Resources
Practical guidance for teams building and deploying AI agents in production. No hype, just what works.
The model gets the headlines, but the harness — the system you build around the model — quietly decides what the work costs, whether you can trust it, and whether a mistake turns into a one-off or a lesson.
An agent is just a model (the brain) plus a system prompt (the job) running inside an app (the harness) — and that combination is something anyone can build.
Working with AI is less about vibe coding and more about operating a capable system: setting context, constraints, judgment, and durable workflows so agents can do useful work.
Why the rough first version of AgentShelf mattered: it turned years of Salesforce automation, flow design, and LLM experimentation into a working product direction.
InsightsHow to take a product concept from idea to development-ready backlog using AI-assisted workflows
InsightsLLMs are minds. Agents are workers. Understanding this distinction is the key to deploying AI that actually delivers value.
Browse agents, read developer documentation, or explore what organizational adoption could involve.
We use optional assistant personalization, analytics, and advertising technologies only when you allow them. Necessary site functions remain active. Cookie Policy