AI Agents

AI agents—systems that perceive, decide, and act without human intervention—are transitioning from research labs to production. Anthropic's API with tool use, OpenAI's function calling, and open-source frameworks like LangChain and CrewAI have matured the infrastructure. But building agents that actually work remains brutally hard: task decomposition fails, hallucinations compound over steps, and real-world uncertainty breaks pure language model planning. The winners aren't pure "agent platform" startups; they're domain-specific agentic applications: customer service agents for Zendesk tickets, sales agents for pipeline automation, DevOps agents for infrastructure management. Startups like Retool and Zapier have embedded agents into workflow platforms, while Pure Storage launched StorageGPT to automate storage optimization—narrow domains with measurable outcomes. The next inflection point is multi-agent systems: teams of specialized agents collaborating to solve complex problems. This requires new debugging infrastructure, governance frameworks, and evaluation metrics. The startups that own agent orchestration (the Kubernetes of AI agents) and domain-specific agent libraries will outcompete point-solution competitors. Expect consolidation around agent platforms by 2026.