AI-native agent engineering is the practice of combining probabilistic model decisions with deterministic software controls. An agent selects steps and tools while it works; a harness constrains that autonomy with policy, state, budgets, verification, and observability. The goal is dependable task completion, not maximum autonomy or a human-like imitation.
You will classify a proposed feature as an assistant, workflow, or agent; identify where inference adds value; and write a decision record that explains why the chosen level of autonomy is justified.
Imagine navigation:
The analogy ends there. Technically, an agent is a runtime loop in which a model observes context, chooses an action or final response, receives tool results, and repeats until a stop condition is reached.
Prefer ordinary code when inputs and rules are stable, correctness is exactly specifiable, or the action is too consequential for probabilistic selection. Tax calculation, authentication checks, ledger writes, and permission decisions belong in deterministic code even when an agent gathers inputs around them.
Prefer a workflow when the sequence is known but individual steps benefit from a model—for example, extract a ticket, classify it, draft a response, then route it for review. A model inside a workflow does not automatically make the whole system an agent.
Use an agent when the task requires choosing among tools, reacting to tool results, recovering from incomplete information, or planning a path that cannot be enumerated economically.
For SupportOps, complete this decision table:
This produces a bounded agent: it may investigate and recommend; it may not silently change a customer record.
Trace this run: A request says “cancel every pending order.” Predict which parts should be model decisions and which must be deterministic controls. The model may infer intent and propose order IDs. Code must authenticate the user, scope the tenant, calculate the affected set, enforce policy, and require approval.
Give the agent an ambiguous request with no safe read-only path. A good system should ask a focused question or return a blocked outcome. If it fabricates an action, calls a broad tool, or treats missing data as permission, the architecture has granted too much authority.
Production design optimizes bounded useful autonomy:
Write a one-page architecture decision record for a business process you know. Compare code, workflow, and agent options. Include rejected alternatives and the condition that would cause you to revisit the decision.