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Achievement: Pair ExtraordinaireAchievement: YOLOAchievement: Pull Shark

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erickcxc/README.md

The agent stack: how I run AI agent swarms in production. Me for direction, review, and the final 10 percent. Six agents in parallel: code, QA, Google Ads, creative, email, voice. Models plus harness: frontier, small, local, with roles, SOPs, and plans. A policy gate that checks every action and masks secrets. The real world: ad account, CRM, phone line, GitHub, email inbox, storefront.

I run AI agent swarms in production. Clear roles, written procedures, QA gates in code, and hard limits on what runs without me. They do in hours what used to take a team months, inside the ad account, the CRM, and the phone line.

The stack in one paragraph

Me on top: direction, review, and everything that spends money or can't be undone. Below me, a swarm: many agents in parallel, one job each. The task picks the models: frontier for reasoning and code, small and fast for proven actions, local when data shouldn't leave the machine. The harness is everything you'd hand a new hire, written down. A separate policy gate checks every action, masks secrets, and keeps untrusted content from taking control. A model scores the work; a threshold in code makes the call.

Selected work

  • Voice agents in production: self-hosted voice platform operated end to end from the terminal and by agents over MCP (dograh-ctl: 33 commands, 25 MCP tools, on PyPI). Real calls, transcripts, latency budgets.
  • Multi-tenant voice and automation platform: per-tenant Postgres row-level security, prepaid wallets with billing that stays consistent under concurrent webhooks, and a two-stage call-classification pipeline (deterministic rules plus an LLM tie-breaker) at 14/14 on its eval set.
  • Autonomous pipelines: ingest, score, act on a schedule (market monitoring, lead generation, ad creative), each with run-cost budgets as first-class constraints.

How I think about agents

  • Agents own outcomes, not steps. If it can't own an outcome, it's not ready.
  • LLM-as-judge everywhere, but the threshold lives in code: testable, versioned, immune to model mood.
  • Every mistake becomes a rule the next session follows.

Contact

dev@erickpaniagua.com · erickpaniagua.com · Detroit, remote (US)

Pinned Loading

  1. qc-gate qc-gate Public

    A model-judges-model QC gate: a multimodal model scores generated images and audio against a rubric, and code (not vibes) decides PASS/FAIL.

    Python

  2. fable5-gemini-pro-3.1 fable5-gemini-pro-3.1 Public

    Cross-lab QC for what the build model can't perceive: Fable 5 reads a still, Gemini 3.1 Pro watches the video and hears the audio. Capture in, JSON verdict out, loop until PASS.

    Shell 2

  3. dograh-ctl dograh-ctl Public

    CLI to run a self-hosted Dograh voice-agent platform from the terminal: agents, calls, transcripts, numbers, models, campaigns, tools, keys, and an MCP server

    Python 1 1