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Autonomous agents are fundamentally different from assistants or chatbots. They are given a goal and a set of tools, and they plan and act without requiring a human to approve each step. This makes them useful for long-horizon tasks like research, lead qualification, or multi-system integrations, but it also means errors can compound before a human notices. Products like NexusGPT and AgentRunner provide infrastructure for building custom agents, while Superagent leans toward ready-to-deploy solutions. When evaluating, the most important factor is how gracefully the agent handles ambiguity and failure. Look for guardrails, human-override features, and transparency into what the agent actually did. Memory architecture affects how well the agent stays on task over long runs. Most tools in this category are priced by compute usage or API calls rather than seats, so cost modeling at scale requires testing with realistic workloads.