Agentic OS — 50+ agent engineering platform

Challenge

AI-assisted coding usually collapses at scale: single-shot agents lose context, skip verification, and produce diffs that look plausible but fail lint, types, tests, or disclosure rules. The speed gain disappears when a human must re-review everything from scratch.

I needed an engineering operating system — not a chat wrapper — that could run long-horizon product work with the same bar as a senior team: plan with acceptance criteria, implement in parallel scopes, audit before trust, verify against real services, and ship with traceability.

Constraints

• Trust over throughput — A fast wrong answer costs more than a slow right one; gates must block progression.
• Host-resource limits — Dozens of concurrent agents cannot destabilize the development machine.
• Disclosure-aware output — Public copy, résumé bullets, and case studies pass automated scans before publish.
• Long sessions — Context must survive across hours and days, not single prompts.

Approach

I designed a five-phase pipeline — plan, implement, audit, test, ship — with specialist agents, explicit handoffs, and release-blocking verification.

Orchestration design

• Planner agent — Scopes work with acceptance criteria, dependency order, and disclosure constraints before code changes.
• Parallel implementers — UI, API, test, and infra specialists work in isolated scopes under strict TypeScript and lint rules.
• Independent auditor — Reviews diffs for type safety, accessibility, architectural fit, and policy before merge.
• Real-services testing — Integration paths hit real APIs where possible; mocks hide the failures that matter in production.
• Synthesizer / ship — Reconciles agent output, prepares commits, and hands off to CI/CD with a clear gate record.

Production guardrails

// Illustrative pattern: gate before progression
export function assertGatePassed(results: readonly GateResult[]): void {
  const failed = results.filter((r) => !r.passed);
  if (failed.length > 0) {
    throw new PipelineBlockedError(failed.map((f) => f.name));
  }
}

Key moves:

• Generator–evaluator pairing — Generating agents are checked by evaluators that see output, not rationale.
• Checkpointed artifacts — Long outputs resume from the last good checkpoint instead of restarting.
• Subagent monitoring — Liveness, memory, and duration caps keep orchestration predictable.
• Reduced-motion fallbacks — Visual pipeline motifs degrade to accessible text when motion is reduced.

AI-assisted throughput

The OS is both the subject and the tool: this portfolio site, résumé pipeline, disclosure scans, and CI gates were built with the same orchestration described on /agentic-os. Throughput is measured honestly — commits and reviews per year, with methodology documented on the About page — not vanity “10×” claims without context.

Outcomes

The system runs daily in production on real engineering work: multi-file features, test gaps, deploy runbooks, and content pipelines that must pass the same gates a hiring manager can inspect in public CI.

Measurable impact

| Area | Result | | --- | --- | | Agent fleet | 50+ specialized roles behind one orchestration contract | | Engineering throughput | ~2,951 GitHub contributions/yr (self-measured; see /about) | | Quality bar | Strict TypeScript, automated lint/test/disclosure/a11y gates | | Public proof | bretmcgee.com shipped under the same pipeline |

Throughput figures are self-measured and method-documented. No client-confidential productivity claims are stated.

What this demonstrates

• Agentic systems that ship — Not demos: production repos, CI, and a live site.
• Guardrails as product — Orchestration without verification is a liability; gates are first-class.
• Craftsmanship exhibit — The process is inspectable: colophon, CI workflows, and case studies match the narrative.

Explore the live narrative and pipeline diagram on /agentic-os, or compare with enterprise platform delivery in the Judo case study.