I'm Patrick Hughes. I build small AI tools for developers shipping agents into the real world. AgentGuard is the flagship: budget caps, loop detection, timeouts, and kill switches before your agent burns money at 2 AM.
$ pip install agentguard47Stats update hourly from PyPI and GitHub. Downloads use a conservative floor when upstream totals are unavailable.
The bet: a useful software company can be one human, a stack of tools, and agents doing the boring work.
No spend cap. No loop detection. No timeout. No kill switch. That is fine for a demo. It is reckless for anything that runs unattended.
AgentGuard exists because I needed a guardrail layer before letting agents run real workflows in my own one-person company.
Stats update hourly from PyPI and GitHub. Downloads use a conservative floor when upstream totals are unavailable.
from agentguard47 import Guard
with Guard(budget_usd=2.00, max_tool_calls=50, timeout_s=300):
agent.run("summarize this inbox")Stop a run before an experiment turns into a surprise bill.
Kill runaway agents before retries turn into an operator problem.
Put a hard wall-clock ceiling around unattended agent work.
Keep background jobs from hammering tools while nobody is watching.
Stop bad runs clearly instead of pretending everything is fine.
AgentGuard is the main product. The rest of the shelf supports the same operating model: build, test, ship, and control agents without needing a team around every workflow.
$ pip install agentguard47Runtime guardrails for AI agents before they spend money unattended.
$ pip install agentguard47Describe an agent. Get risks, architecture, guardrails, and next steps.
Watch multi-agent coordination, handoffs, and tool calls in real time.
Paste a URL. Get a summary, key points, and sentiment in seconds.
Four AI personas that react to your live podcast in real time.
Persistent key-value memory for AI agents, paid per call with x402.
This is the operating model behind the product. I keep the judgment. Agents handle the repetitive work that should not require a meeting.
Daily brief, decisions, routing
Standup, context, keeping the system on
Runs the Autotrader. +34% YTD
Blog, SEO, LinkedIn, newsletter
Web scraping, summaries, competitors
CI/CD, deploys, build pipelines
Cron jobs, queues, always-on services
Portfolio numbers, trend detection
Agent fleet / 22 running
Each node = one active agent. Pulse rate reflects heartbeat interval.
Recent activity
Before you ship an AI agent for a client, prove budget caps, loop detection, alert proof, remote kill, and retained incident history.
The demo worked. Then the same CrewAI tool call retried until the run became an operator problem.
A trace tells you what happened. A kill switch changes what happens next.
Cloudflare shipped agent flows that create accounts, buy domains via Stripe, and deploy infrastructure end-to-end. Good news for builders. Sharper case for runtime budget enforcement than any hypothetical we have used.
What shipped, what broke, and what I learned while building a one-person AI tools company. One email a week. Unsubscribe in one click.
How one human plus twenty-two AI agents runs a seven-pillar portfolio with no employees.