Workflow model
The job, people, systems, evidence, exceptions, and outcome the system needs to support.
Operational agents with visible control
Bounded agents that research, coordinate, and act across systems while keeping people informed and in control.
An agent is useful when the people responsible can understand what it did, what changed, and where it needs help. We begin with one valuable job, make progress visible, and keep consequential decisions with a person until evidence supports more authority.
Where this can help
Often a good fit
- Operations with variable multi-system work
- Research-heavy requests
- Costly coordination and review
- Products adding action-oriented assistants
Probably not the right fit
- Undefined goals
- Unrestricted consequential tools
- Work with no observable completion state
What the engagement can produce
Integrated capability
A useful interface connected to approved data, tools, permissions, and operating context.
Review and recovery
Visible handoffs, exception handling, evidence, and human decisions where needed.
Learning record
Examples, feedback, and measures that show whether the capability is genuinely helping.
How we work together
Choose the job
Start with a valuable, bounded piece of work instead of a vague AI category.
Connect context
Use the systems, information, and policies people already rely on.
Build with evidence
Keep source material, calculations, and actions inspectable as the capability takes shape.
Learn in use
Review real cases and improve the system with the people responsible for the outcome.
What may be involved technically
- Workflow state
- Tool contracts
- Human review
- Task verification
- Replay and tracing
- Budgets
Questions teams often ask first
How autonomous should it be?
Only as autonomous as reversibility, evidence, and failure cost allow.
Can it work across legacy software?
Often, through existing APIs, database views, files, email, or controlled browser workflows.