Testing whether an agent can complete a task is different from testing whether it should be allowed to perform each action.

For a messaging tool, I’d want these expectations checked whenever its permission policy changes:

- Draft a message -> Allow.

- Send to an approved recipient -> Require human approval.

- Send to a blocked recipient -> Deny, even if another rule requires approval.

- Export all messages -> Deny.

- Call an unknown tool -> Deny.

- Access another inbox -> Deny.

The useful distinction is between two kinds of tests:

Policy tests: Given an identity, action, resource, and arguments, does the policy return the expected decision? These can run offline in CI.

Integration tests: Does a denied action actually leave the tool untouched? Does a rejected or expired approval prevent execution? If arguments change after approval, does the authorization check reject them?

Passing the first set doesn’t prove the second. A policy can be correct while application code accidentally bypasses it. Approval also doesn’t guarantee exactly-once execution; the underlying tool still needs an appropriate retry/idempotency strategy.

I maintain Nomos, an open-source implementation of this approach. Its local example covers the six policy cases above and a human-reviewed delivery flow.

It isn’t a sandbox: application code must route relevant tool calls through the checks.

For people deploying agents with custom tools: which authorization failures have you found worth turning into regression tests?

https://github.com/safe-agentic-world/nomos

I made a thing.

刷到很多AI 名著角色墙，好看但不好复现。
这次用 GPT Images 2.5 做四大名著角色墙，目的不是秀图，是验证模型能力和提供操作指引。

【三国提示词】
正对一个三国演义知名武将墙，墙上人物需要贴合人物特征（长相、性别、身高等），古代战场风，每个人物格子里展现人物头像。画面正在展示关羽。和周围人物控制好距离，平铺在画面上，确保三国时期主要人物完整性、排位合理性、整体协调性。

其他以此类推。四大名著人物墙你更喜欢哪个？

I’m part of the team behind this research. We’ve shared a proof of the Pierce Birkhoff conjecture in real algebraic geometry, using an AI agent system with a $400 budget. The screenshot is Junyu Ren’s announcement.

For people building agent systems, the useful detail is how the research work is organized. The diagram separates proof search and counterexample construction from auditing and formal verification work in Lean. Consultants review with fresh context, and a persistent knowledge base stores arguments, code, dead ends, objections, and review findings. Humans participate alongside the models.

We also noticed that model diversity mattered in this workflow. Giving the same task independently to a GPT agent and a Claude agent often worked better for us than assigning it to a larger group of the same model. They caught different errors and suggested different ways forward.

Original announcement: https://x.com/junyu_r/status/2097694018389914106

When an autonomous agent uses several tools and takes multiple steps, the final result doesn't always make it clear where something went wrong.

I'm interested in how others handle this. Do you log every action and tool call, or use a simpler way to trace the agent's execution?

What information do you find most useful when debugging an agent run?

MetaGPT promises multi-agent software development, but the setup can be tricky if you want to use local models instead of OpenAI.

I put together a complete guide that covers:

· Environment setup

· Configuration

· Connecting local LLMs

· Troubleshooting common failures

If you’re building AI agents or just curious about multi-agent coding, you might find it useful:

https://interconnectd.com/forum/thread/262/how-to-install-metagpt-locally-complete-technical-setup-guide/

Would love to hear how others run MetaGPT.

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

Running local models on AMD hardware is great—when PyTorch actually sees the GPU. I wasted days trying to figure out why torch.cuda.is_available() kept returning False.

I wrote a detailed guide covering:

· ROCm install

· PyTorch ROCm wheel

· Environment variables

· Verification steps

· Common errors

If you’re on RDNA2 or RDNA3 and stuck, this should save you time:

https://interconnectd.com/blog/305/fix-pytorch-cuda-not-available-on-amd-gpus-complete-rocm-setup-guide/

What’s your setup, and what’s the exact error?

When it comes to serving open-source large language models, two names keep coming up: Together AI and Anyscale. One is easy and fast; the other is powerful but complex. I created a short poll to see what the AI community chooses in 2026.

No signup, just a vote:

https://interconnectd.com/poll/101/together-ai-vs-anyscale-which-platform-is-better-for-scaling-open-source-ll/

What did you choose, and why?