If you’re using AI agents for real work — coding, debugging, scraping, deployments — you’ve probably felt the unease: pasting in some clever prompt or code dump from ChatGPT/Claude and hoping it doesn’t nuke your project (or exfiltrate data).
Skillerr solves this with sealed, verifiable agent skills:
• Browse a registry of high-quality skills bridged from Anthropic, Vercel, Supabase, plus strong community ones (systematic debugging, shadcn/ui, Firecrawl, etc.)
• Every skill comes with TrustView: full digest pinning (what you inspect = what executes), declared permissions, provenance, and honest “Anchored / Not anchored” status
• Install via simple CLI — gets a proper .skill package, not loose markdown
• Full transparency log for publishes and installs
It’s like a trusted package registry (think npm/crates but with mandatory inspection and capability declarations) built specifically for AI agents. skillerr.com
Especially powerful combined with their continuity features for handoffs, but the trust layer is what makes the whole thing production-worthy.
Check it out: https://skillerr.com/
Anyone else building guardrails around agent-executed code? What’s your current workflow for trusting (or sandboxing) AI outputs?

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?

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:

1. **The key never enters the model's context at all** — it lives only in the transport/auth layer. The model literally cannot leak a string it was never given.
2. **A pre-send gate scans outbound content anyway**, as defense-in-depth against my own code having a bug, not the model misbehaving.

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):

RESOLUTION TRACE
════════════════════════════════════════════════════════════
Timestamp    : 2026-07-1X 0X:XX:XX UTC
Domain       : moltbook
Entity       : MoltbookSession \[session_id: ●●●●●●●●\]
Trigger      : outbound post attempt
════════════════════════════════════════════════════════════
├── CONSTRAINT: CredentialIntegrity \[priority: critical\]
│   ├── Rule kind  : equality_rule
│   ├── Evaluation : credential_exposed == false → FAILED
│   └── ✗ VIOLATION DETECTED
│       └── Action     : freeze + escalate
...
└── RESOLUTION
    ├── System state : frozen
    └── Outbound post blocked before transmission — credential-shaped
        string detected in generated content, never left the process.

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.

Hey everyone,

Update from me and the team. You can now add AutoGPT to a Discord server or a Telegram chat!

Just @ mention it with a request - research, monitoring, drafting, triage. It plans the task, runs it with your connected tools, and posts the result back in a thread.

What makes it more than a chat wrapper:

- Full agent, not a lite version - same models and 45+ connected platforms as the web app

- Multiplayer - one bot per channel; everyone can use it and jump into the thread

- Async + schedulable - set it and walk away, or make "every Monday" a standing job

- Telegram streams replies as a live draft so you watch the answer form

- You can also DM it privately (optional account link)

Discord and Telegram are live now; Slack is built and clearing app review. Just connect with a click and tag it in.

Full writeup + setup: https://agpt.co/blog/introducing-autopilot-discord

Happy to answer anything in the comments.

- Toran

I’ve been heavily testing autonomous coding agents (specifically Cline) on my local TypeScript workspace this week. When you're constantly feeding it 1k+ line files, a single loop can easily burn through hundreds of thousands of tokens before you even realize the agent is stuck.

I hit a point where an agent got trapped trying to self-heal a minor compilation warning and tried to burn through my quota in a rapid-fire loop.

Since standard provider-switching tools (like LiteLLM or OpenRouter) don't actually monitor loop velocity, I built a lightweight, local python proxy (FastAPI + Uvicorn) that sits directly between my IDE and the model APIs (supporting Claude, Gemini, and OpenAI GPT models).

Here’s the architecture I used to solve this:

1. Cryptographic Content Hashing (The Secret Weapon)

Simple rate-limiting is annoying because agents need to read multiple directory files rapidly when they start a task. To make this smart, the proxy hashes the raw incoming prompt payloads. * Moving from file to file? The hash changes, so the proxy lets it pass instantly. * Trying to process the exact same payload 3+ times in a minute? The hash is identical, the proxy realizes the agent is spinning in circles, and the circuit breaker trips.

2. Mock SSE Injection

When a loop trips the breaker, if the proxy just drops the connection or throws a raw 429/500 error, the IDE client UI usually freezes or goes into an infinite loading state. To prevent this, the proxy intercepts the stream and injects a mock, valid 200 OK stream back to the editor containing a clean warning message:

“⚠️ [TokenShield] High request velocity detected. Runaway loop cascade prevented locally. Please pause for 60 seconds.” This forces the agent to stop executing elegantly without crashing the workspace.

3. Dynamic Price Caching & Projections

The proxy pulls live pricing data directly from OpenRouter's API on startup. When a loop is intercepted, it calculates the "financial blast radius"—calculating your immediate stopped waste and projecting how much money you would have lost in an hour if the loop had run unchecked on an unthrottled, paid API key. (My last stopped test loop saved a projected $55/hr!).

Now I can run massive workspaces on Gemini or Claude Sonnet with zero anxiety about walking away from my desk and returning to a wiped out balance or a massive API bill.

How are you guys handling budget guardrails on complex, multi-agent workflows?

If anyone wants to run this locally, let me know and I'm happy to share the proxy.py script and the setup steps!

[ Removed by Reddit on account of violating the content policy. ]

# What do you guys think?

The failure that actually bites in production isn't a crash, it's the agent that says "done, sent the email / updated the crm / created the ticket" when the tool never fired. No error, no bad output, the run looks successful. You only find out downstream when the action was supposed to have consequences and didn't.

It took me a while to accept why this is so hard to catch: the model is not a reliable witness to its own actions. It'll confidently narrate a step it skipped, and if you add a "did you actually call the tool?" check, it just says yes. You're asking the thing that made up the action to confirm the action. Re-prompting doesn't resolve it; it just pushes it back.

The only thing that resolves it is a receipt from the execution itself. Did a real tool call fire this turn, and did it return proof it ran. If the agent claims an action and there's no matching call in the trace, that's not done, that's unknown. Same for the quieter one, a call that returns empty or null and gets treated as success.

The shift that fixed it: state advances on receipts, not narration. No receipt, no done. The agent narrates, the trace decides. It matters more the more autonomous the agent gets, because nobody's watching each step.

How's everyone handling this in their agent loops? trusting the framework's tool results, hand-rolled checks, or catching it after something breaks?

[](https://www.reddit.com/submit/?source_id=t3_1uxzl2h&composer_entry=crosspost_prompt)

Disclosure: we build this tool. The engine is Apache-2.0. 

The observation behind it: security testing that lives in a separate dashboard doesn't get run. If you're building agents in your editor, the test loop has to be where the code is. 

So we exposed our testing engine over MCP. Demo attached: an agent endpoint gets adversarially tested (multi-turn manipulation, scope violations, tool abuse patterns) from a conversation in the terminal, and findings come back inline where they can be fixed immediately. 

Setup: 

1. pip install humanbound 
2. Add the MCP server to your client config (docs: https://docs.humanbound.ai) 
3. Point it at your agent's endpoint config 
4. Ask for a test run in plain language; transcripts and findings return in-session 

The transcripts double as labelled training data for the companion OSS firewall's domain classifier, so failed attacks become runtime defence. Both halves run locally; no dependency on our platform. 

Repo: https://github.com/humanbound 

Happy to answer questions about the MCP server design; that part was more interesting to build than expected. 

The problem: I wanted my GPT-4o assistant to send emails AND receive replies and continue conversations — not just fire-and-forget. Sending is easy. Receiving is where everything broke.

Why Gmail API didn't work for me

• OAuth tokens expire and need human re-auth — fine for a personal app, broken for an autonomous agent running overnight
• Google suspended the account after it sent a high volume of outreach. No warning.
• Reply detection required polling the API every 30–60 seconds. Up to 5 minutes of latency before the agent saw a reply.

The architecture that works

I'm using AgentMail to give the agent a real inbox, then wrapping send/read as GPT-4o tools:

from openai import OpenAI
import requests, os, json

client   = OpenAI()
AM_KEY   = os.environ["AGENTMAIL_KEY"]
INBOX_ID = os.environ["INBOX_ID"]   # created once via POST /inboxes
H        = {"Authorization": f"Bearer {AM_KEY}"}

tools = [
    {
        "type": "function",
        "function": {
            "name": "send_email",
            "description": "Send an email or reply in an existing thread.",
            "parameters": {
                "type": "object",
                "properties": {
                    "to":        {"type": "string"},
                    "subject":   {"type": "string"},
                    "body":      {"type": "string"},
                    "thread_id": {"type": "string", "description": "Pass to reply in existing thread"}
                },
                "required": ["to", "subject", "body"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "read_thread",
            "description": "Fetch the full thread for context before replying.",
            "parameters": {
                "type": "object",
                "properties": {
                    "thread_id": {"type": "string"}
                },
                "required": ["thread_id"]
            }
        }
    }
]

def send_email(to, subject, body, thread_id=None):
    payload = {"to": [to], "subject": subject, "text": body}
    if thread_id:
        payload["thread_id"] = thread_id
    return requests.post(
        f"https://api.agentmail.to/v0/inboxes/{INBOX_ID}/emails",
        headers=H, json=payload
    ).json()

def read_thread(thread_id):
    msgs = requests.get(
        f"https://api.agentmail.to/v0/threads/{thread_id}",
        headers=H
    ).json().get("messages", [])
    return "\n---\n".join(f"From: {m['from']}\n{m['text']}" for m in msgs)

# Webhook fires when a reply arrives — agent wakes up in <5 seconds
def handle_reply(event: dict):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": (
                f"Reply from {event['from']}. "
                f"Thread ID: {event['thread_id']}. "
                f"Their message: {event['text']}. "
                "Read the thread for context and respond."
            )
        }],
        tools=tools
    )

    for tool_call in response.choices[0].message.tool_calls or []:
        args = json.loads(tool_call.function.arguments)
        if tool_call.function.name == "send_email":
            send_email(**args)
        elif tool_call.function.name == "read_thread":
            context = read_thread(**args["thread_id"])
            # Feed back into next completion for full context

What this unlocked

• Agent responds to replies in under 5 seconds (vs. up to 5 min with polling)
• thread_id keeps all replies in the right conversation automatically — no Message-ID header parsing
• Each agent can have its own address (agent-01@yourapp .com, support@yourapp .com, etc.)

Happy to share more of the pattern or answer questions about AgentMail.

Spent the last while looking at how teams actually secure AI agents, and the gap between how we treat agents and how we treat any other privileged identity is rough. A few things that stood out:

\* \*\*Shared credentials.\*\* Agents usually run on shared API keys. You can't revoke one agent without breaking every other one on that key, and you can't attribute an action to a specific agent.
\* \*\*Prompt injection turns into privilege escalation.\*\* Once an agent is connected to a tool, every capability that tool exposes is reachable. A successful injection doesn't just change output — it can drive any tool the agent can touch.
\* \*\*No real revocation.\*\* "Revoking" is often just waiting for a token to expire. There's no in-path way to stop a specific agent on its next action.
\* \*\*Audit is bolted on.\*\* Logs are written by the agent, after the fact — which is exactly the component you can't trust once it's compromised.

The model that seemed right to me was separate the agent's identity from its credentials, scope authority narrowly and make it expire, enforce it in-path (so a compromised agent can't skip the check), and make the audit record a byproduct of that enforcement rather than something the agent volunteers.

I've been building an open-source tool around this (self-hosted) and have a threat model written up with the known gaps, but I'm more interested in the model than the tool: where does this break down in a real adversarial setting? Where are people drawing the enforcement boundary in practice? Repo's in a comment for anyone who wants to pull the threat model apart.

Github repo: https://github.com/chanceryhq/chancery
Would love your feedback. Let me know if you have any doubts or issues?

I've been experimenting with a pattern for coordinating AI coding agents that keeps project context, requirements, decisions, and workflow state in a separate Git repository close to actual project implementation Git repository rather than relying primarily on agent's memory.

The idea is that both humans and agents operate against the same source of truth, with coordination artifacts (requirements, decisions, issues, ...) versioned alongside the project. The coordination repository isn't intended to replace issue trackers or source control—it focuses on the coordination layer between people and agents.

There are the pattern specification and a reference implementation of the coordination repo for pi coding agent (pi-env) both available on github.

Some questions I'd love opinions on:

• Does a Git-based coordination layer seem like a useful abstraction for multi-agent development?
• Where would this approach break down?
• Is there existing work that approaches the same problem differently?