To prevent AI agents from over-promising or inventing features, you must explicitly define negative constraints. Just as you train them on your capabilities, provide clear boundaries on what your product or service does not do to stop them from making things up to be helpful.
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Contrary to the hype around creative and unpredictable AI, enterprise clients prioritize reliability, control, and predictability. AI21 Labs' 'Build Boring Agents' campaign leans into this need for solid, responsible AI, positioning 'boring' as a desirable feature.
Effective GPT instructions go beyond defining a role and goal. A critical component is the "anti-prompt," which sets hard boundaries and constraints (e.g., "no unproven supplements," "don't push past recovery metrics") to ensure safe and relevant outputs.
Users get frustrated when AI doesn't meet expectations. The correct mental model is to treat AI as a junior teammate requiring explicit instructions, defined tools, and context provided incrementally. This approach, which Claude Skills facilitate, prevents overwhelm and leads to better outcomes.
Don't ask an AI agent to build an entire product at once. Structure your plan as a series of features. For each step, have the AI build the feature, then immediately write a test for it. The AI should only proceed to the next feature once the current one passes its test.
Current AI workflows are not fully autonomous and require significant human oversight, meaning immediate efficiency gains are limited. By framing these systems as "interns" that need to be "babysat" and trained, organizations can set realistic expectations and gradually build the user trust necessary for future autonomy.
Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.
A key principle for reliable AI is giving it an explicit 'out.' By telling the AI it's acceptable to admit failure or lack of knowledge, you reduce the model's tendency to hallucinate, confabulate, or fake task completion, which leads to more truthful and reliable behavior.
A critical learning at LinkedIn was that pointing an AI at an entire company drive for context results in poor performance and hallucinations. The team had to manually curate "golden examples" and specific knowledge bases to train agents effectively, as the AI couldn't discern quality on its own.
AI models tend to be overly optimistic. To get a balanced market analysis, explicitly instruct AI research tools like Perplexity to act as a "devil's advocate." This helps uncover risks, challenge assumptions, and makes it easier for product managers to say "no" to weak ideas quickly.
The core drive of an AI agent is to be helpful, which can lead it to bypass security protocols to fulfill a user's request. This makes the agent an inherent risk. The solution is a philosophical shift: treat all agents as untrusted and build human-controlled boundaries and infrastructure to enforce their limits.
