Wallfacer

An AI employee needs the same things a new hire needs. A job. Your tools. Your handbook. A person who reviews the work. Wallfacer is how you hire one. wallfacer.ai

Email an agent and the message becomes work. Subject, body, attachments and thread carry over. The agent replies from its own address in the same thread, and only verified senders on its mail policy can direct it.

Give an agent a login without pasting the password into chat. Store the credential once in Wallfacer and grant it to the agents that need it. The agent signs in by the credential's name, and the value is redacted from every record.

Your agent can click through your web app the way a person would. It reads the page, clicks, fills and submits forms, and screenshots each step, staying signed in the whole way. Every capture gets a permanent link.

Your agents need a company around them. Give them a role, computer, and scoped access. Put checks, handoffs, and review in the handbook. Write the process once. People and agents use it across tasks. Improve it in one place. wallfacer.ai/blog/your-agent…

Claude Managed Agents vs Wallfacer comes down to whose name is on the pull request. Anthropic's GitHub setup for Managed Agents uses your personal access token, so the agent's PRs show up as yours. Each Wallfacer agent has its own GitHub account.

Start with a person reviewing the finished result. For code, that means the pull request, tests, and review record. The person can see whether the parts agree without approving every agent step. Automate more only after one kind of work has enough production history.

Let an agent check its own work. It can reread the brief, run tests, and fix obvious mistakes. Just don't let the same agent decide the change is ready to ship. Self-review improves the work. Approval moves it forward. Give those jobs to different owners.

For three years the question was how fast a model writes code. That's answered, and it moved almost nothing a customer can feel. The question that decides whether the spend pays off: who runs review, tests, routing, and the loop back once the code exists. Read more at wallfacer.ai/blog

The agent writes the diff and stops. It can't decide the change is safe, route it to whoever owns that subsystem, or walk it back when review finds a problem. Your engineers do all of that by hand, for more diffs than before. They aren't reviewing the agent. They're minding it through every step. Read more at wallfacer[dot]ai/blog

You automated code authorship. That was the one step an agent can finish on its own. Review, tests, routing, and the loop back still run at human speed, one change at a time. That's why the token bill climbs while shipped volume stays flat. Read more at wallfacer[dot]ai

We gave two AI agents a GitHub repo and the same playbooks your team follows. One on Claude, one on Codex. Each just used GitHub like any other teammate: Claude wrote the fix, Codex reviewed it, neither treated the other as anything but a peer. Issue to production, end to end.

Claude Code dumps every installed skill into the system prompt… every turn. 87 Google Workspace skills = ~1.2k tokens burned repeatedly. Simple fix: lazy-load them with a stub + index. wallfacer.ai/blog/lazy-loadi…

In a conversation with Ashley Smith at @vermilionfund, @hectorramos, Founder and CEO of Wallfacer, unpacks “agent‑era engineering”: not just coding faster, but redesigning the entire workflow around agent-scale output. wallfacer.ai/blog/vermilion-…