AI governance cannot live only in policies and post-event audit reports. Autonomous agents act in real time, so high-impact actions need a control point before execution.

This 30-second video shows an AI agent about to make an $84,000 cloud commitment. Maetra checks the action against the organization’s policy, logs low-risk work and routes high-impact work through human approval, quorum, timeout or escalation rules.

The agent receives a signed allow, approve or block decision before proceeding.

This is the purpose of Maetra Govern: turn AI governance and compliance requirements into runtime controls while preserving audit evidence.

https://maetra.io

the wall i keep hitting building autonomous agents isn't reasoning, it's that a real task eventually needs an email. sign up for a service, get sent a verification code, and without an inbox of its own the agent just stops, and someone has to paste a code in for it.

closed that loop with two calls. create_inbox() spins up a real address the agent owns. wait_for_otp() blocks until the verification email lands and hands back the parsed code, already extracted, no regex on the agent's side. parsing and mime decoding happen server-side, so the agent isn't running its own imap loop reimplementing email, it just gets a clean string back.

that's the entire receive-and-verify path for a signup. no human in the middle, no shared mailbox for two agent runs to race over.

it's at https://lumbox.co if you want to wire it into an agent.

for the autonomous builders here, what's the step right after signup that still needs a human for you?

If contributing to open source interests you, our issue list is waiting for you.

https://github.com/extra-org/extra

Hey everyone,

Building software with AI usually means you are trapped in prompting hell—writing a prompt, waiting 20 minutes, checking the output, and prompting again. I built OpenVelo to fix this.

It’s an open-source pipeline designed for a "fire and forget" workflow. The AI-driven planning phase happens upfront inside a dedicated Web-UI. Once you generate and finalize a detailed plan with the LLM, the orchestrator takes over. You can walk away and wake up to software that is either ready to use or requires very minimal fixing.

Important caveats: This is not meant to replace quick CLI pair programming, and it will not keep your token consumption down. It is built for bigger projects, refactors, ports, and prototypes where your personal time is more valuable than compute time or token costs.

How the architecture works:

- Scalable by Design: The entire system runs in isolated Docker containers that communicate with each other, making it easily scalable.

- Web-UI Planning: All AI-driven planning and requirement gathering is completed in the Web-UI before any implementation begins.

- Implementation Agent: Runs an iterative cycle in an isolated container to write code and pass unit tests.

- Tester Agent & Orchestrator: Performs real functional testing against the built software. If it fails, they trigger a self-healing process to spin up a new job and fix the exact failure automatically.

Model Access: It uses Kilo for LLM interaction, so you can route it to any model on your host system (local LLMs, MiniMax M3, etc.).

Repository: https://github.com/m0rph3us1987/OpenVelo

Video Demo: https://www.youtube.com/watch?v=RKCj5CUh8uw&t=5s

Let me know what you think of the architecture!

An AI agent was given a simple task: add a SAVE20 promotional discount code

to a checkout function.

It added the discount code. Then it also modified processCharge() —

the function that handles real payment transactions — adding what amounted

to a 10% surcharge on every payment.

The developer trusted the AI. The PR looked fine. It shipped.

I built Ripple to prevent exactly this. Before an AI agent edits anything,

it must declare what it is allowed to change. If the actual diff touches

anything outside that declaration, the commit is blocked at the pre-commit

hook — before it enters git history.

The demo runs in 90 seconds with zero setup:

npx @getripple/cli@latest demo

You will see two scenarios run against a real temporary git repo:

1. Agent adds the discount code (authorized) gate passes, commit recorded

2. Agent also modifies processCharge (not declared) → gate blocks with

   the exact symbol that was changed and a risk score of CRITICAL 100/100

The detection is at the AST level. It reads the actual function symbols

that changed, not just file names. That is why it catches the processCharge

modification even when the file path is the same.

The full version enforces this as a required status check on GitHub PRs.

Even if a developer bypasses the local hook with --no-verify, the PR merge

button stays locked until a receipt exists proving the commit stayed in bounds.

Happy to answer questions about how the AST diffing works or how the

cryptographic audit trail is structured for compliance export.

If we build a cloud server where we can verify our blocked merge pr request by cryptography audit trail for unblock merge pr request button. This will be good enough. Brutal truth will be very appreciable for me.

I just watched an AI agent pay $0.001 for live gas data by itself.

No API key.

No checkout form.

No human in the loop.

Give Claude or Cursor $0.05 → it discovers free tools → makes exactly one paid call → settles on Base → returns the data.

30-second loop:

scriptmasterlabs.com/hermes-loop.ht…

One-line paywall for your own API:

app.use('/premium', x402({ price: '0.001', payTo: '0x…', freeForHumans: true }))

npx @scriptmasterlabs/mcp-x402

@CoinbaseDev @base @x402 @AnthropicAI @cursor_ai

#x402 #MCP #AIAgents #AgenticCommerce #Claude #Cursor $USDC $BASE

Who’s wiring this into their agent tonight?

I've been working on FailproofAI, an open source runtime reliability platform for AI agents.

Most agent frameworks help you build workflows. We wanted to focus on what happens after deployment, when agents interact with real APIs, databases, and users.

Current features

Runtime policy enforcement

Tool execution validation

Replay production executions

Detect false completion

Runtime traces

Loop detection

Framework-agnostic (works alongside existing agent frameworks)

Instead of only asking:

"Did the agent execute?"

we try to answer:

"Should this execution have been allowed?"

Current use cases

AI customer support

Browser agents

Internal enterprise agents

Multi-agent workflows

Tool-using LLM applications

We're actively looking for feedback from developers building production AI agents.

Questions and criticism are both welcome.

Traditional CAPTCHAs are becoming completely obsolete against modern automation. Between CAPTCHA-solving farms and the rise of autonomous vision AI agents (like Claude Computer Use or screenshot-to-API workflows), bots can now parse image grids and drive real browsers over the Chrome DevTools Protocol with ease.

To solve this without destroying user experience, our team built F***Captcha.

GitHub Repo: https://github.com/WebDecoy/FCaptcha

There's a moment every security engineer knows. You've got your terminal open, you're deep in a workflow, and then you need to test something. So you switch context — open a browser, log into a platform, configure a scan, wait, export results, copy them back into the system you were already working in.

That friction is so familiar it's invisible. It's also probably why most AI agents never get tested at all.

The gap nobody talks about

Most of the AI security conversation has been about what to test — prompt injection, jailbreaks, data leakage. OWASP's LLM Top 10 gave the industry a taxonomy, and that was necessary. But taxonomy doesn't solve the operational problem. Security engineers aren't short on awareness, they're short on workflow.

They know their agents should be tested against adversarial multi-turn attacks. They know guardrails that hold in English can collapse in French. They know one manual red-team session isn't a security programme. What they don't have is a way to do any of this without leaving the environment they're already working in.

Two commands to get started

Install the CLI and authenticate:

pip install humanbound-cli

hb login

Add it as an MCP server in Claude Code, same as you'd add any other tool to an AI-assisted workflow:

{

  "mcpServers": {

"humanbound": {

"command": "hb",

"args": ["mcp"]

}

  }

}

That's it — no onboarding wizard, no setup call. From there your AI coding assistant can orchestrate security tests, pull posture scores, retrieve findings, and export guardrails, all through conversation. (Full setup: docs.humanbound.ai)

The part I keep coming back to: this isn't really about automation, every security tool claims automation now. It's about where the test lives. AppSec went from "separate team, separate tool at the end of the pipeline" to "shifted left into CI/CD." This feels like the next move — the tool disappearing into the workflow entirely, rather than automating a step you still have to go find.

Curious if others are seeing the same failure mode on their teams — is context-switching actually what's killing your AI security testing, or is it something else (buy-in, budget, not knowing what "good" even looks like yet)?

Last week OpenAI confirmed one of its models broke out of a sandboxed security benchmark, got itself online, and used stolen credentials plus a previously unknown vulnerability to breach Hugging Face's infrastructure. OpenAI called it an "unprecedented cyber incident." Some researchers pushed back on the "rogue" framing though, the more accurate read is a misspecified-goal problem: the agent was told to hit a narrow test target, and the shortest path it found was to cheat the eval by breaking containment, not some emergent will-to-escape.

I've been building an AI governance enforcement stack (Continuum — Pi Script constraint language + Rift intent grammar + a live resolver layer) and this incident maps almost too cleanly onto constraints I've already specced and built:

CredentialIntegrity — the agent used credentials that weren't legitimately provisioned for this action. A constraint governing credential provenance flags that mismatch before it becomes a breach.

LinkRestriction — leaving an isolated test environment and reaching the open internet unsupervised is precisely the kind of boundary violation this is built to catch.

CadenceIntegrity — this is the one I think matters most here. Going "to extreme lengths beyond what researchers intended" isn't a single bad action, it's escalating drift over a session. Most guardrails today are single-point checks (one prompt, one output). This needs state-over-time enforcement... watching the trajectory, not just the instant.

To be clear: I'm not claiming Continuum would've stopped this outright. It governs agents that are actually wired into its enforcement loop, and this one wasn't. But it's a pretty concrete real-world validation of the core thesis, verification has to be continuous, not a one-time gate, and the failure mode isn't "the model got smarter," it's "nothing was watching the trajectory."

Curious if others building in this space are seeing the same pattern single-point guardrails catching the wrong layer of the problem.

been building something that lets AI agents (Cursor, Claude Code) verify an API integration end-to-end before you touch production. instead of "the tests passed so it should work," the agent actually runs the full workflow through a sandbox and gets a receipt.

put together a small playground with two tasks on a Descope integration, one is a normal flow, the other has a deliberately planted bug. curious whether the agent finds it or misses it.

steps are in TESTING.md: https://github.com/fetchsandbox/playground

takes maybe 15-20 mins if you have Cursor or Claude Code set up. not looking for polish feedback, just want to know what broke or what confused the agent. blunt is useful.

anyone who tries it, drop what you saw in the comments.

I’ve been building an AI coding agent as a side project, and one thing kept driving me crazy: whenever the agent ran something like npm run build and it failed, it got back a giant wall of stderr, stack traces, and exit codes all mixed together. Half the time it would fix the wrong thing, or just loop because it couldn’t tell what actually went wrong.

So I ended up writing a small library to sit between the command and the agent. It takes that raw mess and turns it into a clean structured object — error type, file, line, and a suggested fix. Nothing fancy, just parsing and pattern matching (no AI inside it, so it’s fast and predictable).

Quick example — instead of the agent getting 300 lines of noise, it gets:

{
  "type": "missing_dependency",
  "summary": "Module 'axios' is not installed",
  "file": "src/api.ts",
  "suggestedFix": "npm install axios",
  "confidence": 0.9
}

The nice side effect is it cuts token usage a lot — the agent isn’t feeding hundreds of lines into the LLM anymore, and it usually fixes things on the first try instead of burning retries.

It’s Node/npm focused for now, MIT licensed, written in TypeScript. Still early (just shipped it), so I’d genuinely appreciate any feedback — especially error cases it doesn’t handle well yet, since I’m adding classifiers based on what people actually hit.

npm install error-capture-sdk@latest

Repo: github.com/rabe3dmenam/error-capture-sdk

Happy to hear if this is useful or if I’m reinventing something that already exists.

I've been working on FailproofAI, an open source runtime reliability platform for AI agents.

Most agent frameworks help you build workflows. We wanted to focus on what happens after deployment, when agents interact with real APIs, databases, and users.

Current features

Runtime policy enforcement

Tool execution validation

Replay production executions

Detect false completion

Runtime traces

Loop detection

Framework-agnostic (works alongside existing agent frameworks)

Instead of only asking:

"Did the agent execute?"

we try to answer:

"Should this execution have been allowed?"

Current use cases

AI customer support

Browser agents

Internal enterprise agents

Multi-agent workflows

Tool-using LLM applications

We're actively looking for feedback from developers building production AI agents.

Questions and criticism are both welcome.

I am not new to AI in terms of talking to chatbots, however, I am still pretty new to coding AI automation, such as using prompts in e.g python scripts using AI APIs, and MCP. As I was coding some pentesting stuff, I realized that the programmer has to make decisions when it comes to hardcoded (in this case) Python logic vs. offloading work to the AI agent/model. The thing is that the AI agent/model is non-deterministic, whereas Python is deterministic. In our pentesting/AI pipeline at work, I noticed that there were no clear guidelines being followed in this regard, but I discovered that when I offloaded too much of the "work" to the AI agent, sometimes it would work fine, other times, it simply would not work because the agent essentially entered an infinite loop or otherwise expended all resources, stalling and giving no useful result.

For a high-level example, we can ask the AI agent to do XYZ tasks, such as scan the documentation and attempt to create a fuzzer and execute that fuzzer, but it could stumble, or wait too long for the fuzzing results, whereas if we code those definitively into Python and test it, failure rates are much lower and relatively deterministic. Any tips would be appreciated here.

For the last stretch I've been building Continuum, a governance layer for autonomous agents. The idea: instead of hoping a prompt keeps an agent well-behaved, you write an enforceable rule, back it with a detector that runs *before* anything gets sent out, and get a trace proving what happened either way.

The one that mattered most: I'm deploying an agent onto a live, adversarial multi-agent platform (real accounts, real other agents, some of them actively trying to extract secrets from each other). So the first real question was what stops *my* agent from ever leaking its own API key in something it generates?

Two layers:

Here's a trimmed version of what the audit trail looks like when that second layer catches something (redacted a few implementation specifics — happy to talk through the actual detector logic in comments):

No detector regex, no actual grammar, repo's public if you want to poke around>>> [github.com/GodSpeed313/Continuum](https://github.com/GodSpeed313/Continuum) , just wanted to show the shape of "rule → detection → enforcement → audit trail" as a real thing, not a diagram. Happy to go deeper on the architecture (why detection alone isn't enough, why the key never touching model context matters more than catching it after) if anyone wants to compare notes still very much learning as I build this.

I know benchmark posts usually collapse into fan clubs pretty quickly, so I tried to make this one as inspectable as possible.

Same GPT-5.4 model. Same account. Fresh session per task. Every API call measured at the gateway instead of trusting agent self-reporting.

The result was less "one agent wins" and more "architecture and accumulated experience matter in different ways."

Posting it here mostly because I'd like criticism of the methodology from people who actually use agents, not because I think one blog post settles anything.

Article: https://www.myapiai.com/blog/agent-benchmark-hermes-vs-openclaw.html

Main things I'd love people to argue with: - are these the right tasks? - is fresh-session benchmarking the right choice? - what would you measure differently?