“Which tool should I use?” has a boring answer: the pair you already have. Every working AI employee we have seen is two parts — an agent that reads and remembers, and a workhorse that drafts and builds — sitting on a context layer that knows the company. The tools are interchangeable. The context layer is not.
| Part | Job | Tools we run |
|---|---|---|
| The agent | Always on. Reads the inbox, the reviews, the channels. Classifies, extracts the ask, tallies, flags. Talks to you in Telegram, Slack or WhatsApp. Runs routines. | Hermes Agent, Grok bots |
| The workhorse | Runs on demand. Drafts the reply, the outreach, the investor update, the job description. In a repo, implements the feature and writes the release note. | Claude Code, ChatGPT |
| The context layer | What both of them read: persona files, connected inboxes, a sanitised read-only database view, indexed transcripts, extended memory, plugins. The part almost everyone is missing. | Files you own, in a workspace you control |
At Pocket Clear the agent is Hermes, pointed at the support inbox and the App Store and Google Play review feeds. Every morning it classifies each new message, extracts the ask in one line, appends a row to a tally, and flags lost-data, payment and angry tickets. Once a week it writes a research brief. The full pipeline is here.
A Grok bot does the same job where the conversation lives on X — reading mentions and replies, tallying topics, and talking to you in a channel — and it is the natural choice if that is where your customers already are. Both are agents in the same sense: always on, memory in files you can read, routines on a schedule.
Good at: volume, consistency, never missing a message, turning noise into rows. Bad at: nuance — Hermes misclassifies roughly once a week in our setup — and anything that needs the codebase. Rule we run it under: it never replies to a customer.
The tally and the brief land in the repo, and Claude Code takes it from there: it reads a row, finds the code, implements the change on iOS, Android and the backend, writes the release-note bullet, and drafts the reply to every person who asked — into a drafts/ folder that is itself version-controlled. ChatGPT does the drafting half just as well for teams that live in it, and for non-engineering personas — sales, HR, hiring, the CEO’s investor update — it is often the better fit because that is where those people already work.
Good at: anything where the context is loaded — the draft knows the feature shipped in 2.09 because it can see the commit, and knows the account is on the annual plan because it can query the sanitised view. Bad at: being the always-on reader; it is expensive and slow for that. Rule we run it under: every diff and every draft is reviewed before merge or send.
Already have the tools and still getting generic output?
That is the context problem. We build the layer underneath — per persona, fixed price from $1,000.
Swap Hermes for Grok, or Claude Code for ChatGPT, and the setup still works — because neither tool holds anything important. What holds the value is the layer both of them read: a persona file that says who the teammate is speaking as and what it never says; the inbox it can read; a database view it can query without seeing PII; the meeting transcripts, indexed, so it knows what was decided in March; a tally and a decision log that extend instead of resetting; plugins that pull the current repo, help centre and CRM state. What goes in the context layer, and how to build it is the post most teams should read before picking any tool.
A small business with one inbox needs one persona, one agent, and one flow: Hermes or a Grok bot reading and tallying, Claude Code or ChatGPT drafting, a human sending. A startup with a team, a user database and a repo needs the same stack with a company-wide context layer — one persona per role — and, once the voice is right, the same agent can run a content radar across X, Reddit, Instagram and TikTok.
Whatever the pair, the setup rules are the same: it runs on hardware you control, credentials never sit in a config file, database access is read-only and sanitised, and nothing customer-facing goes out without a person. The security checklist is here, and the packages that install any of this — from $1,000 — are on the AI Employee Setup page.
Use the one that lives where your customers are. Hermes for an email inbox and store-review feeds; a Grok bot where the conversation is on X. Both are always-on agents with memory in files you can read, and both sit on the same context layer.
Claude Code if the work touches a repository — features, release notes, drafts stored in git. ChatGPT is a fine workhorse for the non-engineering personas (sales, HR, hiring, the CEO’s updates) and for teams that already live in it. The context layer is identical underneath.
No. One agent and one workhorse. Small businesses start with one persona and one flow; startups add personas and sources. What you always need is the context layer, which is the part no tool ships with.