Lately, I’ve been thinking more seriously about what AI and automation may mean for my role as a software engineer.

I’m a principal engineer at a large organization, and for more than a decade I’ve led a project that has always felt particularly close to me. I wrote nearly every line of code, aside from a handful of features built by interns over the years.

What started as a single application gradually became an ecosystem: a library, APIs, an orchestration layer, CLIs, scheduled jobs, and more. Today, the organization depends on it in some way.

That complexity did not appear all at once. It accumulated over years through new requirements, integrations, edge cases, architectural decisions, and changes in how the organization operates. Each layer solved a real problem, and the knowledge required to understand the system grew along with it.

Eventually, it became too much for one person to manage while also building every downstream feature people requested. So I rearchitected much of it. I retired obsolete components, created a REST API layer, introduced an MCP layer so other groups could build on top of it, modernized CI/CD and testing, and rebuilt or retired user interfaces. The goal was to make the ecosystem easier to extend and reduce my bus factor.

Since January, as AI models have improved, I’ve also moved toward agentic development. I integrated coding agents into the workflow, created specialized agent skills, and wrote detailed AGENTS.md files for the repositories. The codebase has gradually become more than source code. It now provides the models with an encoded record of the system's architecture, constraints, conventions, and history.

The system can help write feature tickets and implement them reproducibly. It produces code grounded in the existing codebase, writes appropriate tests, respects architectural boundaries, and can mount and exercise the underlying library to validate assumptions.

It can also go beyond the repository. It can inspect the database schema, read from and write to the database within defined permissions, and autonomously retrieve application logs while troubleshooting. It can correlate those logs with the code, architecture, and data to investigate failures and validate its assumptions. It has access to many of the same sources of information I use when diagnosing or implementing something myself.

Yesterday, I demonstrated the workflow to a couple of developers from another team who will be contributing to the codebase for a new project.

The harness pulled a ticket, wrote the code and tests while following my constraints and guardrails, opened the pull request, and deployed everything to the test environment within minutes. Our remaining job was largely to review and test it.

It worked as intended, but it also made me think about where this is heading.

Over the past several months, I’ve effectively been encoding more of myself into the system: technical knowledge, architectural preferences, conventions, constraints, problem-solving patterns, and some of the judgment that comes from working on the codebase for years.

The harness now produces work that is often very close to what I would have written myself.

Getting it to this point still required years of domain knowledge, architectural decisions, modernization work, and careful construction of the context and guardrails that make the agents effective. I’m also still the final review gate, and many decisions require broader technical and organizational judgment.

It seems entirely plausible to me that future systems will increasingly be able to study an unfamiliar codebase, identify its conventions and architectural boundaries, construct their own context, and determine the guardrails needed to work within it safely.

If more and more of my knowledge, judgment, and way of working can be encoded into the systems around me, how much longer are they going to keep me around?

Encoding Myself into the System