How I built an observability layer for my OpenClaw AI agents

Wes Sander — Thu, 12 Feb 2026 21:09:52 +0000

I have been building with AI agents for a while now but I ran into a massive problem. Most of the stuff out there right now is just a chat thread in a browser window. It is basically a toy. If you are running a swarm of autonomous agents, you have no clue what they are actually doing or why they made a specific decision. They are a total black box.

I realized that if we are ever going to trust agents in production, we need real infrastructure, not just chat threads. We need a control tower.

That is why I built DashClaw. It is an open-source observability and governance platform designed to follow your agents wherever they run. I just released it today and wanted to walk through how the architecture actually works.

DashClaw Main Dashboard

The Problem: The Agent Black Box

When an agent runs locally, it is usually just dumping logs to a console or a text file. If it fails, you have to dig through thousands of lines of text to find out what went wrong.

DashClaw solves this by providing three specific layers:

A Next.js Dashboard: The central command center for multi-agent monitoring.
Python CLI Tools: 20 plus specialized local tools for memory health, goals, and context.
A Node.js SDK: A zero-dependency way to instrument any agent in minutes.

The Agent Workspace

How it works: From Local CLI to Cloud Dashboard

The coolest part about this setup is that it is local-first. My agents use a suite of Python tools to manage their own memory and goals.

For example, when my agent learns something new, it uses a tool called learner.py to log that decision into a local SQLite database. But I also want to see that on my dashboard. So I added a --push flag to all the tools.

When the agent runs: python learner.py log "Used AES-256 for encryption" --push

It stores the data locally so the agent has it for the next session, but it also POSTs it to the DashClaw API. Now I can see the "Long-term Learning" of my entire agent fleet in one UI.

Instrumenting the Agent with the SDK

I wanted to make it incredibly easy to get up and running. I just published the SDK to npm, so you can just run:

npm install dashclaw

Then, you just wrap your agent's risky actions. I built something called Behavior Guard that lets you set policies on the dashboard (like blocking all deploys if the risk score is too high) without changing your agent's code.

import { DashClaw } from 'dashclaw';

const claw = new DashClaw({
  apiKey: process.env.DASHCLAW_API_KEY,
  agentId: 'my-swarm-agent',
});

// The agent checks the "control tower" before acting
const { decision } = await claw.guard({
  action_type: 'deploy',
  risk_score: 85,
  declared_goal: 'Pushing to production'
});

if (decision === 'block') {
  console.log('Control tower blocked the action!');
  return;
}

Deep Diving into Actions

When an action is recorded, you get a full post-mortem page. I used SVG to build a graph that shows the parent chain of the action, any assumptions the agent made, and any open loops (blockers) that were created. This makes debugging autonomous failures so much faster.

Action Post-Mortem and SVG Trace

Populating the Dashboard Instantly

Nobody wants a blank dashboard. I built a bootstrap script that scans your existing agent directory (looking for .env files, package.json, and memory files) and imports everything into DashClaw immediately.

If you already have 10 integrations set up, you just run the bootstrap script and they show up on the dashboard in about 5 seconds. It makes the "Time to Value" almost instant.

Integrations Management

Security First

Since I am releasing this as open source, I spent today doing a deep security audit. All sensitive settings (like your OpenAI or Anthropic keys) are encrypted in the database using AES-256-CBC. I also implemented strict multi-tenant isolation so that if you host this for your team, users stay in their own workspaces.

Real-time Security Monitoring

Check it out

I am really proud of how this turned out. It is a dual-layer ecosystem that actually lets you scale an agent swarm without losing control.

If you find this useful, I am an independent builder and I actually added a tip section to the README because I am trying to keep this project going!

Repo: https://github.com/ucsandman/DashClaw

SDK: npm install dashclaw

Website: https://dash-claw.vercel.app/

I would love to hear what you guys think about the "Real Infra" approach to agents. Let me know in the comments!

DEV Community: Wes Sander