neutral, inspectable, secrets-safe, and useful across Claude Code, OpenAI Codex, and Gemini CLI without leaking private operating context.
Signal: Evidence of AI infrastructure judgment: agent systems need durable context, explicit workflow state, and public contracts humans can inspect.

Public source live
Case study live

11 Mile Co

Mission: Build an AI-orchestrated consulting practice where research, analysis, architecture, and delivery move through a structured 15-agent pipeline.
Systems: Agentic workflows, knowledge processing, consulting operations, technical due diligence, SEO, and delivery automation.
Constraints: The work has to be useful to clients, auditable by humans, and disciplined enough to avoid black-box AI output.
Signal: Evidence of AI-space product judgment: agents as operating systems for real work, not demos.

Case study planned
Pipeline diagram planned

Patina CRM

Mission: Design and build an AI-augmented CRM platform for service businesses that need real operational software, not another generic dashboard.
Systems: Next.js, Turborepo, product infrastructure, relational data, workflow design, and AI-assisted operations.
Constraints: The product has to stay practical for small teams while leaving room for deeper automation and industry-specific workflows.
Signal: Founder-level ownership across product definition, architecture, implementation, and long-term system shape.

Case study planned
Architecture notes planned

CUBEX

Mission: Modernize and support healthcare logistics systems where reliability, traceability, and operational continuity matter.
Systems: Healthcare logistics workflows, product engineering, reliability work, modernization, and AI-assisted delivery practices.
Constraints: The systems serve operational environments where correctness and continuity are more important than novelty.
Signal: Evidence of engineering judgment inside regulated, reliability-sensitive product environments.

Artifact pending
Public context pending approval

Carvana

Mission: Contribute to platform engineering, cloud migration, cost reduction, design-system, and internal tooling efforts at enterprise scale.
Systems: Platform infrastructure, migrations, developer tooling, design systems, cost and reliability work, and internal AI tooling.
Constraints: Large systems require change that is measurable, operationally safe, and coordinated across teams.
Signal: Evidence of scale: migrations, platform ownership, cost pressure, and engineering enablement.

Evidence notes planned
Public source pending approval

Salucro

Mission: Lead engineering management, modernization, and delivery transformation across a growing healthcare payments organization.
Systems: Engineering leadership, legacy modernization, delivery process, team growth, and product engineering execution.
Constraints: The work had to improve delivery without losing the institutional knowledge inside existing systems and teams.
Signal: Evidence of leadership depth: growing teams, improving systems, and turning ambiguity into repeatable execution.

Career chapter planned
Leadership notes planned

Boss Fights

Hard constraints, not gimmicks.

These are recurring problems AB Whittington can discuss in depth: the failure mode, the technical or leadership moves, and the chapters that support the claim.

Legacy modernization without stopping the business

Threat: The system is too important to freeze, but too brittle to ignore.
Move Set: Strangle high-risk areas, protect operational workflows, create migration checkpoints, and make tradeoffs visible.
Evidence: Salucro, Carvana, CUBEX

AI-assisted workflows that remain inspectable

Threat: Automation creates output faster than humans can trust or verify it.
Move Set: Design reviewable agent steps, durable artifacts, clear handoffs, and human-readable reasoning surfaces.
Evidence: Saguaro Agent, 11 Mile Co, Patina CRM

Platform migrations under cost and reliability pressure

Threat: Infrastructure changes can quietly become business risk.
Move Set: Measure cost and reliability, sequence migration work, reduce blast radius, and give teams better tools.
Evidence: Carvana, CUBEX

Engineering team scale and delivery accountability

Threat: Headcount grows faster than clarity, ownership, and execution discipline.
Move Set: Build operating structure, sharpen technical decision paths, and make delivery visible without turning teams into status machines.
Evidence: Salucro, enterprise teams

Product systems for small service businesses

Threat: Small operators get buried by tools built for someone else's workflow.
Move Set: Start from real work, keep interaction costs low, and automate the parts that create leverage instead of ceremony.
Evidence: Patina CRM, service-business product work

Ambiguous operations turned into structured systems

Threat: Important work lives in conversations, fragments, and invisible decisions.
Move Set: Model the workflow, define evidence trails, create decision records, and turn messy execution into reusable systems.
Evidence: Saguaro Agent, 11 Mile Co, product infrastructure work

Power-Ups

Capabilities tied to real operating contexts.

Agentic workflow design
MCP systems
Developer tooling
Product infrastructure
System modernization
Engineering leadership
Applied AI delivery
Reliability thinking
Technical due diligence

Evidence Slots

Reserved for public proof, added deliberately.

This page will grow as approved case studies, diagrams, notes, and source trails are ready. Empty buttons and guessed links do not help anyone assess the work.

Case study planned

Project case studies

Public project pages will be added only after the work and links are approved.

Diagram planned

Architecture notes

Decision records and system diagrams will carry the why and tradeoffs behind the work.

Public source pending approval

Source trails

GitHub links will point to deliberate public work, not inferred repositories.

Artifact pending

Career chapters

Employer and leadership evidence will be written as inspectable chapters, not resume bullets.