Teams and email first
Users request, approve, and review work in the channels they already use.
Demo Agents vs Production Agents
The hard part starts after the prototype works.
Production agents need identity, permissions, review, logs, exception handling, and a delivery path people will actually use. That operating layer is where most AI pilots stall.
Demo agents skip identity
A demo can call a tool. A production agent needs tenant-owned roles, delegated access, and clear authority before it touches business systems.
Approvals become invisible
Important workflows need drafts, checkpoints, escalation, and a durable record of who reviewed what.
Portals kill adoption
Most employees will not adopt another AI app just to move routine work. The agent has to meet them in Teams and email.
Ops breaks at exceptions
The hard part is not the happy path. It is retries, missing context, blocked permissions, bad inputs, and knowing when to hand back to a person.
Operating Model
A production agent needs surfaces, runtime, and governance.
Agent Aech starts from the way work already moves: requests come through Teams and email, actions run in a controlled runtime, and every capability is bounded by reviewable operating rules.
01
Work surfaces
Teams, email, shared inboxes, and documents become the front door for agent work instead of a new portal.
02
Private runtime
Agents execute inside a controlled runtime with customer-owned keys, local deployment options, and inspectable state.
03
Governance layer
Identity, capability scope, prompts, memory, review policy, and audit trails stay explicit and manageable.
Why Agent Aech
Built for work that has to survive contact with operations.
The useful comparison is not agent versus no agent. It is whether the system can run with your identity model, user habits, review requirements, data boundaries, and maintenance burden.
Vendor risk
Demo-first agent tools
- Impressive prompt demos
- Unclear system authority
- Weak review trail
- Another place users must work
Vendor risk
Portal-first automation
- Low adoption outside power users
- Work still starts in inboxes
- Approvals drift into side channels
- Hard to package for repeatable delivery
Built for production work
Agent Aech operating model
A governed operating layer for agents that enter through Teams and email, execute in a controlled runtime, and preserve reviewable traces when work gets complicated.
- Teams and email interfaces
- Private runtime deployment
- Scoped capabilities and review
- Maintained agent operations
Control Stack
Production-ready by default
Identity
Tenant-owned roles
Policy
Governed capabilities
Runtime
Reusable execution
Review
Inspectable traces
Teams and Email First
The first interface is the one your team already uses.
Requests, approvals, drafts, exceptions, and status updates can stay in Teams, shared inboxes, and workflow email. The agent works behind the scenes while people keep their normal operating habits.
Private Runtime
Run agents close to the business systems they serve.
Agent Aech is built around controlled runtime deployment, customer-owned model accounts, and a path toward local inference where it makes sense. Sensitive workflow state does not need to live in a generic SaaS agent sandbox.
Human Review
Production agents need a review loop, not blind autonomy.
A useful agent can draft, inspect, reconcile, and prepare work. A governed agent also knows what needs approval, what evidence to preserve, and when an exception belongs with a human operator.
Governance Model
Identity, capability scope, and auditability are deployment requirements.
Every agent should have an owner, allowed systems, prompt and memory boundaries, review policy, and traceable activity. These controls are the difference between a pilot and an operational system.
Use-Case Pattern
Start with one workflow that already costs attention every week.
The first deployment should have a clear trigger, known context sources, defined agent actions, review checkpoints, delivery surface, and an audit trail. That pattern can then be reused across functions and customers.
First Workflow Pattern
Start with one workflow that already has operational weight.
- 1
Choose the workflow
Pick repeated work with a real owner, clear trigger, known systems, and enough friction that solving it matters.
- 2
Define the operating contract
Specify context sources, allowed actions, model account, review points, exception paths, and the delivery surface.
- 3
Run, inspect, and maintain
Launch the agent with traces, cost visibility, feedback loops, and ongoing maintenance as the workflow changes.
Pricing Rationale
Pricing follows governed agents and runtime capacity, not seats.
AI workflow value does not come from another user license. It comes from a maintained production agent, a private runtime, and the controls needed to let the right people participate.
No per-user tax
Pricing follows governed agents and runtime capacity so teams can include the right people in the workflow.
Scoped agent builds
Each build maps one production workflow with owners, systems, prompts, review policy, and launch support.
Private runtime capacity
The runtime is priced as operational infrastructure, including managed updates and capacity planning.
Transparent model spend
Customers can bring their own model account so token costs and data settings stay visible and controlled.