An AI workflow needs an explicit answer to a basic question: which component may turn a suggestion into an external action? Start with that boundary, then decide whether the task needs routing, several model calls or an agent. The procedure below is a proposed design review, not a report of measured performance or an account of a deployed system.
Describe one complete task before dividing it
Write down the input, acceptable result, permitted tools and events that require escalation. Include what should happen when the information is insufficient. Establish a baseline using ordinary application logic or a single model call where appropriate, so that additional orchestration can be assessed against something concrete.
In its December 2024 article, Anthropic distinguishes predefined workflows from agents that direct their own processes and recommends starting with simpler solutions. This is an architecture perspective, not evidence that a particular current model, framework or agent count will suit your workload.
Separate proposals from permission to execute
Define a small set of roles, such as collecting evidence, proposing a change, checking it and executing an approved operation. These can be ordinary functions; they do not all need separate agents. A structured output can make a proposal easier to parse, but does not establish that its content is correct or authorized.
Place permission checks in application code at the execution boundary. Verify the current user, account, resource, allowed operation and relevant version of the record. A model's confidence score or statement that approval exists should not substitute for those checks. Limit each tool to the resources and operations it actually needs.
Design recovery around external effects
Persist enough state to identify the task, accepted inputs, pending action and known outcome. LangGraph documents checkpointers that persist a thread's graph state. A checkpoint can support resuming a workflow, but storing orchestration state does not by itself make a separate API operation safe to repeat.
AWS's discussion of idempotent APIs explains the ambiguity created when a response is lost: the remote operation may already have happened. Where the receiving API supports it, retain a caller-generated request identifier for retries of the same intended operation. Check the provider's contract rather than inventing a new identifier on every attempt.
For operations without that protection, define how to reconcile the remote state or route an uncertain outcome for review. A timeout should not automatically become permission to repeat an irreversible action. Bound retries and distinguish a rejected request, an unavailable service and an unknown result in stored state.
Evaluate a proposed split with representative cases
A useful evaluation set should contain ordinary requests, missing information, conflicting instructions and expected refusals. Add failure cases for stale records, tool timeouts, duplicate events and an attempted operation on the wrong account. Record the intended outcome before running the case, including whether a human decision is required.
Compare task completion, unacceptable actions, intervention needs, latency and cost against the baseline. Treat these as measures to collect, not assumed benefits of a multi-agent design. Add routing or specialist steps only when the observed results and maintenance burden justify them for the intended task.
Make operating limits visible
Set explicit limits for runtime, tool calls and spending, and decide what the user sees when a limit is reached. Review what data each service receives, what is logged and how long it is retained. Calling one component locally does not establish that the complete workflow keeps sensitive information local.
Before release, leave a short operational record: authority boundaries, retry behavior, escalation route, evaluation results and known gaps. That record gives the next engineer something testable when the model, tools or task requirements change.