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.
See the reference architecture ↓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.
Inside your private network. Nothing listens on the public internet.
Agents and the control plane run in your Azure VNet or AWS VPC. Your security team can review every path in and out before the first agent starts.
Your private subnet
- 01
Agent gateway
Connects to Slack or Teams outbound only. No inbound ports, no public IP.
- 02
One sandbox per agent
Each agent runs its tools in its own container with only the mounts and network its job needs.
- 03
Egress proxy
An allowlist per agent. An agent with no reason to reach a domain cannot reach it.
- 04
Memory and work queue
Postgres on private endpoints, encrypted at rest, inside your existing backup policy.
Connected over: private endpoint
05Model access
Azure OpenAI or Amazon Bedrock over private endpoints. Prompts, documents and tool output stay in your tenant. Models are assigned per task and can be swapped.
Connected over: managed identity / IAM role
06Identity and secrets
Each agent has its own identity. Credentials live in Key Vault or Secrets Manager, never in config files, never visible to the model.
Connected over: your SIEM
07Approvals and audit
Reserved decisions route to a named approver in the channel your team already uses. Every tool call, approval and exception is written to a log you own.
Kill switch
Revoke one agent’s identity and it stops. Revoke the gateway and the team stops. Both are one action in your cloud console, not a ticket to us.
What we need from you
A cloud subscription, a subnet, and a security contact. What you keep: everything.
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.
We run our own AI team on this design.
JDK's chief of staff and product engineer agents run on the architecture above, on a private host, working a shared board of real tasks. This is the same runtime, the same controls and the same approval gates we deploy for customers. We publish what we learn from operating it.
What broke, and what we changed
- What happened
- Agents stopped recalling what the other one had learned.
- Cause
- The memory layer only returns what the observing agent itself has seen, so giving each agent a private view cut off shared context.
- Change
- A shared view for team context, a private view per agent for the rest. Documented as a known pattern in the runbook.
- What happened
- A change to an agent's sandbox mounts had no effect.
- Cause
- Persistent sandbox containers reuse their original mounts until they are recreated.
- Change
- Mount changes now follow a stop, remove, restart procedure, and the runbook says so.
- What happened
- Two agents were both handing out work from the same board.
- Cause
- Each gateway ran its own dispatcher by default.
- Change
- One dispatcher, worker concurrency capped, and workers run outside the gateway process so a restart never kills a task in progress.
We publish these because a team that never reports a failure is not reporting.
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.
The questions your security and finance teams will ask.
Is this one of those open agent frameworks with the security problems?
The runtime is a tool choice, not the product. We deploy open, auditable agent runtimes inside your network with the controls shown above: no inbound ports, a sandbox per agent, allowlisted egress, and credentials the model cannot read. The public incidents you have read about came from agents running on laptops with broad access and unreviewed community skills. Neither is allowed in a JDK deployment. Every skill is reviewed before it is installed.
What happens when an agent is wrong?
It is caught at the gate it was designed to stop at. Low-risk work is logged. Elevated work produces evidence a person reviews. High-risk work waits for approval. When an agent is wrong below a gate, the rework shows up in the metrics and we adjust the policy or the job.
How do we shut it off?
Revoke one agent's identity and that agent stops. Revoke the gateway and the whole team stops. Both are a single action in your cloud console.
Which models? Can we switch?
Any model reachable from your network: Azure OpenAI, Amazon Bedrock, Anthropic, OpenAI, or an open-weight model you host yourself. Models are assigned per task and can be changed without touching the agent's job, tools or permissions.
Where does our data go?
In a customer-hosted deployment, nowhere outside your tenant. Prompts, documents and tool output travel over private endpoints to the model provider you choose. JDK works through your access controls and sees only what you allow.
We already have Copilot or Agentforce. Why this?
Keep them. Copilot assists a person inside Microsoft apps. Agentforce runs workflows inside Salesforce. An agent team runs work that crosses systems, on your infrastructure, under your approval rules. Where one of those products already covers a job, the kill list from the Discovery Sprint says so and we do not build it.
How long until something is running?
Each step has a written scope and a fixed fee before it starts: AI Work Discovery Sprint, then First Agent Pilot, then Secure Agent Foundation. The Sprint ends with a ranked backlog and a kill list, and the pilot runs one agent on one real workflow measured against your current baseline. Timelines for your workflow come out of the Sprint, not a brochure.
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