ShowcaseJD DavenportFounder & Managing Director, BYU AI Foundry
Everything on this page is running, cloneable, or clickable. No slideware.
The AI operating system I run my work on
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.
- A request lands: a Telegram voice note, a cockpit message, or a scheduled job firing on its own.
- The orchestrator triages it and picks the agent that owns it. The router picks the model.
- It becomes a job row in Postgres. A durable runner claims it, and agents coordinate over the bus.
- An agent does the work, in its own git worktree if it touches code, reading memory and state as it goes.
- Anything irreversible or outward-facing stops at the gate until I say yes.
- 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.
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 serverA 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
PythonFive 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
PythonThe 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 hooksPreToolUse 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 dependenciesA 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 reprosTarget-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
PythonPoint 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
PythonA 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 skillTreats 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 skillsFour 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 ↗Shipped, public, clickable
Live apps, the production teardown, and the writing behind them.
- Live
The live brain
Every agent, skill and background job in the running system as a live graph. Names and data hidden; the structure is real.
jddavenport.com/brain ↗ - Case study
Running agents in production: the full teardown
Each capability, its risk, the control that bounds it, and the incident that taught it.
jddavenport.com/teardown ↗Source: agent-safety-case-study ↗ - Open source
VC Job Board
Startup roles across Utah and California VC portfolio companies, with resume matching. Built for the AI Foundry; the code is public.
github.com/jddavenportOpen/vc-job-board ↗ - Live app
BookJD
A self-hosted booking app, my own Calendly: Next.js, Supabase and Google Calendar availability.
book.jddavenport.com ↗Source: bookjd ↗ - Live app
Clawdacademy
An interactive trainer for hands-on AI fluency.
clawdacademy.app ↗Source: ai-fluency-trainer ↗ - Docs
Agent Tree Knowledge Hub
Guides on building agent systems, written from running this one.
docs.agenttree.army ↗ - Essay
I ran my life on a fleet of AI agents for 6 months
What worked, what broke, and what I would build again.
docs.agenttree.army/articles/personal-ai-os ↗ - Newsletter
Raising Clawd
AI builds, frameworks and intel, from someone running agents daily.
jddavenport.com/newsletter ↗
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.