To avoid failure, launch AI agents with high human control and low agency, such as suggesting actions to an operator. As the agent proves reliable and you collect performance data, you can gradually increase its autonomy. This phased approach minimizes risk and builds user trust.

Purely agentic systems can be unpredictable. A hybrid approach, like OpenAI's Deep Research forcing a clarifying question, inserts a deterministic workflow step (a "speed bump") before unleashing the agent. This mitigates risk, reduces errors, and ensures alignment before costly computation.

To introduce AI into a high-risk environment like legal tech, begin with tasks that don't involve sensitive data, such as automating marketing copy. This approach proves AI's value and builds internal trust, paving the way for future, higher-stakes applications like reviewing client documents.

When addressing AI's 'black box' problem, lawmaker Alex Boris suggests regulators should bypass the philosophical debate over a model's 'intent.' The focus should be on its observable impact. By setting up tests in controlled environments—like telling an AI it will be shut down—you can discover and mitigate dangerous emergent behaviors before release.

Treating AI evaluation like a final exam is a mistake. For critical enterprise systems, evaluations should be embedded at every step of an agent's workflow (e.g., after planning, before action). This is akin to unit testing in classic software development and is essential for building trustworthy, production-ready agents.

To mitigate risks like AI hallucinations and high operational costs, enterprises should first deploy new AI tools internally to support human agents. This "agent-assist" model allows for monitoring, testing, and refinement in a controlled environment before exposing the technology directly to customers.

An FDA-style regulatory model would force AI companies to make a quantitative safety case for their models before deployment. This shifts the burden of proof from regulators to creators, creating powerful financial incentives for labs to invest heavily in safety research, much like pharmaceutical companies invest in clinical trials.

The concept of "human-in-the-loop" is often misapplied. To effectively manage autonomous AI agents, companies must map the agent's entire workflow and insert mandatory human approval at critical decision points, not just as a final check or initial hand-off.

In high-stakes fields like healthcare, the cost of an AI error is immense. Product leaders must prioritize safety, reliability, and the reproducibility of outcomes. A complete audit trail is non-negotiable, as it enables the reversal of incorrect decisions and ensures accountability.