Agents are generally defined as autonomous entities. This means they can usually act without supervision to complete a task. This has been groundbreaking but is also slowly being abused. It is also expensive — the lack of human direction always guarantees high token spend.
A partial alternative is agentic workflows. Workflows, like autonomous agents, are goal-oriented, but rather than letting the agent find its way around the maze, workflows can always guarantee predictable results because the rules for failure and success are predefined and require greater human involvement.
While workflows are more precise and generally cost-effective, they may yield worse results, especially for research tasks. This is because agentic workflows require task definitions, and some tasks may require more flexibility to yield the best results.
where do workflows thrive?
If you can predetermine every step and be specific, I suggest you create a workflow. But if things are more open-ended, I suggest you stick to autonomous agents.
The problem is the frontier AI companies have agent features embedded into their chat, while workflows remain more effort. Workflows can be reduced to the infrastructure level of agent memory — another topic I'll be exploring — where a workflow is a snapshot that can be revisited and reiterated to increase agent response and memory capacity.