JD Davenport · jddavenport.com
JD DavenportShowcase

JD DavenportFounder & Managing Director, BYU AI Foundry

Everything on this page is running, cloneable, or clickable. No slideware.

01 · Live system view

The AI operating system I run my work on

Snapshot · 2026-11-11

One orchestrator over 30+ agents in daily production · 100+ built over time, with a durable job queue and a gate on every irreversible action. It runs 24/7, self-hosted on a Mac mini, and republishes its own inventory when it ships.

30+agents in daily production · 100+ built over time
318background jobs
66of those jobs are watchdogs
1,205shipped-work log entries, last 30 days
Live agent graphOpen the live brainEvery agent, skill and background job in the system, counted live. Names and data hidden.
or tap any box
MEMORY + STATE · every layer reads and writes hereINROUTEDISPATCHWORKGUARDSHIPTelegramtext + voiceCockpitweb + iOSSchedulesjobs + watchersOrchestratorOpus · triage, delegate, verifyModel routerone registryJob queuePostgres rowsDurable runnerDBOS workflowsAgent busreceiptedDomain agentsone per areaWorker fleetown worktreeExpert agentsKB-groundedEthos gatesends, spends, deploys wait for a human yesRelay outboxreceipt on every sendWarden + watchdogsdetect · fix · roll backPostgresSupabaseQdrantvectorsKùzugraphMarkdowngit + vault
Amber dots are work moving through the system.
How work moves
  1. A request lands: a Telegram voice note, a cockpit message, or a scheduled job firing on its own.
  2. The orchestrator triages it and picks the agent that owns it. The router picks the model.
  3. It becomes a job row in Postgres. A durable runner claims it, and agents coordinate over the bus.
  4. An agent does the work, in its own git worktree if it touches code, reading memory and state as it goes.
  5. Anything irreversible or outward-facing stops at the gate until I say yes.
  6. The result ships through the relay with a receipt. Watchdogs keep checking after it lands.

The map is drawn by hand from the running system: component names are real, hostnames and credentials are left out. The counts load live from /api/metrics, a server-side filter over the inventory the system republishes on its own. It forwards numbers only, never names or contents. If that request fails you see the snapshot from 2026-11-11.

02 · Projects

Open source, cloneable today

10 featured of 47 public repos. Most are pulled out of the system above and generalized so anyone can run them. The full open-source list is on /work.

mcp-judge

Python · MCP server

A calibrated multi-persona LLM-as-judge that any MCP client can call as a tool to score a document against a rubric.

Why it mattersIt fixes the four ways LLM graders fail (mid-band compression, severity bias, score and verdict drifting apart, run-to-run noise) and ships the eval harness that proves it.

github.com/jddavenportOpen/mcp-judge ↗

claude-deploy-kit

Python

Five working controls for putting Claude agents inside an enterprise: a policy chokepoint, a destructive-command deny-hook, an eval gate, a redacting audit log and pluggable secrets.

Why it mattersThe questions every enterprise deployment asks, answered in code instead of a slide.

github.com/jddavenportOpen/claude-deploy-kit ↗

orchestra-agents

Python

The production orchestration core of the system above: bounded fan-out, a durable message bus, circuit breakers, best-of-N with an adversarial judge, and permission tiers, with the eval harness that tests them. clawd-agent-os is an earlier, smaller extraction of the same core.

Why it mattersParallel agents that cannot silently drop a message, and a judge that has to be convinced before work counts as done.

github.com/jddavenportOpen/orchestra-agents ↗

ethos-gate

Python · Claude Code hooks

PreToolUse and Stop hooks for autonomous Claude sessions: irreversible commands wait for tests, credential stores stay off-limits, outbound GitHub actions get judged.

Why it mattersIt enforces at the tool boundary what a system prompt can only ask for.

github.com/jddavenportOpen/ethos-gate ↗

warden

Python · zero dependencies

A governor for autonomous fixes. An action runs unattended only if it can be undone, and it undoes itself if the health signal does not go green.

Why it mattersAutonomy admitted by reversibility, not confidence. Every test runs offline.

github.com/jddavenportOpen/warden ↗

claude-code-behavior-specs

Specs + runnable repros

Target-behavior specs for the Claude Code CLI, written from production incidents. Each pairs observed and expected behavior with a reproduction you can run and a proposed eval.

Why it mattersWhat breaks when the CLI runs unattended, at concurrency, for months, written so a product team can act on it.

github.com/jddavenportOpen/claude-code-behavior-specs ↗

claude-bug-squash

Python

Point Claude Code at a repo and a bug report. It triages, fixes in an isolated worktree, proves the bug goes red to green, and runs the fix through a layered gate.

Why it mattersSafe by default: it proposes and gates, and never merges until you turn that on.

github.com/jddavenportOpen/claude-bug-squash ↗

deep-research-agent

Python

A research loop that searches 9 sources in parallel until a knowledge-gap check says it is done, argues against its own draft, then fact-checks every claim with a 3-vote refute-or-survive pass.

Why it mattersIt treats a draft as a hypothesis to attack, not an answer to trust.

github.com/jddavenportOpen/deep-research-agent ↗

recruit-copilot

Python · Claude Code skill

Treats a job search as a verification problem. It merges your resumes into one experience bank, tailors a resume per posting, then reads the finished PDF back and runs three independent judges before it will say yes.

Why it mattersIt measures its own output instead of trusting it, and it deliberately has no submit path.

github.com/jddavenportOpen/recruit-copilot ↗

context-kit

Markdown · Claude Code skills

Four Markdown templates and five skills that give Claude Code a durable model of you: who you are, how you decide, how you write, and your hard rules.

Why it mattersStop re-explaining yourself every session. One-command install.

github.com/jddavenportOpen/context-kit ↗
Talk to me

Hiring for AI product or applied AI?

Pick a time or send a note. The fastest way to see how I work is to ask me to walk you through the system above.