Launching today
Kastra
Runtime authorization for Claude, Cursor, Codex and OpenClaw
508 followers
Runtime authorization for Claude, Cursor, Codex and OpenClaw
508 followers
Kastra is the runtime authorization layer for AI agents. It decides what agents can and cannot do before actions execute, enforcing policies with sub-1 ms latency across tools, prompts, inputs, and outputs. Use one control plane to govern agents and policies across Claude Code, Cursor, Codex, OpenClaw, the Anthropic SDK, the OpenAI SDK, and more. Prevent unauthorized tool use, prompt injection, and exposure of sensitive data before they become incidents. Trust the rules, not the agents.
Governing agents before they act instead of auditing after the fact is the shift I've been hoping for.
Kastra
Kastra
@charlos_brat Exactly! And the nicest part is it isn't either/or. Every decision Kastra makes before an action runs is written to a tamper-evident, per-tenant hash-chained decision log, so the "audit after the fact" is just the other face of governing before it. And because it's chained, you can prove nothing was altered or dropped. There's a chain-verification API an auditor runs, not a log file you take on faith.
minimalist phone: reduce your screentime
So basically it prevents to disasters made by AI agents?
Kastra
@busmark_w_nika Exactly! No accidental file deletions, dropped database tables, emails sent to the wrong people, and so on. Kastra stops those actions before they execute, rather than reading about them in the logs afterward.
Kastra
@busmark_w_nika That's exactly the goal. AI agents are incredibly capable, but they can also make high-impact mistakes when given real autonomy. Kastra evaluates every action before it executes and enforces deterministic policies, so instead of discovering a destructive action after it happens, you can prevent it, require approval, or allow it based on your own rules. Think of it as an authorization layer for AI agents; you decide what they're allowed to do before they do it.
I like that this doesn't ask me to trust the agent's judgment at all, it just checks the action against a rule before anything happens. That's the right mental model. Most of the "AI safety" tooling I've evaluated this year still leans on the model behaving well, which never sat right with me.
Kastra
@thomas_jack3 Very accurate! We learned this by testing the product with some of the top enterprises pushing AI products into production. We realized autonomous AI products required a new infra layer and we built Kastra to solve the trust problem in a scalable way.
Finally something that treats agent permissions like a real problem. The sub-1ms latency claim seems legit - I didn't notice any lag in tool calls when I tested it with Claude Code.
Kastra
Kastra
@baharyakupbcr2 Awesome Bahar, glad you had a good experience! Keeping the AI flow fast was a concern for us since day one. Every decision was made around adding this security layer without hurting performance.
Earth.fm
Kastra
@1mirul exactly! The primary goal is to deterministically stop things before they happen. The monitoring and logs are a natural byproduct, since we're already intercepting every request anyway.
Kastra
@1mirul Thanks! That's exactly how we think about it. Monitoring is valuable, but once an action has already happened, you're often in incident response mode. We wanted to give developers a deterministic decision point before execution, so they can safely delegate more work to AI without giving up control.
Runtime authorization for coding agents is something I didnt know I needed till now. Using OpenClaw quite a bit and the permission layer is always something I have to handle manually. Nice solve
Kastra
The "zero implicit trust" framing is really nice. Most agent safety thinking stops at the model level. Most actual work is on how to set actual programmatic hard walls
well done team:)
Kastra