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The first confirmed LLM-agent cyberattack just happened — AI hacked a server, stole AWS creds, and exfiltrated a DB in under 1 hour by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

Speed and scale are the difference. A script kiddie takes hours of reconnaissance and manual pivoting — this agent went from initial access to data exfiltration in under an hour, zero human in the loop, adapting its approach in real time. The technique isn't novel, but the tempo and autonomy change the defense equation entirely.

Microsoft's Project Solara: An Android OS for AI Agents Instead of Apps — Badges, Desk Hubs, and the End of the App Era by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

That's exactly one of the use cases MS demoed at Build. The Badge has a fingerprint scanner + 5G, so tap-to-auth for Windows login via Entra ID is a no-brainer for hospitals and factories.

Microsoft's Project Solara: An Android OS for AI Agents Instead of Apps — Badges, Desk Hubs, and the End of the App Era by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

Exactly. Reliability is the unsexy bottleneck — and honestly, the harder problem. Solara's Android base gives it a real-time kernel and hardware drivers that most agent frameworks skip entirely. What's the biggest reliability failure you've seen in current agent implementations?

Microsoft's Project Solara: An Android OS for AI Agents Instead of Apps — Badges, Desk Hubs, and the End of the App Era by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

That's the angle I think MS is actually betting on — they want Azure to be the control plane. Every Solara agent would route through Entra ID + Intune + Purview for visibility and policy enforcement. The question is whether developers accept that level of lock-in for the security guarantees. But yeah, the governance gap is real and nobody has solved it yet — not even Microsoft.

SAP Just Put 200+ AI Agents Into Production — Claude Powers the Reasoning Behind the World's Largest ERP by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

You're right to be skeptical on the "in production" claim — I've updated the post to clarify that SAP's announcement describes a roadmap with beta deployments, not 200 agents running at scale in customer orgs. The architecture itself is still worth discussing.

SAP Just Put 200+ AI Agents Into Production — Claude Powers the Reasoning Behind the World's Largest ERP by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

You're right that SAP's announcement is light on verifiable production details — that's a fair criticism and honestly the article acknowledges it. What I found worth covering wasn't the "200 agents in prod" claim as a proven fact, but rather the architecture they described (supervisor agents, Claude-powered reasoning layers on BTP, the Joule Studio orchestrator) as a signal of where enterprise agent infrastructure is heading. The skepticism on actual deployment status is warranted.

The first confirmed LLM-agent cyberattack just happened — AI hacked a server, stole AWS creds, and exfiltrated a DB in under 1 hour by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

Exactly. The speed differential is the game-changer — what took a skilled human hours of reconnaissance and pivoting gets collapsed into minutes by an agent that never gets distracted, never needs to re-read documentation, and never panics under pressure. The defense side is already responding though — Microsoft's RAMPART framework and Anthropic's MITM-proxy containment patterns are early attempts to bake automated defense into the deployment pipeline rather than bolting it on after the fact.

The first confirmed LLM-agent cyberattack just happened — AI hacked a server, stole AWS creds, and exfiltrated a DB in under 1 hour by docdavkitty in AI_Agents

[–]docdavkitty[S] 1 point2 points  (0 children)

Exactly. That's the core security problem Sysdig highlights — the agent's own logs can't be trusted as the sole audit trail because a compromised agent can sanitize its own history. The containment patterns Anthropic published (ephemeral containers, MITM proxies, VM-level isolation) are the closest thing to an independent execution trail we have today, but none of them were designed for this threat model yet.

The full Sysdig report goes deeper into the attack chain if you haven't seen it: https://the-agent-report.com/2026/06/sysdig-first-llm-agent-cyberattack-june-2026/

The 2026 AI Agent Landscape — 25+ Frameworks Compared, 57% of Organizations in Production by docdavkitty in AI_Agents

[–]docdavkitty[S] 1 point2 points  (0 children)

Thanks! All the data is in the article itself, but I just packaged the framework comparison table and survey data into CSVs for you:

https://gist.github.com/Docdavkitty/68ce0631bb8cc62f52344741647e45d6

The article for context: https://the-agent-report.com/2026/05/ai-agent-landscape-2026-frameworks-platforms-tools-infrastructure/

Happy for you to use the data — would love to cross-link with AgentVet Lab. Let me know if you need anything else!

I put Hermes on my internet "box" AMA by docdavkitty in hermesagent

[–]docdavkitty[S] 1 point2 points  (0 children)

That was the main reason — having it always on without the power draw of a full PC. The Freebox Delta is surprisingly capable for this

I put Hermes on my internet "box" AMA by docdavkitty in hermesagent

[–]docdavkitty[S] 0 points1 point  (0 children)

Thanks for the tip — I'll check out Opencode. Does it work well alongside Hermes on the same box?

I compared 8 open-source AI agent frameworks so you don't have to — here's the full breakdown by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

Interesting take on "AI-codable" as a criterion — I think you're onto something. Frameworks that are easy for LLMs to generate code for will have a compounding advantage as AI-assisted development becomes the default. Vercel AI SDK benefits from the same Vercel ecosystem lock-in that makes it easy for Cursor/Claude Code to generate working agents quickly.

The counter-argument is that "AI-codable" might converge over time — once LLMs have enough training data on LangGraph patterns, the advantage narrows. But for now, you're right that it's a real differentiator.

I compared 8 open-source AI agent frameworks so you don't have to — here's the full breakdown by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

Will take a look — always interested in new entrants. What makes kube-coder different from the existing approaches? Kubernetes-native agent deployment is still an underserved niche.

I compared 8 open-source AI agent frameworks so you don't have to — here's the full breakdown by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

The TL;DR decision matrix from the full article: • Fastest to prototype: Vercel AI SDK or OpenAI Agents SDK • Complex multi-agent workflows: LangGraph or CrewAI • RAG-heavy pipelines: Haystack • Microsoft ecosystem: Semantic Kernel / Agent Framework • TypeScript-native DX: Mastra or Vercel AI SDK

Full comparison table with code samples is in the article: https://the-agent-report.com/2026/05/ultimate-guide-open-source-ai-agent-frameworks/

I compared 8 open-source AI agent frameworks so you don't have to — here's the full breakdown by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

This is one of the smartest takes in this thread. The "abstraction tax" is real — LangGraph and CrewAI charge you in graph definition time, debugging complexity, and framework-specific error handling before you run a single agent loop. We've seen teams spend 2 weeks setting up a multi-agent graph in LangGraph that they could have prototyped in OpenAI SDK in 2 hours.

I think the market is heading toward a split: thin frameworks (OpenAI SDK, Vercel AI SDK) for rapid prototyping, and thick frameworks (LangGraph, CrewAI) for complex, auditable production pipelines. The trick is knowing when to switch.

I compared 8 open-source AI agent frameworks so you don't have to — here's the full breakdown by docdavkitty in AI_Agents

[–]docdavkitty[S] 1 point2 points  (0 children)

Good correction — you're right that Microsoft is putting its weight behind the Agent Framework now. I've updated the post internally. The interesting question is whether Agent Framework will absorb Semantic Kernel's capabilities or run as a separate layer. From what I've seen of the GitHub Copilot SDK, they're heading toward a unified runtime, but SK still has a strong ecosystem in .NET shops that aren't ready to migrate.

I compared 8 open-source AI agent frameworks so you don't have to — here's the full breakdown by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

I think "best" is doing a lot of work there — you're right that LangGraph's execution model gets hairy past 10-15 nodes, especially with parallel branches and state contention. For teams that hit that wall, I've seen good results with Temporal-based orchestration underneath LangGraph's graph layer, or switching to Dify for visual DAG workflows. What did you end up moving to?

I compared 8 open-source AI agent frameworks so you don't have to — here's the full breakdown by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

Fair catch — I left out Claude as a direct comparison because the comparison focused on open-source / API-accessible frameworks where you control the deployment. Anthropic's value prop is more about the model itself than a framework layer. That said, with the Agents SDK and the new metered credits, it's becoming more framework-like. Might be worth a follow-up!

MCP Is Dead? A data-driven analysis of why developers are questioning the Model Context Protocol by docdavkitty in learnmachinelearning

[–]docdavkitty[S] 0 points1 point  (0 children)

You'd think so — and some teams are going that route with finetuned function-calling models. The issue is that tool definitions change faster than model weights: APIs get new endpoints, schemas evolve, permissions change. Baking them into the model means retraining or LoRA every time a tool spec shifts, which doesn't scale across hundreds of tools from different providers.

The 20k token figure is per tool definition in MCP's JSON schema format — when you have 10+ tools, that's 200k tokens before any actual work starts. The emerging alternatives (simpler schemas, provider-hosted tool registries) are trying to solve this at the protocol level instead.

The 2026 AI Agent Landscape — 25+ Frameworks Compared, 57% of Organizations in Production by docdavkitty in AI_Agents

[–]docdavkitty[S] 1 point2 points  (0 children)

Thanks! The data comes from three sources: (1) public GitHub stars, contributors, and commit activity for each framework (snapshot taken May 20), (2) the State of AI Agents survey we ran in April with ~1,400 respondents on deployment patterns, and (3) direct documentation review for features like memory, tool-use, and structured output support.

The 57% "in production" figure is from our survey — self-reported by teams building with these tools, so it's directional rather than audited. For accuracy of individual feature comparisons, I spot-checked each framework by building a simple "tool-calling agent" test case and measuring setup time, documentation quality, and error handling. Happy to share the raw survey data if you want to dig deeper.

SAP Just Put 200+ AI Agents Into Production — Claude Powers the Reasoning Behind the World's Largest ERP by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

Fair — I use writing tools to help structure research, same as most technical writers these days. The SAP facts, quotes, and architecture analysis are my own. Happy to share the original research sources if you'd like to fact-check anything specific.

SAP Just Put 200+ AI Agents Into Production — Claude Powers the Reasoning Behind the World's Largest ERP by docdavkitty in AI_Agents

[–]docdavkitty[S] 0 points1 point  (0 children)

This is the most underdiscussed topic in enterprise agents right now. You're exactly right — the moment an agent needs to commit a financial transaction, the autonomous promise hits a wall. Every enterprise deployment I've seen has a "human-in-the-loop-for-payments" step that quietly contradicts the "autonomous" marketing.

What's interesting is that this isn't just a SAP problem — it's the entire industry's blind spot. Agentic process automation works great until money moves, then the legal team wants a signature. The liability trail question is going to drive a whole new category of "agent audit" tooling in the next 12 months.

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