In a recent piece, I explored the growing mismatch between our existing data infrastructure and the demands of emerging AI agents. Since then, I have had the opportunity to speak with some founders and engineering leaders who are tackling this challenge directly. Their work confirms that the rise of agentic AI is not just anContinue reading “Reimagining the Database for AI Agents”
Author Archives: Ben Lorica
Inside the race to build agent-native databases
Subscribe • Previous Issues Reimagining the Database for AI Agents In a recent piece, I explored the growing mismatch between our existing data infrastructure and the demands of emerging AI agents. Since then, I have had the opportunity to speak with some founders and engineering leaders who are tackling this challenge directly. Their work confirms that theContinue reading “Inside the race to build agent-native databases”
Agentic AI Applications: A Field Guide
If you’ve been captivated by demos of agentic AI, you’ve likely also encountered the immense challenge of making them work in production. While demos promise unprecedented capabilities, the path to building reliable, scalable, and cost-effective agents is fraught with challenges. This field guide is for teams navigating that chasm, mapping the terrain of architecture patterns,Continue reading “Agentic AI Applications: A Field Guide”
Agentic AI Applications: A Field Guide
Subscribe • Previous Issues Hard Truths About AI Agents: What Works, What Doesn’t, and Why If you’ve been captivated by demos of agentic AI, you’ve likely also encountered the immense challenge of making them work in production. While demos promise unprecedented capabilities, the path to building reliable, scalable, and cost-effective agents is fraught with challenges. This fieldContinue reading “Agentic AI Applications: A Field Guide”
Autonomous Agents are Here. What Does It Mean for Your Data?
By Ciro Greco and Ben Lorica. A striking factoid emerges from Anthropic’s latest Economic Index report: directive AI usage, where users delegate complete tasks to Claude, has surged from 27% to 39% on Claude.ai in just eight months. Among API customers building production systems, that figure jumps to an overwhelming 77%. This rapid shift fromContinue reading “Autonomous Agents are Here. What Does It Mean for Your Data?”
The Convergence of Data, AI, and Agents: Are You Prepared?
Subscribe • Previous Issues Autonomous Agents are Here. What Does It Mean for Your Data? By Ciro Greco and Ben Lorica. A striking factoid emerges from Anthropic’s latest Economic Index report: directive AI usage, where users delegate complete tasks to Claude, has surged from 27% to 39% on Claude.ai in just eight months. Among API customers buildingContinue reading “The Convergence of Data, AI, and Agents: Are You Prepared?”
Escaping Prototype Purgatory: A Playbook for AI Teams
We’re living through a peculiar moment in AI development. On one hand, the demos are spectacular: agents that reason and plan with apparent ease, models that compose original songs from a text prompt, and research tools that produce detailed reports in minutes. Yet many AI teams find themselves trapped in “prototype purgatory,” where impressive proofs-of-conceptContinue reading “Escaping Prototype Purgatory: A Playbook for AI Teams”
Think Smaller: The Counterintuitive Path to AI Adoption
Subscribe • Previous Issues Escaping Prototype Purgatory: A Playbook for AI Teams We’re living through a peculiar moment in AI development. On one hand, the demos are spectacular: agents that reason and plan with apparent ease, models that compose original songs from a text prompt, and research tools that produce detailed reports in minutes. Yet many AIContinue reading “Think Smaller: The Counterintuitive Path to AI Adoption”
A Better Way to Build and Refine Agents
Modern AI applications have evolved far beyond single models. Many systems orchestrate multiple specialized agents — planners that decompose tasks, extractors that gather data, generators that create content — all coordinating through external tools and APIs. This architectural shift creates a fundamental optimization problem: the entire workflow becomes non-differentiable, making traditional gradient-based training methods impossibleContinue reading “A Better Way to Build and Refine Agents”
Beyond RL: A New Paradigm for Agent Optimization
Subscribe • Previous Issues A Better Way to Build and Refine Agents Modern AI applications have evolved far beyond single models. Many systems orchestrate multiple specialized agents — planners that decompose tasks, extractors that gather data, generators that create content — all coordinating through external tools and APIs. This architectural shift creates a fundamental optimization problem: theContinue reading “Beyond RL: A New Paradigm for Agent Optimization”