Takeaways
- The debate over what qualifies as an “agent” is less productive than discussing how agentic a system is across a spectrum of autonomy.
- Most business processes involve simple, mostly linear workflows that can be partially agentic, offering low-hanging opportunities for automation.
- Voice-based interfaces are underutilized but valuable, reducing user friction and improving interaction by encouraging spontaneous input.
- Evaluation systems (evals) are crucial for improving agent performance but are often delayed due to perceived complexity. Start with fast, simple ones.
- Speed and technical depth are the strongest predictors of startup success. Companies that understand tooling and iterate quickly can gain disproportionate advantage.
Summary
Andrew Ng advocates for framing AI applications not in binary terms of “agent” or “not an agent” but as lying on a continuum of “agenticness,” allowing for varying levels of autonomy based on application needs. This shift reduces unproductive debates and encourages flexible, task-appropriate design. Many high-value business applications remain simple and linear—comprising step-by-step workflows with occasional branches—suggesting rich ground for partial automation with agentic systems.
Ng emphasizes the need for practical skills among developers: breaking down workflows, applying appropriate levels of autonomy, and using systematic evaluation (evals) to identify and refine weak components. He notes that many teams underutilize evals due to misconceptions about their complexity. Instead, quick, imperfect evals can be iterated on like any other code to great effect.
On voice interfaces, Ng explains that speaking lowers user friction compared to typing and better supports natural, iterative thinking. He identifies latency as the biggest technical challenge in voice-based applications and shares strategies such as “pre-response” prompts and ambient noise to manage user expectations. Despite strong enterprise demand, developer attention to voice remains disproportionately low.
Ng also discusses Multi-Agent Communication Protocol (MCP) as a key development in reducing plumbing work and simplifying data and tool integration for AI systems. He acknowledges that Agent-to-Agent protocols are still in a very early stage and not yet widely viable.
In terms of coding, Ng critiques the term “vibe coding” for trivializing what he views as a deeply intellectual process enabled by AI tools. He encourages everyone, even non-engineers, to learn basic coding to improve their ability to instruct AI systems effectively. AI-assisted coding doesn’t reduce the need for programming skills—it raises expectations for precise communication with machines.
Ng concludes by offering advice to prospective startup founders: execution speed and technical insight are the two greatest determinants of success. While business and marketing skills are important, technical fluency is rarer and more decisive in the fast-evolving AI landscape.
