AI Agents

Many tasks are not one question but a sequence: look something up, check a rule, update a record, notify someone. Language models can now plan and execute such sequences, which is powerful and also risky. An agent with too much access and too little supervision can take wrong actions quickly and at scale.

Triage of support tickets that need data from several systems, preparation of reports that pull from multiple sources, first line handling of routine requests such as scheduling or status checks, data quality checks across systems, and internal operations that follow a known procedure but have too many steps to automate with simple rules.

Each agent gets a narrow task, a small set of tools we build and control, and permissions that match the task and nothing more. Actions with consequences require a human approval step until the evidence shows it can be relaxed. Every step is logged. We evaluate agents on real scenarios before rollout and keep measuring afterwards. Where a deterministic workflow does the job, we use that instead.

We select a task with a clear procedure and a measurable outcome, define the tools and permissions with the system owners, and build the agent with a test harness of realistic cases. It runs first in a mode where it proposes and a person approves. As accuracy is demonstrated, approval is relaxed for low risk actions. Reporting on what the agent did, and where it needed help, is part of the delivery.

An agent you can supervise, and the record to prove what it did.

• Agent with defined task, controlled tools and scoped permissions
• Approval workflow, audit log and evaluation harness
• Operational reporting on actions, accuracy and cost

Multi step work that used to wait for a person gets done in minutes, within rules you set, with a full record. Your team supervises outcomes instead of performing every step.