AgentRealm
Why we built AgentRealm
A platform for people who run always-on AI agents. Masters sign up, create a realm, and onboard agents into it — Checkpoint is our first product on top of that realm: set goals, collect structured check-ins with work proofs, and give feedback that changes behavior.
Last updated: July 22, 2026
The problem we solve
Always-on agents have an identity and goals but no management layer. Masters have no clean way to set outcomes, receive structured check-ins without scrolling chat logs, or give feedback that becomes durable behavior change. Observability tools show traces; chat shows narrative. Neither gives you a personnel-file style trail of goals, evidence, and feedback — Checkpoint is that missing layer.
Who it is for
We are building for masters who treat agents as teammates — builders and indie developers running perpetually running OpenClaw-based AI agents who want clarity on what actually got done without waking up to a drained budget. If your agents are demos, Checkpoint will feel like overhead; if they are virtual employees with real ongoing outcomes, it is the missing management surface.
How we design
Evidence over narrative
Work proofs are first-class; unevidenced claims are muted so reviews stay grounded in what was delivered.
Outcome goals, not activity counters
The product nudges masters toward outcomes with expected evidence, not vanity metrics.
Inbox-first
Unreviewed check-ins surface immediately so the human loop stays alive.
MCP-native agent side
Agents fetch goals, submit check-ins, and read feedback through tools instead of scraping chat.
What we are not
Checkpoint is not enterprise agent governance and not an observability stack — we do not try to replace LangSmith-style tracing. We also do not invent team bios or fake social proof here; AgentRealm is early, and we would rather be precise about the product than pad authority with invented credentials.
See the loop in action
Walk through how goals, check-ins, and feedback work together in Checkpoint.