AI agents as products

The pitch around “AI agents” has shifted from better prompts to software that keeps state, uses tools, and completes work across sessions. (anthropic.com) (openai.com) Anthropic says Claude Code can read a codebase, edit files, run tests, and deliver committed code, while its newer Agent Software Development Kit exposes the same tools, context management, and permissions systems for custom workflows. (anthropic.com 1) (anthropic.com 2) OpenAI has made a similar push in its Responses application programming interface, which it describes as a unified interface for “agent-like applications” with built-in tools including web search, file search, computer use, code interpreter, and remote Model Context Protocol servers. (openai.com 1) (openai.com 2) A one-off chatbot answers the message in front of it. An agent product stores working memory, recalls prior steps, calls outside systems, and resumes a task later, closer to how a project manager keeps notes, opens apps, and follows up the next day. (openai.com 1) (openai.com 2) That is the frame Ryan Wiggins used in a recent interview about a “Claude Code second brain,” where he described workflows built around application programming interfaces, Model Context Protocol connections, and persistent notes rather than isolated chats. The episode summary lists segments on agent-ready products, MCPs, command-line tools, and using Claude Code after meetings. (youtube.com) (getpodcast.com) The bottleneck is not only model quality. Anthropic’s tool-use post says the goal is to let models discover and execute tools across “hundreds or thousands” of actions, while OpenAI’s memory guides focus on session state, handoffs, and bounded histories that make systems easier to debug and resume. (anthropic.com) (openai.com) Enterprise discussions have moved to the same plumbing. Moor Insights & Strategy said in a February 2026 podcast write-up that as companies move from experiments to production, infrastructure, governance, enterprise resource planning systems, and data quality are becoming the real differentiators. (moorinsightsstrategy.com) Other industry commentary has made the trade-off more explicit: useful agents need continuity and memory, while large companies also demand isolation, access controls, and protection for internal data. SW14 Group argued in January 2026 that this tension between utility and governance is a main reason enterprise rollouts stall at scale. (sw14group.com) The result is a product race around scaffolding as much as models. The companies getting attention are shipping memory layers, permissions, tool routing, and observability so an agent can act less like a clever reply box and more like software a team can leave running. (anthropic.com) (openai.com)