One product path: AgentGuard Pro. Patrick Hughes proves the build. BMD Pat LLC handles the receipt. Start free with the SDK; pay for Pro when hosted history, read keys, alerts, and dashboard proof matter.
$ pip install agentguard47Observed package downloads, updated hourly. SDK lifetime counts use Pepy; SDK recent-month counts use Pypistats; MCP recent-month counts use npm. Downloads are a usage signal, not an adoption proof. Tracked copy is shown when a metric upstream is unavailable.
The bet: a useful software company can be one human, a stack of tools, and agents doing the boring work. The homepage still shows the full tool shelf, but the purchase path is AgentGuard Pro.
AgentGuard gets the homepage because it is the only one of the three with a public install path, a self-serve trial, fixed pricing, and usage signals.
Projection math: 6 AgentGuard Pro subscribers at $39/mo = $234/mo. That clears the $200/mo baseline.
Trust signal
Builder proof, not the product.
Use the founder story to judge taste, urgency, and product judgment. Do not buy a personal brand.
Conversion path is indirect: good for trust, weak for immediate self-serve revenue.
Receipt and legal layer
Seller of record, not the pitch.
The company belongs on receipts, terms, refunds, and support. It should not compete with the product.
Conversion path is administrative: needed after purchase, weak before purchase.
$39/mo self-serve trial
The only thing to buy.
$39/mo after the 14-day trial for hosted history, read keys, alerts, and a dashboard around the local runtime proof.
6 Pro subscribers at $39/mo = $234/mo, enough to clear the $200/mo baseline.
Most agent tools tell you what happened after the run. AgentGuard is the guardrail layer I wanted before letting agents work unattended.
Start with the Python SDK. Add MCP only when you want your coding agent to read traces and budget health. Use the hosted dashboard when local files are not enough.
Observed package downloads, updated hourly. SDK lifetime counts use Pepy; SDK recent-month counts use Pypistats; MCP recent-month counts use npm. Downloads are a usage signal, not an adoption proof. Tracked copy is shown when a metric upstream is unavailable.
from agentguard import BudgetGuard, JsonlFileSink, LoopGuard, Tracer
budget = BudgetGuard(max_cost_usd=5.00, max_calls=50)
loop = LoopGuard(max_repeats=3)
tracer = Tracer(
sink=JsonlFileSink(".agentguard/traces.jsonl"),
service="support-agent",
guards=[loop],
)
with tracer.trace("agent.run") as span:
budget.consume(calls=1, cost_usd=0.02)
loop.check("search", {"query": "refund policy"})
span.event("tool.call", data={"tool": "search"})
# Call your agent or tool here.Open source. Local-first. Raises inside the running process when a budget, loop, rate, retry, or timeout limit is crossed.
Read-only access for Claude Code, Cursor, Codex, and other MCP clients to traces, alerts, usage, costs, and budget health.
Use the hosted app for read keys, retained history, alerts, and shared visibility. Keep the first integration local if that is all you need.
Create read key ->Stop a run before an experiment turns into a surprise bill.
Kill repeated tool calls before retries become an operator problem.
Put a hard wall-clock ceiling around unattended agent work.
Keep background jobs from hammering tools while nobody is watching.
Stop retry storms instead of letting flaky tools spin forever.
Write JSONL traces and incident reports without sending data anywhere.
{
"mcpServers": {
"agentguard": {
"command": "npx",
"args": ["-y", "@agentguard47/mcp-server"],
"env": {
"AGENTGUARD_API_KEY": "ag_your_read_key_here"
}
}
}
}Use AgentGuard when you need runtime controls. Current shelf: 12 live from 12 public tools. Open what helps. Skip what does not.
$ pip install agentguard47Runtime guardrails for Python agents: budget, loop, timeout, and rate limits with MCP visibility for Claude Code, Cursor, and Codex.
$ pip install agentguard47Estimate GGUF VRAM fit, --n-gpu-layers 53 planning, and CPU offload risk from model, quant, GPU, and context presets.
Rank 18 local models across 6 workloads and 3 priorities for GPU fit.
Compare 9 GGUF quant levels by size, quality, speed, and 24GB GPU fit.
Remove the 5 free runs per tool per day limit. Saved GPU rigs, Hugging Face model import, fit alerts, and benchmark history for local LLM builders.
Describe an AI agent workflow. Get risk score, top risks, architecture, first guardrails, and next steps.
Agent Architect scopes AI agent builds into DIY / Startup / Growth / Enterprise tiers with top 3 risks, cost, timeline, and architecture output.
Watch two AI agents coordinate through 5 pipeline steps, 1 tool call, and 1 handoff.
Paste a URL. Get summary, topics, sentiment, and entities plus saved history.
4 AI personas reacting to your live podcast in real time.
Pay-per-call memory for agents. USDC on Base, no accounts.
Look up public Dota 2 profiles, recent matches, ranks, hero performance, and 10-player breakdowns via OpenDota.
This is not a platform committee. It is Patrick building useful AI tools, using AgentGuard on the agent work that should not run loose.
AgentGuard is the product I reach for before an agent can run past budget, loop, timeout, and rate limits.
22 small agents handle repeatable work: checks, summaries, drafts, and repo chores.
Every useful lesson turns into docs, posts, examples, or another small tool.
Patrick keeps the judgment. The software handles the boring loops around it.
Short posts on agents, local models, cost control, and the parts of this stack that actually held up.
Prompt instructions are a request. API contracts are a wall. Why I moved my blog QA gate out of the prompt and into the server.
My blog repair loop chewed on a stale draft for 23 mornings and reported "blocked" every time. The fix was not a smarter retry. It was a TTL and a heal path.
Scheduled tasks exit 0 even when the work never happened. Here is the outcome layer I built on top of my agent fleet, and why it shipped before any new dashboard.
Google found the first AI-built zero-day in a planned mass-exploitation event. A builder's read on what changes for small operators running agents.
What shipped, what broke, and what I learned while building a one-person AI tools company. Monday through Friday when there is something useful to send. Unsubscribe in one click.
How one human plus twenty-two AI agents runs a seven-pillar portfolio with no employees.