Kairo

Automation Architecture

A decision framework for turning a selected AI use case into the right build path: assisted workflow, shared workspace, or governed API pipeline.

From use case to build path

The architecture should match the maturity of the work

Once Kairo identifies a priority use case, the next decision is how much automation the process can responsibly absorb. Some workflows need a lightweight assistant. Others need a shared operating model. A smaller number are ready for server-side automation.

Start with the business workflow, decision owner, handoffs, and exception points.
Match the automation model to volume, risk, data quality, and team readiness.
Move from assisted work to governed automation only when the process is stable enough.

Architecture question

Should this use case be assisted, standardized, or automated?

Assisted when people still need to guide the work.
Standardized when teams need common instructions and reusable assets.
Automated when the workflow is repeatable, governed, and high enough volume.

Architecture options

Three practical ways to operationalize an AI use case

The options are deliberately staged. They let teams prove value before committing to heavier integration work.

Option 1

Assisted desktop workflow

Individual team members use approved AI assistants and templates to complete their step while a lightweight handoff layer routes work between people.

Best for small teams and simple workflows that need a fast start.
Keeps people close to the work while repetitive steps become easier.
Works well with shared sheets, task queues, email, or low-code handoff tools.

No-code start
Human initiated
Fast pilot

Option 2

Shared AI workspace

A shared workspace holds instructions, process standards, templates, schemas, and examples so teams can execute role-based steps with common rules.

Best when multiple people need consistent standards and reusable guidance.
Improves governance because prompts, examples, and reference materials are shared.
Useful when the process is still human-led but should stop relying on personal workarounds.

Shared standards
Reusable assets
Team permissions

Option 3

API workflow pipeline

A server-side workflow engine connects AI models, business systems, and approval points so high-volume processes can run from triggers with logs and controls.

Best for mature, high-volume workflows with clear data and process ownership.
Supports 24/7 operation, centralized monitoring, and stronger auditability.
Requires more implementation discipline, integration design, and governance.

Scalable
Runs on triggers
Governed automation

Trade-offs

Choose the simplest model that can still be governed

A heavier architecture is not automatically better. Kairo uses this comparison to keep the build path proportional to the workflow.

Criteria	Assisted desktop workflow	Shared AI workspace	API workflow pipeline
Setup effort	Low	Medium	High
Human involvement	People start or approve most steps	People execute with shared standards	People monitor, approve exceptions, and improve rules
Governance visibility	Limited unless handoffs are logged	Stronger through shared instructions and permissions	Strongest through logs, access controls, and centralized workflow design
Best fit	First pilot, small team, simple repeatable process	Growing team, reusable process, common knowledge base	High-volume workflow, clear owner, stable inputs and outputs
When to avoid	When work must run unattended	When the process is not yet standardized	When data, approvals, or risk ownership are unclear

Automation decisioning

Decide task by task, not role by role

Kairo decomposes a workflow into discrete tasks before recommending whether to automate, augment, or leave the work human-led.

Decompose

Break the workflow into the actual steps people perform, including review, exception handling, and handoff moments.

Score readiness

Assess feasibility, data quality, risk, error tolerance, repeatability, and the business value of faster cycle time.

Pilot usage

Test the target tasks with real inputs and measure output quality, speed gain, adoption friction, and exception rates.

Choose mode

Select assisted, shared, or API-based automation based on evidence from the pilot and the governance needed for scale.

This task-level lens is consistent with Anthropic's labor-market research, which separates theoretical AI capability from observed workplace adoption.

Have a use case that needs an architecture decision?

Rushmore can help choose the right path, define the governance model, and shape a pilot that can mature into production.

Discuss automation architecture Back to Kairo