[0:00] 36% of open claws AI skills ship with at
[0:03] least one security flaw. That's from
[0:05] published audit flashing across the
[0:07] screen right now. So when Pokey launched
[0:09] a sandboxed agent claiming 70% lower
[0:12] token costs, zero setup and
[0:15] enterprisegrade security, I gave it free
[0:17] real workloads. No demo magic, real
[0:20] APIs, real accounts, real money on the
[0:22] line. One token cost. Most agents are
[0:25] GPT class models with a flaky function
[0:28] call-in layer bolted on. Every
[0:30] multi-step task burns thousands of
[0:32] tokens because the model rereads its own
[0:34] context a dozen times to stay on track.
[0:37] Two, setup pain. Half the open- source
[0:39] stacks need a dedicated Mac Mini running
[0:41] 24/7. Manual OOTH juggling and a
[0:44] credentials file you are quietly praying
[0:47] nobody pushes to GitHub. Three,
[0:49] security. That 36% number is not
[0:52] theoretical. It is why most enterprise
[0:54] teams will not touch agentic workflows
[0:56] yet. But the real moat isn't the model,
[0:59] it's the architecture around it. Every
[1:00] agent runs inside the isolated sandbox
[1:04] they call pokey claw. Encrypted
[1:06] credential vault. Your authentication
[1:08] tokens never sit in plain text. Approval
[1:11] workflows and any action that touches
[1:13] the outside world. Raw based access
[1:15] control. Full audit log of every tool
[1:18] call and zero local setup. No Mac Mini,
[1:21] no a Docker Compose file, no
[1:23] babysitting. Before the demos, let's
[1:25] break down the interface. You land here.
[1:27] The main dashboard shows your current
[1:29] plan, credit balance, and usage chart.
[1:31] I'm on pro 14,976
[1:35] credits remaining. Left sidebar, Pokey
[1:37] Claw is the main agent workspace. It
[1:39] still has the new badge, which tells you
[1:41] how early this product is. Below that,
[1:44] deep research, e-commerce agent,
[1:45] authentications, organization settings.
[1:48] Authentications is where your OOTH
[1:50] connections live. That's the credential
[1:51] vault in practice. Every app you connect
[1:54] shows up there. Scoped manage never
[1:56] exposed in plain text. Inside Pokeclaw,
[1:59] the files panel on the left is your
[2:01] agents working directory. Every file it
[2:03] creates or downloads lands there. Top
[2:06] right skills schedules tasks
[2:07] actions. Schedules is where your
[2:09] recurring workflows live after you set
[2:11] them up. Tasks is the execution log.
[2:13] Skills is where pre-built agent
[2:15] templates live. First launch takes 2 to
[2:17] 3 minutes to warm the sandbox. After
[2:19] that instant workload one, I asked
[2:22] Pokeclaw to build me a working
[2:24] influencer marketing tool. Front-end web
[2:26] app scoring real engagement rate per
[2:29] platform across LinkedIn X, YouTube, Tik
[2:32] Tok, Instagram with industry
[2:34] categorization. This is not a toy
[2:36] prompt. This is a oneweek sprint at most
[2:39] agencies. Watch what actually happens
[2:41] here. Three sub agents spinning up
[2:43] simultaneously. Agent one is handling
[2:45] the config layer and layout scaffold.
[2:47] Agent two is pulling in the data models.
[2:49] Agent three is setting up the store
[2:51] layer and they're not waiting for each
[2:52] other. That's a parallel execution, not
[2:54] a queue. A junior dev doing this
[2:56] sequentially would take 3 to 4 hours
[2:59] just on setup. The agent does it in
[3:01] under 90 seconds, self-corrects the npm
[3:03] errors midrun, and reports back with a
[3:06] build status table. Eight pages, zero
[3:08] errors. That's the token efficiency in
[3:10] action. The model stays on task because
[3:12] the architecture handles the state
[3:14] management, not the context window. App
[3:17] preview lands in chat. 80 mock profiles
[3:19] for testing. Now, here's where I pushed
[3:21] it. I asked it to swap the mock data for
[3:23] real data. It proposed three API
[3:25] integrations on its own. Asked me to
[3:27] pick industries and a sample size, 50
[3:30] influencers, mixed verticals, and went.
[3:32] Three more agents in parallel,
[3:33] harvesting live from X and YouTube. 26
[3:36] YouTube channels, 65.6 million combined
[3:39] subscribers. Real numbers, real handles.
[3:42] Final dashboard. Real cards. Fire ship.
[3:44] Mattwolf. Coder. Coder. I filtered to
[3:46] English. 10,000 minimum follower count.
[3:49] AI tech industry. Click into a profile.
[3:51] Sam Altman. Engagement metrics. Content
[3:54] analysis. Audience and geography. Final
[3:56] click opens the actual Sam Alman X
[3:58] profile. Workload two. One prompt. Every
[4:01] day at 12:30 PST. Search the latest AI
[4:04] news on X. Summarize it. Email me the
[4:07] digest and posts to my ex and LinkedIn.
[4:10] Scheduled multiplatform with my real
[4:12] accounts. Agent confirms the schedule
[4:14] and runs the first job immediately so I
[4:16] can verify before walking away. This is
[4:18] the moment that separates Pokeclaw from
[4:20] a raw API integration. The agent isn't
[4:22] storing your Gmail token in a config
[4:24] file. It isn't asking you to paste it
[4:26] into a prompt. The authentications vault
[4:29] handles the oath handshake scopes the
[4:31] permission read and send only and the
[4:34] agent request actions through that
[4:36] scoped connection. You revoke the oath
[4:39] tomorrow. The agent loses access
[4:41] immediately. That's the security model.
[4:42] One screen, one click, auditable at
[4:44] every step. Now watch this. This is the
[4:46] moment that matters. The agent drafts
[4:48] the expost and the LinkedIn post and
[4:50] shows me both before publishing. Big
[4:52] green approve button. Nothing, and I
[4:54] mean nothing, touches a public platform
[4:57] until I click. That is the human in the
[4:58] loop gate. That is the visible security
[5:00] mode. Compare that to a typical N8 flow
[5:02] where the web hook fires and your only
[5:05] fall back is hoping the LLM didn't
[5:08] hallucinate a sentence into your CEO's
[5:10] LinkedIn. Workload three, the kind of
[5:12] task I'd normally hand to junior analyst
[5:15] on a Friday afternoon. Find current
[5:17] pricing for five automation tools.
[5:19] Zapier make.com nadn bardine active
[5:23] pieces build a Google sheet titled
[5:25] automation tool price and comparison
[5:28] March 2026 specific columns summary row
[5:31] with cheapest most expensive best value
[5:33] schedule a monthly auto refresh five
[5:35] parallel research agents one per vendor
[5:37] five tabs five sites simultaneously
[5:40] status flips to done one at a time same
[5:42] approval pattern as the productivity
[5:44] demo but notice what's being approved
[5:46] here not a vague write to spreadsheet
[5:48] the approved gate shows the exact call
[5:51] Google chart sheets.update charts cell
[5:54] values with the payload. You can see
[5:56] exactly which cells are being written
[5:58] before anything changes in your drive.
[6:00] That's the audit trail that compliance
[6:02] teams need. Every tool call timestamped,
[6:04] logged, reversible before it fires.
[6:07] Pricing array compiles. Approval gate
[6:09] again this time on Google Sheets update
[6:11] cell values. Same pattern as the
[6:13] productivity demo. Nothing writes to my
[6:15] drive without me clicking. Let's talk
[6:17] pricing because this is where the credit
[6:19] math either makes sense or doesn't. Four
[6:22] tiers, free 500 credits a month, enough
[6:25] to test the interface and run a handful
[6:27] of tasks. Light at $19.99, 5,000
[6:30] credits, that's your solo operator tier
[6:33] covers daily scheduled workflows at
[6:35] moderate volume. Pro at $49.99 gets you
[6:39] 15,000 credits. That's what I'm running.
[6:41] And as you saw, three full workload
[6:44] demos burned 24 credits total. The math
[6:47] on that is very favorable. Ultra at
[6:49] $199.99
[6:51] pushes to 100,000 credits. That's the
[6:53] small team tier. Switch to yearly and
[6:56] you get 30% off across the board. Light
[6:59] drops to $14 a month. Pro to 35, ultra
[7:02] to 140. If you're running this as a
[7:04] recurring production workflow, daily
[7:07] news digest, weekly research reports,
[7:09] monthly price and trackers, the annual
[7:11] plan is the one that makes the token
[7:13] savings argument complete. There's also
[7:15] a credit slider if you want to top up
[7:17] midcycle without upgrading your plan
[7:19] tier. Flexible, no locked in overage
[7:21] fees. The link with the current offer is
[7:23] in the description. Okay, the honest
[7:25] review. Use Poke Claw when you need a
[7:27] chain workflow that touches multiple
[7:29] real apps with real credentials. When
[7:32] human approval gates are non-negotiable
[7:34] for compliance. When you've outgrown
[7:36] stitching together N8N plus make plus a
[7:39] custom GPT and you want one sandbox that
[7:42] handles the whole graph. The token
[7:43] economics matter most when you're
[7:45] running these workflows daily, not
[7:47] weekly. Skip it when you're doing
[7:49] singlestep automation as Zap or Zap can
[7:51] already cover. when your team has zero
[7:53] compliance overhead and security
[7:54] genuinely isn't a concern yet or when
[7:56] you're philosophically opposed to closed
[7:58] source agents and you'd rather wire your
[8:01] own fair position just know the trade
[8:03] versus the field against openclaw the
[8:05] security delta and the 70% token
[8:07] reduction are the headlines against
[8:09] Kimmy the thousand plus native
[8:11] integrations and the approval
[8:13] architecture are what actually move you
[8:15] to production against genspark the
[8:17] parallel sub aent orchestration is
[8:19] meaningfully ahead against the DIY and
[8:21] aid and stack. You trade flexibility for
[8:23] reliability. Fair trade if your time
[8:26] costs more than your tooling. Quick
[8:27] disclosure before we wrap. This video is
[8:29] sponsored by Pokey AI. The link, the
[8:32] trial, the current launch offer are all
[8:34] in the description below. Zero setup,
[8:37] lower costs, fewer hallucinations, more
[8:39] done. That's the slogan. Use it where it
[8:41] fits, skip it where it doesn't. That's
[8:43] how I always did