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Primary practice

AI agents built for a specific job, not a general chat window

AI Agent Build is the main SEYON service. An engagement designs an agent around one workflow, connects it to the systems it is allowed to use, and leaves a person accountable for the outcome.

What we build

Agent capabilities

Each build starts from the task. The model is one part. The tools, the data contract, and the review step are the rest.

  • Workflow agents

    Agents that prepare a next step in an operational workflow, such as an exception list, a status summary, or a draft follow-up for a person to accept.

  • Tool and system use

    A defined set of tools the agent may call: an API, a query, a knowledge source, or a ticketing action. Anything outside that list is out of scope.

  • Grounding in trusted data

    Answers tied to a known source, a timestamp, and an identifier. The agent cites the system it used instead of filling a gap from general knowledge.

  • Evaluation before release

    A set of synthetic cases, expected behavior, and failure examples. The agent is scored against that set before it sees a production workflow.

  • Guardrails and review

    Permissions, logging, and a human decision on actions that change a record, a message, or a clinical workflow.

  • Run and improve

    Monitoring for tool failures, off-topic requests, and cost. The prompt, tools, and model version are recorded so a bad run can be explained.

How a build proceeds

From the job to a system people can run

The sequence is the same whether the first agent watches interfaces, drafts an operations note, or answers questions from a curated knowledge set.

  1. 1Define the job and the human decision
  2. 2Select the tools and data the agent may use
  3. 3Design prompts, actions, and stop rules
  4. 4Evaluate on synthetic cases
  5. 5Deploy with identity, logs, and limits
  6. 6Review runs and improve the scope

Running step 1: Define the job and the human decision

Healthcare use case

A care follow-up agent that runs the loop

One healthcare job: read an allowed care-management task, draft the follow-up, let a person send it, and store the patient reply on that same task.

Healthcare use case

Care follow-up agent

Synthetic sample

  1. Step 1

    Worklist

    Open follow-up tasks the agent may see.

  2. Step 2

    Read

    Appointment, care-plan item, and contact method.

  3. Step 3

    Draft

    A follow-up written only from those fields.

  4. Step 4

    Notify

    A care team member releases the message.

  5. Step 5

    Store

    The patient reply returns to the same task.

Running now: Worklist

Open follow-up tasks the agent may see.

The picture moves from the worklist to the stored reply. A care team member sends the message. Clinical decisions stay with qualified people.

Running step 1: Worklist

Worked examples

One agent build applied to two operational jobs

Synthetic sample

The shared pattern

Both jobs use the same shape. The records, the draft, and the system that stores the result change with the job.

  1. 1

    Scope

    One job and the decision a person still owns

  2. 2

    Allow

    Only the records and tools named for that job

  3. 3

    Draft

    A follow-up or a coding note from those fields

  4. 4

    Accept

    A person releases the message or the code

  5. 5

    Store

    The result is written back to the system of record

Use case 1

Care follow-up

Patient data already in care management drives a follow-up. The reply returns to that same record.

  1. 1

    Open the worklist

    Care management supplies the follow-up tasks the agent is allowed to see.

  2. 2

    Read the task

    Appointment, care-plan item, and the contact method already on the record.

  3. 3

    Draft the follow-up

    A message that uses those fields and cites the task it came from.

  4. 4

    Notify the patient

    The care team releases the message through the channel on file.

  5. 5

    Store the reply

    The patient response is written back onto the same care-management task.

Inbound

Patient reply

Stored in

Care management task

The patient reply is stored on the care management task.

Use case 2

Claims coding review

The agent reviews a claim for missing codes and prepares a correction a coder can accept.

  1. 1

    Open the claim

    The submitted claim and the supporting note the agent is allowed to read.

  2. 2

    Compare the record

    Services described in the note against the codes present on the claim.

  3. 3

    Flag the gap

    A missing or incomplete code, with the source field that supports it.

  4. 4

    Propose the correction

    A coding change prepared for a coder to accept, edit, or decline.

  5. 5

    Store the accepted code

    The reviewer-accepted correction is written back onto the claim.

Proposed

Missing code, with its source

Stored on

The accepted claim

The accepted code is stored on the claim.

Both flows are illustrative and use synthetic records. Clinical decisions stay with qualified people. A care team member releases the patient message, and a coder accepts the code, before either result is stored.

Why the other practices matter

Agents are only as solid as the systems under them

Healthcare context, data engineering, interoperability, applications, and cloud support are how an agent gets identifiers, interfaces, and a place to run. They are part of the same company, used when the agent needs them.

Limits that stay in the design

  • The agent proposes or performs only the actions named in the design. Clinical judgment stays with qualified people.
  • Demonstration and evaluation use synthetic data. Real patient information is not a sample input.
  • Access follows the organization identity system and the least privilege that job needs.
  • This service is an engineering capability. It is not a claim of regulatory certification or of a partnership with a model provider.

Describe the job for the first agent

Tell SEYON who uses it, which systems it may touch, and which decision a person must still make.

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