Miguel Ángel Ballesteros bio photo

Miguel Ángel Ballesteros

CTO and co-founder of GoKoan. I build AI products such as Koanly, learning systems and agentic software workflows that turn complex knowledge into usable tools.

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Selected work

These are representative examples of the kind of work I do. Some details are intentionally high-level when they involve private systems, customers or internal codebases.

GoKoan: learning science, planning and AI tutors

Context. GoKoan helps students prepare for competitive exams by combining planning, learning science and digital study materials.

My role. Co-founder and CTO: product architecture, technology strategy, engineering execution and AI capabilities.

What mattered.

  • Turning available study time into personalized daily planning.
  • Applying AI to educational content: questions, flashcards, true/false activities, fill-in-the-blank exercises, summaries and tutoring.
  • Keeping the product useful for real learners, not just technically impressive.
  • Moving from early model experimentation to production workflows.

Why it represents me. This is the clearest example of my long-term pattern: use technology to make complex learning more practical, adaptive and humane.

Related reading:

Koanly: AI content production for education

Context. Koanly is a GoKoan product for AI-powered educational content production.

My role. Technical and product leadership around the generative AI workflows behind the product.

What mattered.

  • Converting content workflows into AI-assisted pipelines.
  • Balancing speed, quality, review and cost.
  • Designing systems where AI helps subject-matter work without hiding responsibility.
  • Making content generation repeatable instead of artisanal.

Why it represents me. I am interested in AI as an operating model, not only as a feature. Koanly is the kind of product where process design, domain knowledge and model behavior matter together.

Agentic engineering: mining PRs into operational guidance

Context. Coding agents can produce a lot of code, but they fail when they do not understand the local taste, conventions and recurring review patterns of a repo.

My role. Designed and ran an experiment mining hundreds of PRs and review comments to extract recurring engineering feedback, then converted that into backend guidance, checklists and agent handoff rules.

What mattered.

  • Treating PR comments as a corpus of tacit engineering knowledge.
  • Separating generic rules from repo-specific conventions.
  • Building a review checklist for both humans and coding agents.
  • Closing the loop so future PR review comments can become new guidance.

Why it represents me. This is close to my current thesis: strong AI-assisted engineering is not “more prompting”; it is building a system where agents inherit the operating culture of the team.

Related reading:

Technical feedback loops with Codex

Context. A serious technical problem is not only a bug; it is a chance to improve how the team detects, reproduces, tests and prevents similar issues.

My role. Built a closed loop for detection, triage, tests, fixes and documentation with Codex as an engineering operator.

What mattered.

  • Making reproduction explicit.
  • Adding tests that captured the failure mode.
  • Turning a one-off fix into a reusable loop.
  • Using the agent for disciplined execution, not blind code generation.

Why it represents me. I care about AI because it can increase engineering leverage, but only when paired with evidence, tests and operational discipline.

Related reading:

Enterprise architecture before the AI wave

Before GoKoan, I spent many years building and leading complex software projects in enterprise environments: Ford, Tissat, Indra and Akamon.

Selected themes:

  • Modernizing healthcare systems without breaking production service.
  • Building search and dynamic forms for clinical workflows.
  • Leading education administration systems with large multi-site teams.
  • Creating reusable Java frameworks, web architecture and code-generation tools.
  • Working as developer, architect, project director, product director and technical lead.

Why it still matters:

  • AI does not remove the need for architecture, reliability, observability and clear ownership.
  • My current AI work sits on top of a long background in systems that had to survive real users, budgets, deadlines and legacy constraints.

Related reading:

Personal AI/product lab

I also build small products and experiments to explore how AI changes learning, writing, reading and personal systems.

Examples:

Why it matters:

  • I learn best by building.
  • Small apps are a good way to test product instincts, interfaces, AI workflows and personal operating systems before they become company-scale patterns.