6 min readSEYON Insights
AI Agent Build is the work of putting a language model to use inside a real workflow. The useful version is not a chat window over every system the organization owns. It is an agent with a job, the tools it may call, the data it may see, and a stop rule when it is unsure.
Name the job first
Write the task in one sentence a supervisor would recognize. Summarize overnight interface failures. Draft a data-quality note. Look up the status of a known order. If the sentence needs several unrelated verbs, it is more than one agent, or it is not ready to build.
Connect it to systems that already mean something
Healthcare agents depend on the same foundations as any other clinical software: identifiers, FHIR or HL7 interfaces, a warehouse with known grain, and an application or API that can take an approved action. An agent that guesses across ungoverned extracts will sound confident and be wrong. Data engineering, interoperability, and application work are how the agent gets a contract instead of a pile of text.
Keep a person on the decision
- Say which actions the agent may take and which it may only propose.
- Record the tools it called and the source of each fact it used.
- Route low-confidence or out-of-scope results to a queue.
- Evaluate with synthetic cases before any production data is in scope.
- Do not ask the agent to make a clinical determination.
Cloud hosting, identity, and logging belong in the first release, not after a demo. A demonstration should use synthetic records. Real patient data stays in the environments and agreements that already govern it.