Context curation is the single fastest path to AI value—the moment anyone gets this, they stop asking 'what AI tool should I use' and start asking 'what knowledge do I have that isn't written down anywhere.'

Every agentic tool is becoming functionally equivalent—Cursor added agents, Claude added memory systems, OpenAI added workspace agents, and open-source tools like Hermes have similar architectures—which means the tool you pick matters less and less, and what matters much more is the system you build underneath it.

Start with an MVP skill, not a perfect skill, because the first version is always not perfect and kind of wrong, but you use it for a week, notice when it's off and what needs improvement, patch it, and a few weeks in, that skill is writing better first drafts than you'd ever get from starting over each time.

The identity layer answers who you are and what rules you want enforced every single time the agent talks to you, and it is the file your tool reads first before any question you type and before memory—named 'soul' in Open Claw, 'agents.md' in Cursor, 'Claude' in Claude Code, and 'copilot instructions' in GitHub Copilot.

Context files are documents in your workspace that your agent reads on demand and are not part of the prompt you type—they are the library that the agent can reach for when the task needs it, and the effective approach is to have three to five focused files, each on a single page, each covering one thing (team, product, customers, quarter, stakeholders), made dated and fresh, and updated when things change.

A skill is a reusable instruction set for a workflow that you do repeatedly—such as weekly status updates, meeting prep, stakeholder emails, decisions, or memos—and every knowledge worker easily has 20 or 30 of these patterns, each can be written as 'when I say [trigger], do [process] using [sources] and produce output in [format].'

Focus on knowledge work (strategy, communication, operations, decision-making, research, management) rather than coding when building an agentic operating system, because that's where most professionals live and where the agent OS makes the biggest difference.

If you've never proactively written an identity file, your agent starts from zero or from what it was able to collect randomly along the way, and if you don't have a high-intention, regularly updated, just-sufficient amount of information in the identity file, you're missing a huge opportunity to get much more out of the agentic tool.

Nofar's personal agentic system includes a chief of staff agent named Chloe running on Open Claw, plus specialist agents for content, technical building, and platform work, all sharing state to a central hub and the same agentic OS, with the chief of staff seeing the specialists and what they're doing.

The Agent OS free program walks people through the same sequence as Nofar's personal system and is available with a link in show notes and on the AIDB training website.

Don't write the identity file from scratch yourself because you will hate it and quit; instead, open an AI tool and have it interview you by asking 15 questions about how you work, what you want, what you don't want, what frustrates you about AI, and what rules you want enforced, then have the AI draft it, you edit it, and ship a first version that is about 70% right, which you can refine over the next 3 weeks.

Context—what you know—is the single biggest predictor of whether AI gives you generic output or something genuinely useful for your actual situation, because generic AI advice is just one Google search away, but what you cannot get from the public internet is your situation, your roadmap, your org chart, your customer segments, your priorities, and so on.

Context curation is not a project with a beginning or an end but a practice—and every time you catch yourself re-explaining something about your situation to AI, that thing should have been in a context file, so you write it down, add it to the library, and then move on.

Without a skill, you're re-explaining the format every time, pasting the same sources every time, complaining that AI writes in a weird voice, and never teaching it your voice—but a skill fixes that, and if you write it once well, it fires forever.

At minimum, understand how your tool's memory works by asking it directly 'Explain how your memory system works. What do you remember between sessions? What do you forget?'—you need to know what you're working with before you can improve it.

Connections make your agent capable of acting in the real world by reaching real systems such as email, calendar, Slack, Jira, Salesforce, or databases through MCPs (model context protocols), CLI tools, direct APIs, or scripting.

Start with as much read-only access as possible before letting agents write back into systems—let agents only read calendar or inbox first, not send emails or add calendar events—because write access should be added only after you watch the agent behave for a few weeks and have enough trust.

Use the least privileged connections, talk to your IT team if you're connecting any work systems, and don't be the one creating cautionary tales for others in your company.

The worst thing that happens with your agent OS isn't that it fails—it's that it works confidently and wrongly, and you ship the output before you notice, so verification is knowing what to check, and every agent job has its own very quick test.

For verification, do at least three to five checks per task—these are task-specific tests (e.g., for drafted emails check tone matching and factual accuracy; for data analysis check the numbers)—and while these might take under a minute per task, they will save you a lot of grief after.