Turtle AI Coworker is the engine an enterprise AI workforce runs on. Build agents, teams and AI employees from the ground up: every task, table, tool grant, model, trigger and approval gate is yours to configure — and every action runs through a policy engine and lands in one immutable audit trail. Or skip the blank page: install a complete working department in about 15 minutes.
A writing tool here, a support bot there, an SDR tool somewhere else. None share context. None hand work to another. Ten AIs, no workforce.
Initiatives stall between demo and production. Integration takes months, the security review has nothing to audit, and promising pilots quietly die.
Autonomous AI without approval gates is a liability. Bolt governance on afterward and no one can prove what happened, or who authorized it.
From an empty workspace to a governed, working department: install, first run, an approval on a phone, and the audit trail it all lands in.
This is not a wrapper with three settings. It is an engine where each control is explicit, each change is versioned, and each run leaves a trace you can audit. Configure as deep as you need; the defaults are safe either way.
An eight-tab editor: typed inputs, ordered tasks with expected outputs, tool grants, knowledge, table permissions, triggers and advanced controls. Nothing is a black box.
every run traced step by stepPer-table read, create, update and delete for every worker, with per-run caps and row-level filters. Least privilege is the default, not a hardening project.
every write attributed & loggedA cheap model for routine steps, a frontier model for hard reasoning, chosen per agent and swappable without a rebuild. Your keys, your vendor relationship.
per-message token & USD accountingSchedules, integration events, webhooks, table-row events, agent chains and thresholds, with filters, debounce windows and rate caps you set.
every firing logged, even the filtered onesFour scopes per worker, from human-in-the-loop to full autonomy, with hard caps per turn. Held actions land in one approval queue, single-use grants enforced at execution time.
approvals recorded with the run70+ integrations plus any REST API or MCP server as a custom tool. Credentials sit in an encrypted vault, decrypted only at request time, never shown to a model.
every credential read audit-loggedA policy engine on every tool call — allow, deny, hold for approval — plus hard cost caps and circuit breakers that pause a runaway worker before it grows.
every evaluation logged, even the allowsRole-scoped scorecards for every agent, team, employee, model and user: cost per outcome, hours saved, success rate, p95.
one ledger under all of itThe engine gives you full control. The library gives you a running start. Both end in the same governed runtime.
Build agents, teams and employees from the ground up: tables, knowledge, triggers, any model per agent with your own keys. Every control explicit, every change traced. Pack your own workspace when it works.
Explore the engineSkip the blank page: a complete working system, tables, agents, triggers and a named AI employee, live in about 15 minutes. Then reconfigure any part of it; a pack is the same engine, pre-wired.
Browse Solution PacksSingle workers for single jobs: one agent, team or employee at a time, installed from the gallery and adapted to your stack.
Browse templatesConnect Turtle AI Coworker to Claude once, then describe what you need: "automate cold email outreach and book meetings." Claude searches the gallery, installs the right pack or template, customizes it to your ask, hands you a secure link to authorize your tools, and tells you what's left. You watch the results in the dashboard.
Works with any MCP client. We lead with Claude because it is the smoothest, but ChatGPT, Cursor, Codex and others connect the same way.
Walk the path a request takes, from the moment it arrives to the traced result. Plain English, or flip to the technical terms.
Everything below is one product. Work comes in at the top, gets done in the middle, is grounded in your data, and is governed the whole way down. Step through the journey of a single request, or press play.
A teammate asks in plain language, a schedule fires, an event or webhook comes in from your tools, or your own code calls the API. It all enters through one interface.
A single Sequential Agent for one task, an Agentic Team when specialists must collaborate behind a router, or an AI Employee that owns a whole role and remembers context between jobs.
The worker reads the request and looks things up, plans the steps, acts on your tools, checks itself against your rules, writes the result, and records every step, looping until the job is done.
As it runs, it reads and writes your Tables, searches your Knowledge by meaning, calls your Tools in the apps you already run, and reasons on whichever Model you choose.
Permissions decide what each worker can touch, approval gates hold risky writes for a human to sign off, budgets cap spend, and a full trace records every run for audit.
Start with a single task and climb to a whole department. Each rung is built from the one below it, so you grow without rebuilding.
One worker, one job: typed inputs, ordered tasks, scoped access, a traced result. Fires on chat, schedule, event or another agent.
One chat, many specialists. Each message is routed by intent to the right sub-agent, then handed back, all under one shared scope.
A named coworker who owns a role: its own agents, tables, knowledge and memory. Delegate by chat, schedule or event.
The whole department, pre-wired: tables, agents, an AI employee and triggers in one manifest. Installed in about fifteen minutes.
Everything the engine can do, pre-configured into working departments: the data tables, the agents, the triggers, and the AI employee who fronts the role. Install one, then reconfigure any part of it. The full blueprint is public; read it before you sign in.
Recruiting and Staffing Ops is one manifest. Install it and every part below arrives pre-wired, in dependency order, with governance defaults already set. This is a live pack from the library, not a mockup.
Open roles you are recruiting for, with the must-haves the screener checks candidates against. Drives screening, submittal packages, and the pipeline digest.
Candidates in your pipeline. The screener checks each against the job's must-haves; the stage tracks where they are. Rejections and offers are always a human call.
Interview scheduling coordination per candidate. The coordinator drafts availability requests and proposed times; a human sends and confirms.
Recruiting pipeline metrics the digest writes: open jobs, new candidates, submittals, interviews scheduled, and placements.
Your recruiting playbook: screening standards, submittal format, candidate communication voice, and compliance guardrails. Agents ground screening, outreach, and submittals in this. Replace the placeholder with your own.
Screens a candidate against a job's must-haves and nice-to-haves, records a match result and reasoning. Never rejects.
Drafts availability requests and proposed interview times, tracking scheduling status. Never confirms a time itself.
Drafts candidate outreach and status-update messages per the communication voice. Draft only.
Builds a submittal package mapping a candidate's experience to the job's must-haves, for a hiring manager or client. Draft only.
Reads job orders, candidates, and interview scheduling and produces a pipeline digest with what needs attention.
Owns the department: its own agents, tables, knowledge and channels under one instruction set. Chat with it, assign work, review what it ships.
Set during install: whether the employee needs human approval before external actions. Enforced at execution time, not on the honor system.
Per-table read, create, update and delete for every agent, with tools scoped and call-capped.
The installer builds the department bottom-up in dependency order: tables first, then knowledge, agents, triggers, and finally the employee. Every step is logged live.
Ends with a smoke test, not a hope.
Name collisions are resolved during setup (use existing, rename, or replace). Manifests run sandboxed, and only Official and Verified manifests run unprompted.
Browse Solution PacksMost AI has one entry point, a chat box. A department that runs without being pushed needs more.
Ask a team or employee directly, answered live, token by token.
A row is created or a field changes, and the right agent runs automatically.
Cron triggers fire daily or weekly work with no one asking.
An AI employee calls a sequential agent as one of its own tools.
Voice-capable: an agentic team driven through a telephony-grade voice stack.
Turtle AI Coworker is its own MCP server. Claude, ChatGPT, Cursor or any MCP client can set up, configure and drive the whole platform from a chat. See it in action →
AI pilots die because nobody can answer four questions. Turtle AI Coworker answers all four by design. Governance is a layer the whole platform runs through — a policy engine in the execution path, not a settings page bolted on top.
Per-agent Read / Create / Update / Delete on every table, and a policy engine on every tool call — rules written against a catalog of 1,095 named integration functions, with allow, deny, hold or alert as the verdict.
Held actions wait in one queue with the holding rule attached. Approvals are single-use grants enforced at execution time — an injected or unapproved plan can't perform a write. Delegation covers absences; stale asks escalate.
Every run and tool call logged: inputs and outputs sanitized, PII flagged, secrets redacted, read-vs-write classified — and each run pinned to the immutable config version it executed under.
Hard budget ceilings with breach-date forecasting, circuit breakers that auto-pause a runaway worker, a prompt-injection shield over untrusted content, and a kill switch on any live run.
A workforce that runs around the clock needs oversight that travels. The same queue, policies and audit trail — on six surfaces.
The command center: build, govern, watch, audit.
Approvals, the morning briefing, audit drill-down, critical alerts.
Chat and the queue, resident at the OS level.
Delegate and approve from any page in Chrome.
Claude, ChatGPT or Cursor drive the whole platform.
The same governed session, spoken.
Not our claims, your scorecard. Every department reports the same way real ones do.
What each screened candidate, matched invoice or resolved ticket actually costs, in tokens and dollars.
Time the department returns to your team, rolled up per role and per period.
How reliably runs complete, and how long the slowest ones take.
Live spend against the budget you set, with circuit breakers that can halt a runaway before it grows.
A deterministic runner for procedures, a reasoning engine for teams, an autonomous runtime for employees, all under one audit surface. Hybrid retrieval on our default engine, token-level streaming, three-tier memory, per-message cost accounting, and its own MCP server.
One engine, configurable to the last permission, governed like staff. A working department in about fifteen minutes if you want the head start. Your data, your models, your rules.
