The interesting shift in AI tooling is the move from producing text to participating in work: finding context, calling tools, and checking results. My first question is still small: what is the system allowed to decide?
For many engineering tasks, the answer can remain narrow. A model might summarize a deployment diff or propose an incident timeline while ordinary code controls the sequence and validates the output.
Choose the control flow deliberately
Anthropic’s architectural distinction is useful: a workflow follows predefined code paths, while an agent dynamically directs its process and tool use. Those are design choices, not maturity levels. More autonomy adds a different failure surface and often more variable cost and latency.
Consider an illustrative release-note assistant. A fixed workflow can fetch an approved diff, extract changed components, generate a draft, and validate that each claimed change has evidence. An open-ended agent that browses repositories and decides what to publish may be solving a broader problem than the team actually has.
Give every step an observable outcome
I would record the input version, selected context, tool results, validation outcome, and final reviewer decision. That lets the team distinguish a retrieval failure from a generation failure or a bad acceptance rule.
Do not use the model’s confidence as the only success criterion. A fluent summary that omits a breaking change can be more damaging than an obvious failure. Put important requirements into checks that the model does not get to waive.
Expand only when the task requires it
If a fixed workflow repeatedly encounters legitimate branching that cannot be specified economically, an agent may be worth evaluating. Give it a bounded tool set, a time or step limit, a spending budget, and an explicit handoff state.
For an incident assistant, a useful first version might read alerts and link runbooks. Permission to restart production workloads is a separate product decision. It should not arrive as a side effect of adding another tool description.
The trend worth following is not a leaderboard of agent frameworks. It is whether a system can complete a useful piece of work with evidence, understandable failure modes, and a recovery path. That is a much more durable engineering target than appearing autonomous.