One offer / $500 / no calls

Buy the AI Workflow Audit._

Send the messy AI workflow. Get a written plan: what to automate first, what to skip, and how to guard it. Delivered in 5 business days.

Buy the $500 audit

Stripe checkout. Written audit. No discovery call, proposal, or retainer.

what you are buying$500

The AI Workflow Audit

A written priority map for an AI workflow you already have, want to fix, or keep postponing because it is too messy.

Priorities

The 3 automations worth doing first.

Skip list

What not to build yet.

Risks

Where the workflow can fail, loop, or leak money.

Build order

The first implementation sequence.

price: $500
delivery: 5 days
format: written
calls: none

For businesses / $500 flat. 5 business days. No calls.

Click once. Send the workflow. Get the plan.

Buy the $500 audit

§ 002 / WHY IT EXISTSopen source SDK + read-only MCP

AgentGuard stops bad runs.

Budget, loop, timeout, rate, retry, trace.

Read GitHub docs ->

Downloads: 6,231+
Version: v1.2.13
GitHub stars: 3
Forks: 2

Observed package downloads, updated hourly. SDK totals use Pepy first, then Pypistats no-mirror overall data when Pepy is unavailable; 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 metric upstreams are unavailable.

local proof in code

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.

Limits raise. Traces stay local.