General-purpose assistants may suggest useful steps, but they lack permissions, workflow context, and approval boundaries.
A vague agent promise creates complexity without a measurable job to complete.
Teams cannot tell whether an agent is useful, safe, or improving because there is no evaluation method.
Define the job, tools, data, approvals, and actions the agent is allowed to use.
Connect approved APIs, documents, workflows, or internal systems through controlled interfaces.
Create examples, success criteria, and review routines for usefulness and failure modes.
Document prompts, tools, providers, credentials, data paths, and support expectations.
Gather approved context, summarize findings, and prepare a reviewed output for staff.
Check records, propose next actions, and prepare updates for human approval.
Read approved inputs, extract structured information, and route exceptions to a person.
Agents separate suggestions from actions, require explicit approval for consequential changes, and document model providers, data paths, and failure modes.
We define the bounded task, data access, allowed tools, and actions requiring approval.
We design the agent flow, model choices, memory needs, evaluations, and fallback behavior.
We build the agent, connect tools, and test it against expected and adversarial examples.
We launch with monitoring, evaluation reviews, and clear limits for future expansion.
Yes, but consequential actions require explicit approval and permission design.