Hexalinks field notes
When Does a Business Need a Custom AI Agent?
A custom agent is worth exploring when the job depends on your workflow, approved information, or controlled system access—and the expected benefit justifies maintaining it.
Practical guidance
A clear place to start.
Describe the gap first
Name the task a general-purpose tool is not handling well. Is the problem inconsistent inputs, missing context, repetitive handoffs, access to a business system, or the need for an approval trail? A vague desire to use AI is not a sufficient project brief.
Check the simpler options
Look at features in the software you already use, a standard form, a template, or a conventional automation. If the work is a stable rule-based transfer, an AI model may add cost and uncertainty without adding much value.
Identify what needs to be specific
A custom workflow can be designed around an approved knowledge source, defined permissions, a required output format, and review checkpoints. Each of those needs to be implemented and tested; calling something an agent does not provide them automatically.
Count the ongoing work
Include integration upkeep, data quality, model usage, security review, staff training, monitoring, and correction handling. Consider who owns the workflow after launch and how a replacement provider could take over.
Write a one-page pilot brief
State the user, task, inputs, allowed actions, prohibited actions, review owner, and acceptance criteria. Use representative examples with permission. Ask a provider to explain how they will test failures as well as successful cases.
Your next step
Bring us a workflow worth improving.
Tell us what your team does today, where the work gets stuck, and what a useful outcome would look like.