Agent Audit

The Economic Layer for AI Agents

[Agent Audit @AgentAudit_]

Introducing Agent Audit — The Economic Layer for AI Agents.

AI agents don’t just run workflows, they operate hidden economies of calls, retries, loops, and decisions that quietly accumulate cost.

Agent Audit acts as the system of record for this economy, showing which agents are pulling their weight, where spend is wasted, and where opportunities for savings may exist, all in real dollar terms instead of tokens.

With one command, you can run a local audit and get your first signal in under sixty seconds.

Agent Audit analyzes your OpenClaw or Hermes data directly on your machine, requires no account, and keeps everything local unless you choose to push results.

Agent Audit goes beyond simple usage metrics by turning runtime data into economic visibility. It identifies confirmed waste like retries and loops, along with savings opportunities such as context bloat, idle workflows, and inefficient model usage.

Every audit provides a clear view of spend, waste, opportunity impact, waste rates, and trends over time, helping teams understand where costs are coming from and how their agent systems are performing economically.

Run everything locally or push results to a hosted workspace for shared visibility and historical tracking.

Today, Agent Audit brings transparency to agent economics.

Tomorrow, it evolves toward economic governance with budget guardrails and policy controls.

Because the future of AI agents isn’t just intelligence, it’s economics.

[Agent Audit @AgentAudit_]

Agents can generate value. But they can also silently burn money.

Agent Audit makes sure you know the difference.

The economic layer for AI agents.

[Agent Audit @AgentAudit_]

We’re moving toward autonomous agents.

But autonomy without economics is chaos.

Agent Audit brings economic structure to agent systems.

The missing layer.

[Agent Audit @AgentAudit_]

If you’re building with AI agents, you need a system that tells you: what’s broken economically.

Not more monitoring. Not more dashboards.

Agent Audit is that system.

[Agent Audit @AgentAudit_]

Not all spend is equal.

Agent Audit separates:
• confirmed waste
• potential savings

So you know what to fix now and what to test next.

[Agent Audit @AgentAudit_]

Run audit → fix one thing → re-run

Agent Audit shows the delta in dollars.

Not guesses. Not vibes. Actual economic impact.

Iteration, but measurable.

[Agent Audit @AgentAudit_]

No dashboards. No accounts. No data leaks.

Agent Audit runs locally.

Your agent data stays with you until you decide otherwise.

Economic visibility, without compromise.

[Agent Audit @AgentAudit_]

Most agent systems don’t fail loudly.

They fail quietly:
- in retries
- in loops
- in unnecessary context

Agent Audit surfaces it all in dollars. That’s the difference.

[Agent Audit @AgentAudit_]

AI workflows aren’t just logic.

They’re economic systems:
inputs → calls → retries → outcomes → cost

Agent Audit turns that runtime into decisions.

The economic layer for AI agents.

[Agent Audit @AgentAudit_]

Every tool shows usage but Agent Audit shows waste.

Not tokens. Not logs. Actual dollar impact.

Because optimization starts with economics.

[Agent Audit @AgentAudit_]

One command. 60 seconds.

Agent Audit audits your agent workflows locally and shows exactly:
• where money is wasted
• what to fix first
• how much you’ll save

[Agent Audit @AgentAudit_]

You’re not running agents.

You’re running a hidden economy of calls, retries, loops, and wasted spend.

Agent Audit makes it visible.

The economic layer for AI agents.

[Agent Audit @AgentAudit_]

AI agents don’t need more logs. They need an economy.

Agent Audit is the economic layer for AI agents.

See where money is actually going.

Find what’s wasting it. Fix it in minutes.