In this episode of Crazy Wisdom, Stewart Alsop sits down with Derek Osgood, CEO of DoubleO.ai, to talk about the challenges and opportunities of building with AI agents. The conversation ranges from the shift from deterministic to probabilistic processes, to how humans and LLMs think differently, to why lateral thinking, humor, and creative downtime matter for true intelligence. They also explore the future of knowledge work, the role of context engineering and memory in making agents useful, and the culture of talent, credentials, and hidden gems in Silicon Valley. You can check out Derek’s work at doubleo.ai or connect with him on LinkedIn.

Check out this GPT we trained on the conversation

Timestamps

00:00 Derek Osgood explains what AI agents are, the challenge of reliability and repeatability, and the difference between chat-based and process-based agents.
05:00 Conversation shifts to probabilistic vs deterministic systems, with examples of agents handling messy data like LinkedIn profiles.
10:00 Stewart Alsop and Derek discuss how humans reason compared to LLMs, token vs word prediction, and how language shapes action.
15:00 They question whether chat interfaces are the right UX for AI, weighing structure, consistency, and the persistence of buttons in knowledge work.
20:00 Voice interaction comes up, its sci-fi allure, and why unstructured speech makes it hard without stronger memory and higher-level reasoning.
25:00 Derek unpacks OpenAI’s approach to memory as active context retrieval, context engineering, and why vector databases aren’t the full answer.
30:00 They examine talent wars in AI, credentialism, signaling, and the difference between PhD-level model work and product design for agents.
35:00 Leisure and creativity surface, linking downtime, fantasy, and imagination to better lateral thinking in knowledge work.
40:00 Discussion of asynchronous AI reasoning, longer time horizons, and why extending “thinking time” could change agent behavior.
45:00 Derek shares how Double O orchestrates knowledge work with natural language workflows, making agents act like teammates.
50:00 They close with reflections on re-skilling, learning to work with LLMs, BS detection, and the future of critical thinking with AI.

Key Insights

1. One of the biggest challenges in building AI agents is not just creating them but ensuring their reliability, accuracy, and repeatability. It’s easy to build a demo, but the “last mile” of making an agent perform consistently in the messy, unstructured real world is where the hard problems live.
2. The shift from deterministic software to probabilistic agents reflects the complexity of real-world data and processes. Deterministic systems work only when inputs and outputs are cleanly defined, whereas agents can handle ambiguity, search for missing context, and adapt to different forms of information.
3. Humans and LLMs share similarities in reasoning—both operate like predictive engines—but the difference lies in agency and lateral thinking. Humans can proactively choose what to do without direction and make wild connections across unrelated experiences, something current LLMs still struggle to replicate.
4. Chat interfaces may not be the long-term solution for interacting with AI. While chat offers flexibility, it is too unstructured for many use cases. Derek argues for a hybrid model where structured UI/UX supports repeatable workflows, while chat remains useful as one tool within a broader system.
5. Voice interaction carries promise but faces obstacles. The unstructured nature of spoken input makes it difficult for agents to act reliably without stronger memory, better context retrieval, and a more abstract understanding of goals. True voice-first systems may require progress toward AGI.
6. Much of the magic in AI comes not from the models themselves but from context engineering. Effective systems don’t just rely on vector databases and embeddings—they combine full context, partial context, and memory retrieval to create a more holistic understanding of user goals and history.
7. Beyond the technical, the episode highlights cultural themes: credentialism, hidden talent, and the role of leisure in creativity. Derek critiques Silicon Valley’s obsession with credentials and signaling, noting that true innovation often comes from hidden gem hires and from giving the brain downtime to make unexpected lateral connections that drive creative breakthroughs.