Choose the implementation layer before choosing your model for an agentic workflow
The choice: design access and oversight before choosing a model
Do not choose the model first. Instead, choose one clearly defined workflow for which you can describe access, decision boundaries, and oversight. NIST specifically notes that organizations must understand the risks of giving AI agents access to diverse datasets, tools, and applications, and apply appropriate identification and authorization controls. This does not prove that every agent needs the same architecture; it does provide a direct reason not to treat access as a post-demo detail. For an initial deployment, this means determining which data and actions are genuinely needed, granting no broader permissions than the task requires, and defining in advance when a human must decide. This makes the implementation layer an explicit product choice rather than a collection of integrations around a model.
Make traceability a design requirement
For applications classified as high risk under EU rules, the European Commission lists risk assessment and mitigation, dataset quality, logging for traceability, and detailed documentation among the obligations before market placement. That source therefore does not describe all business agents or a general legal duty for every team. It does make clear why logging, documentation, and risk assessment matter whenever the applicable context calls for them. Use one verifiable question for each workflow: can an owner later reconstruct which input, authorization, step, and human decision led to an outcome? If the answer is no, the workflow is not yet ready to scale more broadly, no matter how convincing the model appears in a test.
Use a decision register for a single workflow
The practical tool is a small decision register, completed before the agent takes a real action. For each entry, record the task, permitted data and systems, minimum required authorization, owner, human escalation, logging point, and success metric. Link the success metric to the chosen process outcome, such as fully resolved requests within the agreed quality controls, and assess that outcome periodically. For the selected workflow, record: task, data and system access, minimum required authorization, owner, human escalation, logging point, and success metric. This register is a design and control tool, not a guarantee that an agent is safe, compliant, or suitable in every context. The supplied passages concern the identity and authorization of software agents and obligations for high-risk AI; they do not support a universal statement about model quality, returns, vendor lock-in, or the legal status of a specific implementation.
