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“AI agents are humans on steroids, not bots on steroids” is a metaphor, not a technical classification. Mickey Boodaei, CEO and co-founder of Transmit Security, uses it to argue that bots automate clicks while agents can pursue goals: they may try different approaches, use tools, and adapt when an attempt fails. The distinction matters because a system acting toward a goal raises questions about whose goal it serves and what it is allowed to do.
What does “humans on steroids, not bots on steroids” mean?
In an opinion essay for The AI Journal, Boodaei contrasts fixed automation with software that can work toward an outcome. His memorable formulation is: “Bots automated clicks. Agents automate goals.” That is his framing of the business implications, not a settled definition dividing all software into two technical categories.
The metaphor captures a practical difference: a scripted bot generally follows instructions laid out in advance, while an agent may select steps dynamically to reach an assigned objective. But agents are still software, not human beings. Their ability to interpret a goal, choose actions, and recover from failure depends on how they are built and constrained.
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How is an AI agent different from a bot or workflow?
Definitions vary, and “agent” does not imply one standard level of independence. NIST describes an agent as software that interacts with its environment and undertakes self-directed actions toward an externally specified goal. Anthropic draws a related distinction between a workflow, which follows predefined code paths, and an agent, which dynamically directs its own processes and tool use.
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| Dimension | Scripted bot or workflow | Agent |
|---|---|---|
| How it proceeds | Typically follows predefined steps or code paths. | May choose and adjust its process dynamically toward a goal. |
| Tools and external systems | Can interact with tools or systems when the script specifies those actions. | May select tools and take actions in its environment, depending on its implementation and permissions. |
| Autonomy | Usually bounded by the steps explicitly set in advance. | Varies; “agent” does not mean unlimited or unsupervised autonomy. |
| What matters for oversight | Whether the scripted actions are appropriate and authorized. | Which objective it serves, what actions it can take, and what checks constrain those actions. |
The table describes a useful contrast, not a strict taxonomy. A workflow can include sophisticated tools, and an agent can be tightly bounded. The important question is how much of the path is predetermined versus chosen by the system.
What can an AI agent do on someone’s behalf?
An agent may be assigned an externally specified objective and use available tools to work toward it. Rather than only executing one fixed sequence, it can potentially select among alternatives and adjust when a route does not work. The precise actions depend on the particular system, the tools connected to it, and its permissions; the label “agent” alone does not establish what it can access or accomplish.
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This is why Boodaei’s framing shifts attention from whether software looks human to whose interests its actions advance. A system acting for a customer, an employee, or an organization may have different objectives and authorization boundaries. Those should be explicit rather than inferred from the agent’s apparent helpfulness.
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Why do identity, authority, and trust matter?
If software can pursue a goal by taking actions in external systems, an organization needs to know what identity the action represents and what authority accompanies it. Boodaei’s essay makes this the central business question: whose objective does the agent represent, and what is it authorized to do? A goal does not itself grant permission to access accounts, change records, or commit an organization to an action.
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- Identity: Establish whose behalf the agent is acting on, and make that context available wherever its actions are evaluated.
- Authority: Limit the tools, data, and actions available to what the assigned task requires.
- Human checks: Decide which actions can proceed automatically and which require review or approval.
- Evaluation: Assess whether the agent handles intended tasks reliably and stays within its boundaries, including when conditions change.
These are practical questions raised by the essay, not a claim that one control design fits every organization. NIST identifies evaluation and testing, standards, interoperability, governance, and risk management among the areas of focus in its agentic AI overview.
What can go wrong when an agent acts?
Anthropic’s guidance on agent design identifies risks including misreading user intent, taking unintended actions, and being manipulated through prompt injection. These are vendor-authored risk descriptions, not a claim that every agent will experience each failure. They illustrate why an agent’s ability to choose steps needs boundaries and oversight.
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- Misread intent: The system may interpret an underspecified request differently from what the user meant.
- Unintended action: A chosen step may have consequences the user did not expect or authorize.
- Prompt injection: Manipulative content encountered during an interaction may try to redirect the agent from its intended task.
Anthropic frames human control, alignment with human values, secure interactions, transparency, and privacy as design principles. It also notes a core tension: “A central tension in agent design is balancing agent autonomy with human oversight.” The appropriate balance depends on the actions at stake; the more consequential an action, the more important it is to consider meaningful review and a clear authorization boundary.
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Is the “human versus bot” distinction technically precise?
No. The phrase is best read as Boodaei’s metaphor for a change from automating fixed actions to pursuing objectives through adaptable action. NIST’s definition and Anthropic’s workflow-versus-agent distinction support a practical contrast between predefined execution and self-directed process, but they do not establish “human-like” and “bot-like” as formal classes.
Agent capability and autonomy exist on a spectrum. A system can be called an agent while remaining narrowly scoped, and a scripted system can perform useful work without being an agent in the more autonomous sense. Evaluate the actual behavior, tools, permissions, and oversight rather than relying on the label.
What should a business ask before deploying an agent?
- What objective is it meant to serve? Specify the user or organizational goal and how conflicting instructions should be handled.
- What can it access and change? Identify its connected tools, data, and permitted actions.
- Where can it act independently? Distinguish routine steps from actions that need human review or approval.
- How will it be evaluated? Test whether it pursues the intended task, stays within its authority, and handles failures safely.
- How are interactions secured and made understandable? Consider prompt-injection defenses, transparency, privacy, and the ability to review what the system did.
- How does it fit organizational governance? Account for risk management, standards, and interoperability rather than treating an agent as an isolated feature.
These questions translate the essay’s identity-and-authority argument into decisions an organization can examine. NIST’s overview treats evaluation, standards, interoperability, governance, and risk management as important areas of work; Anthropic’s guidance emphasizes human control and secure, transparent interactions. Neither source makes the title’s metaphor a universal technical rule.
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