Meet your AI team.
Specialized agents. One shared mission.
Give each AI agent a real job, the tools for that job, and clear authority. They work together across your systems while your team keeps control of the decisions that matter.
Bring one workflow. We’ll show how a team of agents would run it.
MayaAI Chief of Staff
TonyAI Product Engineer
JackAI Sales Agent
JaneAI Marketing Agent
AI is moving from an individual tool to an execution layer.
The question is no longer which model to buy. It is which work to delegate, under what controls, and how to measure the result.
One team. Four roles. One governed system.
The roles are examples. Your AI team is designed around the work your business needs done.
AI Chief of Staff
MayaPlans and coordinates. Turns goals into work, delegates to specialists, watches progress, resolves blockers, and escalates decisions.
HUMAN-RESERVED: Financial commitments, contracts, legal decisions, strategy changes, security changes.
AI Product Engineer
TonyBuilds and validates. Works in code, tests, GitHub, and staging. Prepares changes and stops at the production approval gate.
HUMAN-RESERVED: Production deployment approval.
AI Sales Agent
JackBuilds pipeline. Researches accounts, qualifies prospects, prepares outreach, follows up, and feeds objections back to the team.
HUMAN-RESERVED: Contracts, pricing, material representations, unusual discounts, closing.
AI Marketing Agent
JaneCreates demand. Monitors the market, plans campaigns, drafts content, analyzes performance, and tests new messaging.
HUMAN-RESERVED: New positioning, crisis communications, public statements, brand changes, significant spend.
Swap in the roles your work needs: Support Triage, Document Intake, Finance Operations, Data Analysis. The structure stays the same. See the workflow patterns →
A feedback loop, not four separate bots.
Janespots interest
Jackcaptures objections
Tonyaddresses technical gaps
Mayacoordinates and escalates
Jane’s campaign generates interest, but Jack’s outreach is not converting. Maya asks Jack to classify recurring objections, asks Jane to test revised messaging, and asks Tony whether a product or demo change could address a repeated technical concern. Maya summarizes the evidence and a recommendation for management. Management decides.
What the Chief of Staff does, and what it never becomes.
Understand
Interpret the objective and constraints
Decompose
Create work and success criteria
Delegate
Route to specialist agents
Observe
Review outputs and KPIs
Adapt
Adjust within authority
Escalate
Ask humans when required
Maya does not silently inherit executive authority. Financial commitments, contracts, legal decisions, material strategy changes, security changes, and other high-impact actions remain with authorized humans.
Autonomy is granted by risk level, not by capability.
Capability and authority are not the same thing.
| Risk | Example | Agent authority |
|---|---|---|
| Low | Research, summarize, classify | Autonomous, with logging |
| Moderate | Draft communications, low-risk updates | Autonomous within policy, with audit |
| Elevated | Create a software PR, prepare a customer action | Autonomous preparation, reviewable evidence |
| High | Production deployment, external commitment | Explicit human approval |
| Critical | Wire transfer, contract execution, privileged security change | Named approver with strong authentication |
The target is maximum safe autonomy at acceptable business risk, not maximum autonomy. See how we draw the line task by task →
Built like a team. Governed like an enterprise system.
Identity
Every agent has a distinct role and identity
Permissions
Only the tools and data its role needs
Approvals
Humans keep the reserved decisions
Audit
Every action stays reviewable
Your cloud, a shared model, or fully managed.
Customer-hosted
Agents and the control plane run in your cloud or VPC. For strict infrastructure ownership and data residency.
Hybrid
You keep sensitive systems, identities, and credentials. JDK runs selected orchestration and operations.
JDK managed
JDK operates the approved agent environment and lifecycle. For teams that want outcomes without a platform team.
Measure work, not tokens.
Task success rate
Completed to acceptance criteria
Cycle time
Assignment to accepted result
Human intervention
Share requiring correction or takeover
Escalation rate
Share intentionally routed to a human
Cost per outcome
Model, infrastructure, and review cost
Rework rate
Reopened, reversed, or corrected
Control exceptions
Policy, permission, or security violations
Autonomy rate
Safely completed without intervention
Autonomy is not the objective. An agent that escalates a high-risk exception exactly when policy requires is performing correctly.
Build the organization once. Add digital workers as the case emerges.
AI Work Discovery Sprint
Map the work. Score fit, value, reversibility, and risk. Get a ranked backlog and a kill list.
See the service →02First Agent Pilot
One agent, one real workflow, measured against baseline.
See the service →03Secure Agent Foundation
Identity, approvals, audit, cost controls, tool governance. Built once, reused by every agent.
See the service →04Managed Agent Operations
Monitoring, tuning, cost, and a pipeline of next agents. The Chief of Staff role comes in here, once there is a team to coordinate.
See the service →Each new agent gets a role-specific policy envelope, not a new security architecture.
See the team in action.
Bring one workflow. We'll show how a team of agents would run it, what stays human, and what it would cost to operate.
Book a demo