LLMs are tools — they generate language and structure data, but they do not understand workflows, goals, or real-world constraints. They’re not autonomous intelligence; they are engines used by agents.
Agents bring reasoning, decision-making, and tool-use. They form the operational layer of AI.
Multi-agent systems take this a step further — breaking down complex problems into specialized micro-agents that work in parallel or in sequence. This enables better reuse, modularity, and resilience.
But agents, even smart ones, still need orchestration. Canvas-based workflows give structure to otherwise unstructured intelligence. They:
Ensure agents work in the right order
Handle errors, missing data, and retries
Define when LLMs should speak and when tasks should end
Provide visibility for product and engineering teams
Crucially, all of this isn’t required for every use case. For simple tasks — like summarizing a text or answering a quick FAQ — an LLM with a prompt may be enough.
