ARTIFICIAL INTELLIGENCE

“The pleasant surprise is how much you can accomplish when you properly harness your agents, and how big companies are leaning in and able to actually get volume done on that basis.” – Nate B Jones – AI News & Strategy Daily

Context of the Quote

This quote from Nate B Jones captures a pivotal moment in the evolution of AI agents within enterprise settings. Delivered in his AI News & Strategy Daily series, it highlights the unexpected productivity gains when organisations implement AI agents correctly. Jones emphasises that major firms like JP Morgan and Walmart are already deploying these systems at scale, achieving high-volume outputs that traditional software cycles could not match^1,2. The core insight is that proper orchestration-combining AI with human oversight-unlocks disproportionate value, countering the hype-driven delays many companies face.

Backstory on Nate B Jones

Nate B Jones is a leading voice in enterprise AI strategy, known for his pragmatic frameworks that guide businesses from AI hype to production deployment. Through his platform natebjones.com and Substack newsletter Nate’s Newsletter, he distils complex AI developments into actionable insights for executives^1,2,7. Jones produces daily video briefings like AI News & Strategy Daily, where he analyses real-world use cases, warns against common pitfalls such as over-reliance on unproven models, and provides custom prompts for rapid agent prototyping^2,4.

His work focuses on bridging the gap between AI potential and enterprise reality. For instance, he critiques the ‘human throttle’-where hesitation and risk aversion limit agent autonomy-and advocates for decision infrastructure like audit logs and reversible processes to build trust³. Jones has documented production AI agents at scale, urging leaders to act swiftly as competitors gain ‘durable advantage’ through accumulated institutional intelligence². His library of use cases spans finance (e.g., JP Morgan’s choreographed workflows) to operations, emphasising that agents excel in ‘level four’ tasks: AI drafts, humans review, then AI proceeds¹. By October 2025, his briefings were already forecasting 2026 as a year of job-by-job AI transformation⁵.

Leading Theorists and the Subject of AI Agents

AI agents-autonomous systems that perceive, reason, act, and learn to achieve goals-represent a shift from passive tools to proactive workflows. Nate B Jones builds on foundational work by key theorists:

• Stuart Russell and Peter Norvig: Pioneers of modern AI, their textbook Artificial Intelligence: A Modern Approach defines rational agents as entities maximising expected utility in dynamic environments. This underpins Jones’s emphasis on structured autonomy over raw intelligence^1,3.
• Andrew Ng: Dubbed the ‘Godfather of AI,’ Ng popularised agentic workflows at Stanford and through Landing AI. He advocates ‘agentic reasoning,’ where AI chains tools and decisions, aligning with Jones’s production playbooks for enterprises like Walmart².
• Yohei Nakajima: Creator of BabyAGI (2023), an early open-source agent framework that demonstrated recursive task decomposition. This inspired Jones’s warnings against hype, stressing expert-designed workflows for complex problems^1,4.
• Anthropic Researchers: Their work on Constitutional AI and agent patterns (e.g., long-running memory) informs Jones’s analyses of scalable agents, as seen in his breakdowns of reliable architectures⁶.

Jones synthesises these ideas into enterprise strategy, arguing that agents are not future tech but ‘production infrastructure now.’ He counters delays by outlining six principles for quick builds (days or weeks), including context-aware prompts and risk-mitigated deployment². This positions him as a practitioner-theorist, translating academic foundations into C-suite playbooks amid the 2025-2026 agent revolution.

Broader Implications for Workflows

Jones’s quote underscores a paradigm shift: AI agents amplify top human talent, making them ‘more fingertippy’ rather than replacing them¹. Big companies succeed by ‘leaning in’-auditing processes, building observability, and iterating fast-yielding volume at scale. For leaders, the message is clear: harness agents properly, or risk irreversible competitive lag^2,3.