Medium Article: https://medium.com/@jsmith0475/ai-in-drug-discovery-is-evolving-are-you-ready-for-agentic-ai-1e2adb51a891

Dr. Smith's article discusses the evolution of AI in drug discovery, contrasting traditional, passive AI with the emerging "Agentic AI." This new form of AI actively executes tasks, learns from mistakes, and makes decisions independently, unlike its predecessors which primarily predict and assist. Agentic AI is portrayed as a transformative force capable of accelerating drug development, optimizing clinical trials, and ultimately saving lives. However, the article also considers the ethical implications, emphasizing the need for AI systems with judgment and moral reasoning. The author argues that companies embracing Agentic AI will lead the future of pharmaceuticals, while those who hesitate will be left behind. The article concludes by stating that the shift to Agentic AI is not just a future possibility, but a present reality with profound implications for the industry.

AI in Drug Discovery is Evolving - Are You Ready for Agentic AI?

Medium Article: https://medium.com/p/c7170ef22460/edit
This article discusses the evolution of AI from simple text generators to more advanced, self-improving systems. It identifies three phases: the era of text generators (2023-2024), the rise of Agentic AI (2024-2025) with thinking tokens and multi-agent systems, and the future of Neuro-Agentic AI (2026+), which emphasizes reflective intelligence and continuous learning. The piece argues that AI's future lies in modular, domain-specific agents that can reason, remember, and refine their decision-making processes. It contrasts current limitations of models like ChatGPT with the potential of future AI systems that act as strategic advisors, offering examples across law, science, and business. The author encourages organizations to invest in memory-driven and self-improving AI or risk being left behind.

The Next AI Evolution: From Text Generation to Reflective, Self-Improving AI

Medium Article: https://medium.com/@jsmith0475/collective-stigmergic-optimization-leveraging-ant-colony-emergent-properties-for-multi-agent-ai-55fa5e80456a

Dr. Smith's article introduces Collective Stigmergic Optimization (CSO), drawing inspiration from ant colonies to enhance multi-agent AI systems. It highlights how simple individual actions and environmental cues in ant colonies lead to complex problem-solving without central control. CSO principles are translated into computational models, showing benefits like scalability, adaptability, and robustness in AI systems. The article discusses real-world applications in traffic management, swarm robotics, and even healthcare billing error correction. It proposes that CSO offers a promising approach to creating more resilient and efficient AI by leveraging distributed environmental interactions. The author notes that the future lies in systematic design methodologies, hybrid approaches, and the exploration of novel application domains.

Collective Stigmergic Optimization: Ant Colony Emergence in Multi-Agentic AI

Medium: https://medium.com/@jsmith0475/we-built-a-10-agent-ai-system-that-monitors-our-225k-project-in-real-time-heres-what-we-learned-1f0de27ca852
"We Built a 10-Agent AI System That Monitors Our $225K Project in Real-Time - Here's What We Learned," written by Dr. Jerry A. Smith, detailing the development and performance of a specialized AI system. This system utilizes ten distinct, collaborating AI agents to continuously monitor a $225,000 consulting project by synthesizing data from multiple sources like email, calendar, budget, and task trackers. The core achievement of ForeSight is its ability to detect complex project risks 7 days earlier than human managers could and dramatically reduce status reporting time from hours to just 4.2 minutes. The author argues that this multi-agent architecture, which relies on parallel execution and inter-agent communication via a Redis message queue, shifts project management from reactive data compilation to proactive strategic decision-making. The article concludes by emphasizing that the collaborative intelligence of specialized agents offers a massive return on investment by saving hundreds of thousands of dollars in manual labor and preventing costly delays or budget overruns.

We Built a 10-Agent AI System That Monitors Our $225K Project in Real-Time - Here's What We Learned

Medium Article: https://medium.com/@jsmith0475/the-math-that-kills-growth-how-we-built-an-ai-system-that-solves-manufacturings-50m-dcdf6e352124
"The Math That Kills Growth" describes a framework called VALORE, a multi-agent AI system designed to solve the Coordination Paradox that stifles growth in scaling manufacturing companies. The author, Dr. Jerry A. Smith, explains that as manufacturing operations grow, coordination complexity increases exponentially, leading to a collapse in decision velocity and massive financial losses, often totaling $15M to $50M annually. The VALORE system replaces manual, email-dependent coordination with specialized AI agents—such as the Scout, Analyst, and Strategist—that communicate and negotiate in real-time to manage disruptions like supply chain shocks or quality failures within seconds. The implementation of this agentic AI is proposed through a three-phase roadmap—Visibility, Assistance, and eventual Autonomy—to build trust in the regulated manufacturing environment gradually. Ultimately, the system aims to transform coordination from a growth-capping liability into a competitive advantage by achieving significant reductions in operational friction and cost.

The Math That Kills Growth - Solving Supply Chain Growth Problems

In-Depth Explorations of Neuroscience-Inspired Architectures Revolutionizing AI.

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