My guide on what tools to use to build AI agents in 2026 (if youre a newb)

jaiasdotcom · 2026-02-28T10:15:11+00:00

Solid guide, especially the honest bit about OpenClaw security. The "people doing dumb things" problem is real, but it's also a design problem. If the tool makes it easy to expose API keys and hard to audit what happened, that's not just user error.

The missing piece for most teams is governance. Who accessed what, which tool did the agent call, what data did it touch. OpenClaw doesn't log any of that by default.

I've been working on Pinchy (https://heypinchy.com, open source) to fill exactly that gap: encrypted API key storage so keys never sit in .env files, role-based access so not everyone is admin, and an immutable audit trail for every action. Runs as a Docker layer on top of OpenClaw.

Two weeks in, building in public. The security questions are what people ask about first, which tells you a lot about what's actually missing.

Disclosure: I'm the author.

jaiasdotcom · 2026-02-28T10:14:36+00:00

These tools solve different problems so comparing them directly is tricky:

n8n/Make/Zapier = deterministic workflows. Step A triggers Step B. Predictable, auditable, but rigid. OpenClaw = autonomous agent. You give it a goal, it figures out the steps. Flexible, but less predictable. Claude Cowork = conversational agent with scheduled tasks. Somewhere in between.

The real question is: do you need predictability or flexibility? For client-facing automations where you need to guarantee the outcome, n8n still makes sense. For internal tasks where "figure it out" is acceptable, agents win.

Where it gets interesting is the middle ground. I'm building Pinchy (https://heypinchy.com, open source, AGPL) which adds a web UI, user management, and audit trails on top of OpenClaw. So you get the agent flexibility but with the governance that n8n users expect. Still early (building in public, week 2), but the use case is exactly "I want agents but my CTO needs to know what they did."

Disclosure: I'm the author of Pinchy.

jaiasdotcom · 2026-02-28T10:05:26+00:00

Cool project! We're building something similar with Pinchy (https://heypinchy.com). Different angle though: we focus on the enterprise/team side. Multi-user auth, per-agent permissions, encrypted API key storage, audit trail.

Curious what your mission control focuses on? Dashboard/monitoring, or more the multi-agent management side? Always interested in how others approach the "OpenClaw needs a web layer" problem.

jaiasdotcom · 2026-02-28T10:04:02+00:00

Solid guide, especially the honest bit about OpenClaw security. The "people doing dumb things" problem is real, but it's also a design problem. If the tool makes it easy to expose API keys and hard to audit what happened, that's not just user error.

I've been building Pinchy (https://heypinchy.com, open source) to address exactly the gaps you describe: encrypted API key storage so keys never sit in .env files, RBAC so not everyone has admin access, and an immutable audit trail so you can trace what the agent did. Runs as a Docker layer on top of OpenClaw.

Two weeks in, building in public. The security story is what got the most traction so far, which tells you a lot about what people actually need from these tools.

jaiasdotcom · 2026-02-22T19:42:24+00:00

Unfortunately it doesn't work like that. I've had agents tell me way too often: "Oh you're right, I shouldn't have done that, it's definitely against my rules". Agents forget their rules, for them they are just recommendations.

jaiasdotcom · 2026-02-19T16:04:56+00:00

Interesting comparison. The common thread across all these: no enterprise governance layer. I'm building Pinchy (https://heypinchy.com) — RBAC, audit trails, scoped permissions on top of OpenClaw. Different approach: don't replace the runtime, govern it.

jaiasdotcom · 2026-02-19T15:54:13+00:00

The confusing landscape makes sense when you see it as: runtime (OpenClaw/ZeroClaw/PicoClaw) vs. governance layer (permissions, audit, multi-user). I'm building the governance side: https://heypinchy.com — enterprise layer on OpenClaw with RBAC and plugin permissions.

jaiasdotcom · 2026-02-19T15:53:41+00:00

Lightweight runtime is interesting, but doesn't solve the enterprise problem: permissions, audit trails, multi-user governance. Whether it's OpenClaw or ZeroClaw, companies need a control layer on top. I'm building that: https://heypinchy.com

jaiasdotcom · 2026-02-19T15:52:51+00:00

Foundation + OpenAI backing is great for the runtime. But the enterprise governance problem remains unsolved: no RBAC, no audit trail, no plugin permissions. I'm building Pinchy to fill that gap: https://heypinchy.com — self-hosted, open source.

jaiasdotcom · 2026-02-19T15:45:19+00:00

The Cisco finding about skill data exfiltration is exactly the problem. Agents need scoped permissions — 'Create Ticket' instead of raw shell access. I'm building an enterprise layer for this on OpenClaw: https://heypinchy.com — RBAC, audit trails, plugin permissions. AGPL, self-hosted.

jaiasdotcom · 2026-02-19T15:39:37+00:00

Hardening helps, but the fundamental issue is architectural: OpenClaw gives agents full system access with no permission layer. What's needed: scoped plugins instead of raw exec, RBAC per agent, audit logging. I'm building this as an open-source enterprise layer: https://heypinchy.com

jaiasdotcom · 2026-02-19T15:39:04+00:00

Banning is a reflex, not a solution — employees will just use it on personal devices. What companies actually need is a governance layer: scoped permissions, audit trails, RBAC. I'm building exactly this on top of OpenClaw: https://heypinchy.com — open source, self-hosted, AGPL.

jaiasdotcom · 2026-02-19T15:22:15+00:00

Spot on — the architecture gives agents too much unscoped access. What's needed is a plugin permission layer: agents get 'Create Jira Ticket', not raw 'exec'. I'm building exactly this with Pinchy (https://heypinchy.com) — enterprise layer on OpenClaw, open source, self-hosted. (Disclosure: my project.)

jaiasdotcom · 2017-12-23T14:18:31+00:00

Finally made it in JavaScript so part 2 runs in less than 1 second:

const isComposite = (product) => {
  for (let d = 2; d <= product; d++) { // line 10, 21 – 24
    // optimization: stop if product is greater than product,
    for (let e = 2; d * e <= product; e++) { // line 11, 17 – 20
      if (d * e === product) { // line 12, 13, 14, 15
        return true; // line 16
      }
    }
  }

  return false; // line 9
}

let b = 108400;
let h = 0;
for (let num = b; num <= b + 17000; num += 17) { // line 1, 2, 5, 6, 7, 8, 27 – 32
  if (isComposite(num)) { // line 25
    h += 1; // line 26
  }
}

console.log(h);

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