Advanced AI agents act as domain experts by providing specific, technical questions for vetting suppliers, such as asking for 'e-coating, not matte paint'. This de-risks the manufacturing process for non-expert entrepreneurs.
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True Agentic AI isn't a single, all-powerful bot. It's an orchestrated system of multiple, specialized agents, each performing a single task (e.g., qualifying, booking, analyzing). This 'division of labor,' mirroring software engineering principles, creates a more robust, scalable, and manageable automation pipeline.
The next frontier for AI in product is automating time-consuming but cognitively simple tasks. An AI agent can connect CRM data, customer feedback, and product specs to instantly generate a qualified list of beta testers, compressing a multi-week process into days.
Instead of a generalist AI, LinkedIn built a suite of specialized internal agents for tasks like trust reviews, growth analysis, and user research. These agents are trained on LinkedIn's unique historical data and playbooks, providing critiques and insights impossible for external tools.
Building an AI application is becoming trivial and fast ("under 10 minutes"). The true differentiator and the most difficult part is embedding deep domain knowledge into the prompts. The AI needs to be taught *what* to look for, which requires human expertise in that specific field.
The company developed an AI that conducts highly technical expert network interviews, automating a high-friction manual process. This enables new, scalable content creation like monthly channel checks across dozens of industries—a task too repetitive for human analysts to perform consistently at scale.
Traditional software required deep vertical focus because building unique UIs for each use case was complex. AI agents solve this. Since the interface is primarily a prompt box, a company can serve a broad horizontal market from the beginning without the massive overhead of building distinct, vertical-specific product experiences.
AI tools like LLMs thrive on large, structured datasets. In manufacturing, critical information is often unstructured 'tribal knowledge' in workers' heads. Dirac’s strategy is to first build a software layer that captures and organizes this human expertise, creating the necessary context for AI to then analyze and add value.
Go beyond using AI for simple efficiency gains. Engage with advanced reasoning models as if they were expert business consultants. Ask them deep, strategic questions to fundamentally innovate and reimagine your business, not just incrementally optimize current operations.
The next evolution of enterprise AI isn't conversational chatbots but "agentic" systems that act as augmented digital labor. These agents perform complex, multi-step tasks from natural language commands, such as creating a training quiz from a 700-page technical document.
The most valuable AI systems are built by people with deep knowledge in a specific field (like pest control or law), not by engineers. This expertise is crucial for identifying the right problems and, more importantly, for creating effective evaluations to ensure the agent performs correctly.