An AI agent combines a model with tools and a loop for deciding what to do next. That flexibility makes boundaries essential. We help identify tasks where an agent is appropriate, limit the actions it can take, and define how people review, stop, or recover its work.
Tasks that may benefit from an agent
Gather and organize information
Assist with multi-step research across approved sources, producing a structured summary for a person to review.
Coordinate routine steps
Move a defined task through internal tools, validate required details, and surface exceptions rather than silently improvising.
Help resolve common requests
Retrieve relevant information and prepare a response or action, escalating cases that are sensitive or outside policy.
How we make agent behavior manageable
- Task decomposition — Specify the task, expected inputs, allowed outcomes, and conditions that require a handoff.
- Scoped tools and permissions — Connect only the tools needed and define which actions are read-only, reversible, or approval-gated.
- Evaluation and testing — Exercise realistic tasks, edge cases, and unsafe or ambiguous requests before expanding access.
- Observability and recovery — Record meaningful steps, make failures visible, and provide ways to pause, retry, or route work to a person.
Prototype carefully, then expand
We begin with a workflow map and a constrained prototype. Together we review the agent’s actions and failure cases, then decide whether the use case is suitable for a broader pilot. We plan monitoring and operational ownership as part of the implementation, not as an afterthought.
For sequential tasks that do not need agent decision-making, consider AI automation and workflows. For a conversational entry point, see chatbot development.