Steward will hire from his portfolio and run every AI agent like a real employee, including Claude, ChatGPT, and Gemini. You onboard Steward once. He does the rest.
Most AI tools make you configure every assistant yourself. ACE starts with one clear relationship: you work with Steward, and Steward helps organize the AI team around your business.
A real person helps Steward learn your goals, people, processes, and rules. After that, describe the work you need done. Steward helps define the role, prepare the right specialist, and keep its access within the boundaries you approve.

Learns how your company works, then manages each specialist from role definition through safe offboarding. Steward coordinates the team while remaining inside the policies and approvals you set.
Tell Steward what needs to be done in the same language you would use with a manager. Steward turns that need into a clear job, prepares a specialist with the right knowledge and tools, and shows you what requires approval. You do not need to write prompts, choose models, or configure technical workflows.
Not a spec. Not a workflow diagram. Just what is not getting done. Steward writes the job description — you don't.
Steward already knows your company, so what Steward proposes is specific to you — the role, the tools it gets, and the line it does not cross.
Hired in a sentence, and born already knowing your company — Steward passes down everything learned at onboarding. No second onboarding. Ever.
Every hire is a position you chose, for a problem you actually had. Add them one at a time as the work shows up — and let one go the moment it isn't earning its seat. Steward runs them as a team, not as six disconnected chatbots you have to keep track of yourself.

Customer Support
BD Analyst
Data Analyst
Workforce Analyst
Proposal WriterSteward routes the work. Customer Support takes the enquiry, the Data Analyst pulls the numbers it needs, the BD Analyst follows up on it. You asked one person. Three did the work.
Your culture, your policies, your people and your voice belong to the company, not to one agent. Every new hire inherits all of it the moment Steward brings it on.
They cooperate; they do not give each other orders. Every line of authority runs back to you — which is why letting one go is one word, and not a migration.
A chief of staff does not become effective by reading a job description. They learn how their leader makes decisions, communicates, sets priorities, and defines good work. Steward’s onboarding follows the same principle. In a guided session, you teach Steward about yourself and your company using only the information you choose to provide. Once trained, Steward uses that understanding to onboard each specialist according to its role.
Your priorities, risk tolerance, and standards — the judgment behind your decisions, not merely the decisions themselves.
Your tone, vocabulary, and working style — so anything prepared in your name sounds familiar and appropriate.
What deserves escalation, what can proceed routinely, and what must always wait for your approval.
The experience, responsibilities, and commitments that shape how you lead — provided by you, never inferred through surveillance.
What the organization does, what it offers, how it works, and the standards every role is expected to uphold.
Who is responsible for what, who approves each decision, and where a specialist should escalate when it needs help.
The handbooks, prices, operating rules, and approved sources required to perform the job correctly.
A clear job description, expected outcomes, performance measures, and boundaries the specialist cannot cross.
Only the accounts and permissions required by the role — granted when needed and revocable when the assignment ends.
Steward holds the complete orientation. Each specialist receives only the information required to perform its assigned job.
A receptionist may know your booking policy and escalation contacts. It does not need access to your financial plan or board materials.
You onboard Steward once. Steward then handles orientation for every specialist that joins your agent workforce — assigning the role, providing the right company knowledge, setting expectations, and limiting access.
A receptionist. A data analyst. A bookkeeper. Each has a title, a core competence, a short tool list and a line it does not cross — exactly like a person you would put on payroll.
Each specialist joins your AI workforce for a defined purpose. It receives a job description, limited access, performance expectations, and a clear reporting path. When the work or role ends, ACE offboards it safely and records what changed.
Not switched on. Hired — into a role with a title, a remit, and an agreed limit, composed by Steward out of the six layers.
It works where you can see it. Every action it takes is on the record, and the ones that matter come to you first.
The season ends, the project ships, or the role changes. You remove the specialist's authority, and ACE completes the offboarding process.
This is the difference between adding another AI tool and building a governed AI workforce.
Every role has a purpose, clear limits, visible performance, and a controlled exit.
Every ACE agent is built on a standardized 6-layer stack — from raw infrastructure at the bottom to the user interface at the top. Each layer has explicit contracts, quality targets, and governance controls. This is how an AI agent goes from "a prompt" to a production-grade digital employee.
Compute, storage, networking. Auto-scaling, secret management, observability. The foundation everything runs on.
Read, write, compute, communicate. Every action an agent can take — registered, versioned, rate-limited, and logged.
Knowledge bases, RAG pipelines, memory, enterprise data. The information agents need to make grounded decisions.
LLM cognition — chain-of-thought, confidence scoring, escalation logic. Where agents actually think and decide.
Workflow coordination — sequential, parallel, conditional, event-driven. How multiple agents work together as a team.
APIs, chat, voice, dashboards, webhooks. How humans and systems interact with the agent workforce.
Every AI agent starts supervised and earns autonomy through measurable performance. The 4-tier career ladder mirrors how enterprises promote human employees — with probation periods, competency tests, and evidence-based advancement. No agent skips a tier.
| Tier | Role | What They Do | Autonomy | Example |
|---|---|---|---|---|
| ACE-Core | Task Worker | Executes single, well-defined tasks under direct supervision | Supervised | Weather alerts, document lookup, data extraction |
| ACE-X | Specialist | Owns an entire domain — multi-task, tool-augmented, deep expertise | Semi-autonomous | Payroll analytics, scheduling optimization, compliance review |
| ACE-Prime | Orchestrator | Coordinates multiple agents across domains, manages workflows | Autonomous | Root orchestrator routing queries to 15 specialist agents |
| ACE-Titan | Strategic Advisor | Enterprise-wide cognition — risk forecasting, policy interpretation, executive briefings | Full (governed) | C-suite strategic advisor, organization-wide monitoring |
Every ACE agent follows a structured 7-stage lifecycle — the same rigor enterprises apply to human employees. Define the role. Build the agent. Onboard it into systems. Train it on domain knowledge. Monitor its performance. Improve and promote. And when the time comes, retire it gracefully without losing knowledge.
"Agents don't replace humans — they extend the enterprise. Every agent has a manager, a role, KPIs, and accountability. No autonomous AI runs unsupervised."
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