Optimizing Agentic Task Predictability
AI works best when it’s predictable. This post explores how breaking complex tasks into clear phases—with checkpoints for review—turns AI from a black box into a reliable collaborator. If you want agentic workflows you can actually trust, it starts with how you structure the work.
Why Your Multi-Agent AI System Is Probably Making Things Worse
While 2025 has been dubbed the 'Year of the Agent,' recent research from UC Berkeley and Google DeepMind reveals a counterintuitive reality: adding more agents or compute often degrades system performance. This deep dive explores why 'scaling up' leads to coordination chaos and error amplification, and why the real path to reliable AI lies in smarter workflow design and strict constraints rather than raw power.
Key Breakthroughs in AI Engineering that Every AI Engineer Must Know
Learn the key AI engineering breakthroughs — from Transformers and few-shot learning to RLHF, RAG, optimization techniques, and future AI agents — that every AI engineer should master.
Lessons from ProductCon NYC: Navigating the Future of Product & AI in Digital Services
How AI, agility, and leadership are reshaping product roles: 10 bold takeaways from ProductCon NYC.
Developing AI Agent Application with Azure AI Foundry - Why and How?
Azure AI Foundry powers multimodal AI—from image generation to workflows—streamlining development and accelerating innovation.
Empower Your Team with Databricks: Harness the Power of Data & AI
Unlock the power of Databricks: unify data, analytics, and AI to drive collaboration, scalability, and real business outcomes.
Lead Agents with Prompts
Boost productivity with AI prompt agents—less guesswork, more results. Lead smarter with prompt engineering.
The Engineer's Role in the Age of AI
Engineers must evolve with AI, focusing on system design, oversight, and leveraging AI tools to stay impactful in the age of automation.