One principal, a fleet of specialists.
AI-leveraged engineering at LaFayette Labs isn't "pasting LLM output into the codebase." It's an orchestrator-delegation pattern: a senior engineer decomposes the work, delegates focused tasks to specialist sub-agents that run in parallel, and integrates and verifies the results before any artifact ships.
This site and the Panakoes hardware BOM were both built this way. The pattern is described below; the proof is in section 02.
Orchestrator, plus a fleet of specialists.
The orchestrator is a senior engineer with full context on the engagement. The sub-agents are scoped, briefed instances of frontier models running specific subtasks. Phil stays in the loop on every decision; the sub-agents carry the load on parallel exploration.
- P1 Orchestrator decomposes the work
The principal reads the brief, frames the system, and breaks the work into tasks that can be done in parallel without colliding. Tasks come with concrete acceptance criteria, a working directory, and the constraints that matter (style guides, voice rules, security posture).
- P2 Sub-agents run in parallel, in git worktrees
Each sub-agent works in its own isolated checkout (a git worktree branched from origin/main) so edits never collide. Marketing review, hardware research, infrastructure scaffolding, and code generation can all run simultaneously on a single workstation. Ten parallel agents on ten worktrees is a normal afternoon.
- P3 Each sub-agent files a run report
When a sub-agent completes a task, it writes a structured report at
.agent-runs/<UTC-timestamp>-<slug>.md: what was done, what files changed, deviations from the brief, build status, links to the PR. The orchestrator reads it before integrating; the report stays in the repo as audit history. - P4 Orchestrator verifies, integrates, ships
The orchestrator reads the diff, confirms it matches the brief, checks the conventional-commit style, walks the preview deploy, and decides whether the artifact ships or goes back for another iteration. Nothing reaches a customer-visible artifact without principal review.
A short list of agents that actually run.
Sub-agents are scoped to a specific role. The brief and the role are baked together: a research agent gets a research brief, a marketing reviewer gets a marketing brief. None of them runs as "general assistant."
- S1 Research wave agents
For the Panakoes hardware buffer-memory question, multi-wave research agents evaluated MRAM vs FRAM vs PSRAM vs internal-SRAM-ping-pong tradeoffs across capacity, power-loss tolerance, and JLC stock. The wave-5 synthesis recommended using the chosen MCU's internal SRAM plus a $3 FRAM for cursor metadata, saving $5 to $25 per BOM vs the original PSRAM + supercap plan.
- S2 Marketing reviewers
Each migrated page on this very site goes through a marketing-review sub-agent before it ships. The reviewer reads the live URL, checks for fabricated content, audits the conversion path, and returns a numbered list of concrete fixes. The orchestrator applies the high-leverage suggestions and pushes.
- S3 SEO / canonical fix agents
The duplicate-URL fix on this site (canonical tags pointing at
lafayettelabs.complus a 301 redirect from the.pages.devhostname) was completed end-to-end by a sub-agent in a worktree, with a full run report and a self-contained pull request. The orchestrator stayed focused on a different page migration in parallel. - S4 Build-bot reviewers
Pre-merge checks include build-pass verification, automated quality gates, and a manual walk of the Cloudflare preview URL. Reviewer agents flag broken links and regressions before a human ever sees the diff. The orchestrator handles only what the bots can't: the judgment call.
Artifact-first, always.
Our deliverables are artifacts that survive the engagement: code, schematics, architecture documents, decision records, run reports. Status meetings happen when they earn their slot.
- D1 Conventional commits and changelogs
Every change ships with a Conventional Commits header (
feat(scope): subject) and a CHANGELOG entry under[Unreleased]. ISO 8601 dates on releases. The git history is part of the deliverable; a future engineer can read the log and understand what shipped, when, and why. - D2 Architecture decision records
Non-trivial architecture decisions get an ADR: context, options considered, what was chosen, what was traded. Lives in
docs/adr/alongside the code. When you read an ADR a year later you understand why the system looks the way it does. - D3 Pull-request review, not direct push
Every change ships via PR, even when Phil is the only reviewer. The PR description is the spec; the diff is the work; the merged commit is the audit trail. Pre-merge checks include a build pass, automated quality gates, and a manual walk of the preview deploy.
- D4 Run reports as audit history
Sub-agent run reports stay in the repo at
.agent-runs/<timestamp>-<slug>.md. A reader can reconstruct the orchestration after the fact: what was delegated, what was returned, what made the cut. The audit trail outlives any one engagement.
If this is the discipline you want on a real engagement: phil@lafayettelabs.com.
See it in action.
phil@lafayettelabs.comThe inquiry form takes a few minutes and lets us read your context before the first reply. First reply within one business day.
Discrete deliverables on a 2 to 6 week scope. Fixed-fee or T&M with a not-to-exceed cap.
Email phil@lafayettelabs.com with the role brief. Phil's open to senior-engineering and principal-level conversations alongside studio engagements.