Guide

AI Agent Architecture

An agent combines a defined job, approved context, an AI model, permitted tools, runtime controls, and human review to produce a checkable result.

By AgentShelfUpdated September 29, 2026

Agentshelf Knowledge Guide

A procurement team needs to check supplier paperwork. An agent reads it, checks requirements, and drafts a response for review. Its architecture connects the information, tools, and human decisions behind that work.

Diagram of a request entering a runtime with orchestration, context documents, an AI model, and read-only tools before human review.

Illustrative Northstar intake flow.

Trace one request through the system

This fictional example follows a procurement reviewer asking: “Please check Northstar Supplies’ intake packet and tell me what is missing.” The agent may consult the submitted packet, the current vendor intake checklist, and the current vendor record. In this example, the checklist requires a current insurance certificate, which is absent from the packet.

Scroll horizontally to see all columns.

PartRole in this example
Job and instructionsPrepare a completeness summary and a draft request for missing material; do not approve the supplier.
ContextLimit evidence to the submitted packet, maintained checklist, and current vendor record.
AI modelExtract relevant details from the documents and explain which checklist item is missing.
Tools and permissionsRead the approved materials and prepare text. Do not edit the vendor record or send a message.
WorkflowCheck the packet against the checklist, verify the record, identify gaps, then prepare a draft.
Runtime and accessLimit the environment to assigned procurement materials and authorized reviewers.
ReviewShow the evidence and draft to a procurement reviewer before anyone sends the request or changes a record.

The resulting draft could read:

Northstar Supplies — intake review (draft)
Checked the submitted packet against the current vendor intake checklist and vendor record.

  • Missing: Current insurance certificate.
  • Next step: Request the certificate, then continue the intake review.
  • Status: Awaiting procurement review. No vendor approval or record update has been made.

The request enters as an instruction, approved sources provide evidence, and the permitted action produces a draft. If the checklist and record conflict, or the document is unavailable, the system should flag the uncertainty instead of filling the gap with an assumption.

Choose the simplest flow that fits

Match the approach to the documents:

  • Use rules when packet fields are consistent and the checklist has fixed requirements, such as detecting a missing certificate.
  • Use an AI model step when document formats vary and the system must interpret which evidence matches each requirement. An AI model can help extract and explain that evidence.

Keep AI model interpretations separate from deterministic checks.

Design rule: Add a step only when it has a clear purpose and a way to verify its result.

Sketch the flow before implementation:

  1. Receive the request and load the approved packet and records.
  2. Extract evidence with rules or an AI model, depending on how consistently the information is structured.
  3. Compare the evidence with the checklist.
  4. Prepare a draft.
  5. Human review: A procurement reviewer checks the evidence and draft and decides the next action.

Mark which data crosses each step, what each tool can read or write, and what happens when a check fails.

Write an architecture brief

Turn Northstar’s example into a plan to build and test:

  • User and sources: Name the procurement user, packet, and approved reference sources.
  • Access and output: Specify read-only access and the draft summary.
  • Review and exceptions: Identify the reviewer who decides whether to send the request, and define what happens when evidence is missing or conflicting.

Test the ordinary missing-certificate case, a complete packet, conflicting information, and an unrelated request. Set the quality criteria before adjusting the design.

Next, define a focused agent job. For fundamentals, see what an AI agent is and what an agent harness is.

In AgentShelf

Define controls with the agent harness, connect approved knowledge and tools, and choose configured AI models through the managed gateway. AI model availability and capabilities, and operating records, vary by deployment and configuration.

Your privacy choices

We use optional assistant personalization, analytics, and advertising technologies only when you allow them. Necessary site functions remain active. Cookie Policy