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.
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