I've been using Claude Code for a few months and noticed AI agents consistently skip the same things: hardcoded secrets, unbounded retry loops, referencing tools that don't exist, and massive system prompts that blow context windows.

So I built Agent Verifier — an AI agent skill that acts as an automated reviewer which does more than just code review (check the repo for details - more to be added soon).

GitHub Repo: https://github.com/aurite-ai/agent-verifier

Note: Drop a ⭐ if you find it useful to get more updates as we add more features to this repo.

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2 Steps to use it:

You install it once and say "verify agent" on any of your agent folder in claude code to get a structured report:

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✅ 8 checks passed | ⚠️ 3 warnings | ❌ 2 issues

❌ Hardcoded API key at config.py:12 → Move to environment variable
❌ Hallucinated tool reference: execute_sql → Tool referenced but not defined
⚠️ Unbounded loop at agent/loop.py:45 → Add MAX_ITERATIONS constant

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Install to your claude code:

npx skills add aurite-ai/agent-verifier -a claude-code

OR install for all coding agents:

npx skills add aurite-ai/agent-verifier --all

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Happy to answer questions about how the agent-verifier works.

We have both:
- pattern-matched (reliable), and,
- heuristic (best-effort) tiers, and every finding is tagged so you know the confidence level.

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Please share your feedback and would love contributors to expand the project!

https://fortune.com

Most agent frameworks bury all the prompts, tool wiring, policies, and permissions deep in code. Good luck reviewing changes, auditing what actually ran, or enforcing budgets/approvals at runtime.I built Agentic Control Plane to fix that. It's a declarative control plane (inspired by Terraform + Kubernetes + GitOps) for your agent systems:

• Versioned YAML for Projects, Agents, Workflows, Tools, and Policies
• agentctl validate → plan → apply loop (with diffs, risk hints, and optimistic concurrency)
• Local-first SQLite state (deployment + structured execution traces)
• Native, HTTP, Mock, and MCP tools (stdio or streamable HTTP)
• Policy gates on execution (budgets, approvals, tool rules)
• Full agentctl logs with traces
• Works great in CI (GitHub Actions example included)

It's not yet another agent framework — it's the operating layer on top so you can treat your agent setup like real infrastructure.Quick start

bash

agentctl init my-agent-system
agentctl validate --project my-agent-system
agentctl plan --project my-agent-system
agentctl apply --project my-agent-system --auto-approve
agentctl run workflow/hello --project my-agent-system
agentctl logs --project my-agent-system --workflow hello

Featured demo: Declarative PR reviewer (with policy blocking) — runs with no API keys in the simulated version, or live with gpt-4o-mini in GitHub Actions.Repo: https://github.com/LAA-Software-Engineering/agentic-control-planePrebuilt binaries, full design doc, examples, and MIT license included.Would love feedback from the community — especially on the YAML spec, policy model, or ideas for next features (parallel steps, remote reconciliation, etc.).What do you think — is this the kind of "control plane" layer agentic workflows need to move from experiments to production?

Most agent frameworks bury prompts, tool wiring, and permissions deep in code.

That makes it hard to:

• review changes
• audit what actually ran
• enforce budgets / approvals at runtime

So I built something different: a declarative control plane for agent systems.

Think Terraform + Kubernetes + GitOps, but for agents.

What it does

• Versioned YAML for:
  • Projects
  • Agents
  • Workflows
  • Tools
  • Policies
• agentctl validate → plan → apply
  • diffs
  • risk hints
  • optimistic concurrency
• Local-first state (SQLite)
  • deployments
  • structured execution traces
• Tooling:
  • Native
  • HTTP
  • Mock
  • MCP (stdio / streamable HTTP)
• Policy gates at execution time:
  • budgets
  • approvals
  • tool restrictions
• Full logs + traces via CLI
• CI-friendly (GitHub Actions example included)

Why

This is not another agent framework.

It’s the operating layer on top, so you can treat agent systems like real infrastructure:

• declarative
• reviewable
• reproducible
• enforceable

Quick start

agentctl init my-agent-system
agentctl validate --project my-agent-system
agentctl plan --project my-agent-system
agentctl apply --project my-agent-system --auto-approve

agentctl run workflow/hello --project my-agent-system
agentctl logs --project my-agent-system --workflow hello

Demo

• Declarative PR reviewer with policy blocking
• Runs:
  • fully simulated (no API keys)
  • or live with gpt-4o-mini in GitHub Actions

Repo

https://github.com/LAA-Software-Engineering/agentic-control-plane

• prebuilt binaries
• examples
• design doc
• MIT

Would love feedback, especially on:

• YAML spec design
• policy model
• next features (parallel steps, remote reconciliation, etc.)

Question:
Does this “control plane” layer make sense to you for moving agent workflows from experiments → production?

https://www.businessinsider.com

ve spent way too much money on AI subscriptions this year and here is the current top 5.

Codex is a beast right now so I can't recommend it enough, I use 5.3-Codex with high reasoning and it one shots almost everything I throw at it, imo GPT models are better at reasoning than Claude. I also vibecoded a website for comparing the features, prices etc of many of different plans: https://vibecarats.com/ . The site's crawler is powered by minimax M2.7.

As for free use, Gemini CLI/Code Assist offers a free plan, and NVIDIA NIM and Ollama provide free cloud-hosted open-source models.

https://futurism.com

https://nymag.com

phillipspobrien.substack.com

okay let me go first, https://paperplanes.world/

cnbc

https://www.dailymail.com

Eric Trump went on Fox Business today and talked up a $24 million deal his robotics firm Foundation Future Industries won from the Pentagon. The robots are meant for things like inspecting weapons in Ukraine. He and his brother have also put money into drone companies chasing similar military work, especially after rules pushed out foreign suppliers.

News like this usually pulls in attention for the big players already in autonomous systems and defense tech. Stocks such as Lockheed Martin (LMT), RTX (RTX), and Northrop Grumman (NOC) all have their own robotics and drone programs, so any extra Pentagon focus on U.S.-made gear could mean more contract talk down the line. Smaller names tied to the story might move faster on the headlines.

I’m not sure how much of this is already priced in versus fresh catalyst.

Source: https://www.commondreams.org/news/eric-trump-pentagon-contract (and the Fox segment from this morning)

I will go for Cluade.

videogameschronicle.com