01 · free
Agent diagnosis
A 30-minute conversation to choose a process, assess the data, and determine whether an agent has credible potential.
Book a diagnosis →Chief AI Agents Officer - Agent Implementation
Chief AI Agents Officer. From strategy to work completed by agents.
I do not stop at a presentation or prototype. We select a process, define a measurable outcome, and deploy an agent inside the tools your team already uses.
20+ yearsof engineering and leadership
20+ applicationsin the Legatus agent ecosystem
Strategy + Buildone accountable outcome owner
The first 30 days
The first agent should start working, not end up in a slide deck.
The first month turns an unclear idea into a validated use case. Scope depends on the process and data access, but the sequence remains consistent.
- 01/
Process selection
We document the current state, owner, exceptions, and cost of manual work.
- 02/
Outcome design
We define the target state, success metric, autonomy boundaries, and human escalation points.
- 03/
Build and integrations
I connect the agent to real data and tools and implement the required guardrails.
- 04/
Launch and measurement
We launch a controlled scope and measure quality, time, cost, and human intervention.
How to start
Three engagement levels. One clear next step.
02 · pilot project
Agent Pilot
One process, a clearly defined outcome, a working agent, integrations, guardrails, and measurement.
Discuss the scope →03 · ongoing engagement
CAAO Partnership
$3,500 / month
Accountability for agent strategy, subsequent implementations, standards, architecture, and team adoption.
Discuss the scope →Pilot scope and pricing depend on the number of systems, data quality, required autonomy, and process risk. They are agreed after diagnosis and before implementation begins.
Outcome language
Without a number, we do not know whether the agent helps.
Every implementation is framed as a change in how work gets done. I do not promise a result before seeing company data, but the measurement is agreed before the build starts.
Example · request handling
Now
Manual data collection from email, CRM, and calendar
Target
The agent prepares context, a response, and the next action for approval
We measure handling time, correct-proposal rate, cost per execution, and cases requiring human intervention.
Why CAAO
Your company does not need another tool. It needs an owner of the change.
A Chief AI Agents Officer combines strategic accountability with hands-on implementation, from selecting the process to a measured production rollout.
AI is everywhere, but nobody owns the outcome
Teams keep testing new tools, but no single person is accountable for priorities, security, and measurable business impact.
The prototype works, the implementation does not
A demo does not solve permissions, data quality, cost, observability, exceptions, or accountability for a wrong agent decision.
Automation stops between systems
Real work moves through email, calendars, CRM, documents, and applications without APIs. An agent must operate safely across all of them.
Technology never becomes the new way of working
Without process ownership, metrics, training, and a gradual rollout, even a strong solution remains an unused experiment.
Operating model
Start with the work and the outcome. Then choose the model, agent, and tools.
- 01/
Work diagnosis
We map repetitive tasks, decisions, data, and handoffs. We identify where an agent can reduce lead time or increase throughput.
- 02/
Agent and priority map
Every idea receives an owner, metric, risk level, integration requirements, and maintenance cost. Only initiatives with a credible return move forward.
- 03/
Agent Implementation
I build the agent, integrations, and guardrails, connect the tools, and design approval paths and exception handling.
- 04/
Production and adoption
We release in stages, measure quality, cost, and time, train the team, and expand the agent's capabilities based on evidence.
Agent Implementation
Agents embedded in the real work environment.
Gmail, Google Calendar, Slack, Notion, GitHub, HubSpot, internal systems, and the browser - with permission controls and a complete audit trail.
Communication agents
Draft and send messages, organize inboxes, schedule meetings, and make sure the next action happens on time.
Operations agents
Update CRM, documents, and internal systems while coordinating multi-step work across applications.
Knowledge and RAG agents
Answer from company sources, provide supporting context, and escalate when the available evidence is insufficient.
Browser agents
Complete controlled tasks in systems without APIs using a real browser and an auditable record of every action.
Sales and support agents
Qualify requests, prepare context, support responses, and help teams react at the right time.
Platform and governance
Permissions, evaluations, observability, cost limits, prompt versioning, and human-in-the-loop controls.
What stays in your company
Operational capability, not vendor dependency.
Architecture, metrics, documentation, and knowledge are part of the implementation. Your team understands what the agent does, where its boundaries are, and how to extend it safely.
- One accountable owner for the agent strategy and implementation order
- A first production use case instead of another demo
- Integration with the existing work environment instead of a separate AI island
- Quality, time, cost, and team adoption metrics
- An architecture ready for additional agents
- Documentation and operational knowledge retained inside the organization
A strong fit
A CAAO makes sense when...
- ✓Your processes span multiple tools and rely on manual information handoffs.
- ✓Your team sees the potential of AI but lacks an implementation and architecture owner.
- ✓You want strategy and hands-on implementation without immediately building a separate department.
- ✓You need production agents with controls, measurement, and escalation paths.
FAQ
Before we start
How is a Chief AI Agents Officer different from a Fractional CTO?
A CAAO focuses on designing how an organization works with AI agents: process selection, agent architecture, integrations, governance, implementation, and adoption. A Fractional CTO has a broader technology mandate covering the whole product, team, and roadmap.
Does the engagement end with strategy and recommendations?
No. The model includes Agent Implementation: prototype, integrations, guardrails, tests, production launch, observability, and knowledge transfer to your team.
Do we need to replace our current tools?
Usually not. We start with the systems your company already uses. The agent can connect through APIs, events, or controlled browser automation when a native integration does not exist.
How do you limit the risk of agent errors?
Autonomy expands in stages. I use structured outputs, validation, least-privilege access, cost limits, audit trails, evaluations, and human approval for higher-risk decisions.
How does an engagement start?
We begin with a diagnostic conversation and choose one process whose current state, expected outcome, data, risks, and success metric can be clearly described. Only then do we select the model and tools.
Chief AI Agents Officer
Let us choose one process an agent can genuinely take over.
The first call diagnoses fit across the process, data, risk, and the outcome worth measuring. No tool presentation for its own sake.
Book a diagnostic call