Agentic Systems: How to Choose the Right Level of Autonomy
Agentic systems are software in which an AI model helps decide what happens next. "Agentic" isn't a yes-or-no label. It's a scale that runs from a model filling in one step of a fixed process to several agents planning work together.
The useful design question is how much of the decision-making you should hand over. Each level adds flexibility, but it also adds cost and makes failures harder to trace. So choose the lowest level that does the job.
Five levels of agentic systems design
1. Fixed workflow. Your code sets every step, and the model does a task within a step, such as summarizing a document. This is the easiest level to test. Use it for: known processes.
2. Router. The model decides which path a request takes, then fixed logic handles the rest. It can also send simple questions to a cheaper model. Use it for: support triage.
3. Review loop. One model drafts and another checks the draft against written criteria. Always cap the number of rounds, or it may never finish. Use it for: work with a clear quality standard.
4. Single agent. The model chooses its own steps and tools until the goal is met. The path changes from run to run, so testing needs many runs instead of one. Use it for: open-ended tasks that can tolerate some delay.
5. Multiple agents. Specialized agents work together, often under a supervisor. This gives you the most flexibility, along with the highest cost and the hardest debugging. Use it for: work that a single agent has demonstrably failed to handle.
Human approval isn't a separate level. You can add it to any of them, and it matters most where an action is expensive or can't be undone.
How to choose
Ask these questions in order:
- Can you write down the steps? If so, use a fixed workflow.
- Are there only a few possible paths? Use a router.
- Can you define what "good" looks like? Add a review loop.
- Do the steps depend on what the model finds along the way? Use a single agent.
- Has that agent failed because the job is too broad, and can you measure the failure? Only then consider several agents.
Before you move up a level, build a set of test cases from real work. If the simpler version passes, you're finished. If it fails, you'll know exactly what the extra complexity needs to fix.
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