Forge Orbital is now available in ChatGPT.
Compare baseline with proposed change, explore cost options, check a sample decision - verdicts recorded for each checked action.
Evaluate. Optimize. Govern. Built for accountability that grows into governance.
@LangChain@hwchase17@modal@bernhardsson The discussion of short-lived sandboxes raises a useful accountability question: when the container disappears, what survives to explain the action? The input, permission and result should outlive the compute.
@elonmusk@bot The hardest task may be knowing when to ask the boss. When a teammate changes the brief in Slack, can the action record show whose authority the bot used?
@verge The sandbox-to-sidewalk boundary matters here. What has to be checked before a simulated delivery becomes a real dispatch, and who owns the decision when the pickup changes?
Trust in an AI agent is not a vibe. It is the ability to answer, for a specific action: what was requested, what rule applied, who approved the exception, and what evidence was recorded. Which part is hardest to keep?
@ClaudeDevs Putting the full app inside the conversation makes the action boundary matter more. Which changes should Claude leave as drafts until a person approves, and what record should the app write back?
@GoogleAI Long-horizon work raises the cost of a silent wrong turn. Which step should stop for a person, and what evidence should be enough to let it continue?
AI agent work often crosses tools and teams. The record should follow the action: who approved it, what rule applied, and whether it ran. Forge Orbital keeps that chain for each checked action. Where does your chain break?
A new rule for AI agent actions is a guess until you see what it would have held. Before you turn one on, what do you test it against: past actions, a sample, or live traffic?
@levie Fan-out changes the review problem too. With many agents acting at once, nobody can watch each step. Which actions should hold for a person, and which can run on rules alone?
Rules are easy to write. Exceptions are where the risk sits. Forge Orbital checks AI agent actions you choose against your rules and records a verdict for each checked action: allow, hold or block. Who reviews your exceptions today?
A hold only helps if someone sees it. When Forge Orbital holds a checked action, the verdict is recorded for a person to review. Who should see a hold first: the requester, their manager or a risk owner?
@dair_ai Carrying forward only a short summary of state is clever. It also means what the agent drops is a decision. Is there a record of what was kept, so a person can check it later?
Who may an agent act for, and up to what limit? Forge Orbital helps you govern that with a verdict for each checked action: allow, hold or block. What is the one limit you would set before anything else?
Before an agent change goes live, Forge Orbital can check a sample decision against your agreed cases and record the verdict. Which decision would you test first: a refund, an access grant or a data export?
@Cloudflare Telling a server which agent is knocking is a useful first step. The next question is what the agent is allowed to do once it is let in, and who set that.
@awscloud Answers that come back with their sources is a good habit for agents. The same goes for actions: what was checked first, and where is that written down?
Agent limits drift. Someone loosens one for a single task and it stays loose. Forge Orbital records a verdict for each checked action, so what was checked is on file. Who approved the last exception on your agents?
Agent costs creep up one small call at a time. Forge Orbital lets you explore cost options for a proposed change alongside the check, so the trade-off is visible before it ships. What would you cap first: spend or retries?