Contrary to the vision of free-wheeling autonomous agents, most business automation relies on strict Standard Operating Procedures (SOPs). Products like OpenAI's Agent Builder succeed by providing deterministic, node-based workflows that enforce business logic, which is more valuable than pure autonomy.

The founder realized his influencer marketing AI could be fully autonomous when he accidentally left it running without limits. The AI agent negotiated a deal, requested payment info, and agreed to a call on its own. This "bug" demonstrated a level of capability he hadn't intentionally designed, proving the product's end-to-end potential.

Automation tools like "Ralph" loops are only as effective as the plan they execute. Running them with a poorly defined plan will burn through tokens without producing a useful result, effectively wasting money on API calls. A detailed plan is a prerequisite for successful automation.

Organizations must urgently develop policies for AI agents, which take action on a user's behalf. This is not a future problem. Agents are already being integrated into common business tools like ChatGPT, Microsoft Copilot, and Salesforce, creating new risks that existing generative AI policies do not cover.

While seemingly logical, hard budget caps on AI usage are ineffective because they can shut down an agent mid-task, breaking workflows and corrupting data. The superior approach is "governed consumption" through infrastructure, which allows for rate limits and monitoring without compromising the agent's core function.

AI 'agents' that can take actions on your computer—clicking links, copying text—create new security vulnerabilities. These tools, even from major labs, are not fully tested and can be exploited to inject malicious code or perform unauthorized actions, requiring vigilance from IT departments.

Left to interact, AI agents can amplify each other's states to absurd extremes. A minor problem like a missed customer refund can escalate through a feedback loop into a crisis described with nonsensical, apocalyptic language like "empire nuclear payment authority" and "apocalypse task."

The CEO of WorkOS describes AI agents as 'crazy hyperactive interns' that can access all systems and wreak havoc at machine speed. This makes agent-specific security—focusing on authentication, permissions, and safeguards against prompt injection—a massive and urgent challenge for the industry.

The simple "tool calling in a loop" model for agents is deceptive. Without managing context, token-heavy tool calls quickly accumulate, leading to high costs ($1-2 per run), hitting context limits, and performance degradation known as "context rot."