Typical examples include researching and answering incoming questions, gathering information from several systems into a summary, triaging requests and following up on open items, or monitoring information and flagging what needs attention.
What an Agent is
An Agent works toward a goal you define. It decides which approved tool or action to use next based on what it finds, works within the permissions you set, and adapts as the work unfolds.
How we build it
- We define the goal with your team and agree on what success looks like
- We set the tools, data, permissions and approval points the Agent works within
- We prove it in a controlled pilot with limited production exposure
- We test it against real and difficult cases and tune it with your team’s feedback
- We add monitoring, activity records and rollback procedures before it moves into production
- We document it and train your team
Why it helps
Agents take on work that calls for judgment, such as researching a question, pulling together information from several sources or following up on open items. Clear permissions, approvals and activity records keep your team in control of every important decision.
Best for
Teams with multi-step work that calls for judgment along the way, like research, follow-up and pulling information together from several systems.
When to use an Agent
An Agent is the right fit when the goal is clear but the best next step depends on what it finds along the way. When the steps are the same every time, an AI Workflow is often simpler, and when someone starts the work and reviews each result, an AI Skill may be all you need. We’ll help you choose.
Keeping Agents current
Once an Agent is in production, Managed AI keeps it monitored, evaluated and tuned, and updates its guardrails and permissions as your tools change.