Need recommendation for cloud security courses (aws focused)
I am open to paid and free courses. I am new to cybersecurity. I have some coding background.
I am open to paid and free courses. I am new to cybersecurity. I have some coding background.
| Element | LangChain | OpenAI | Meta | Anthropic |
|---|---|---|---|---|
| Engine (Specification) | ❌ Prompts only | ❌ AGENTS.md only | 🟡 Procedural skills | 🟡 Model-negotiated contracts |
| Transmission (Delivery) | 🟡 Filesystem | 🟡 PRs + workspace | ✅ MCP tool interfaces | ✅ Agent SDK |
| Tool (Generation) | ✅ GPT-5.2 | ✅ Codex | ✅ In-house agent | ✅ Claude Opus |
| Control (Verification) | ❌ Self-verification | ❌ Model reviews model | 🟡 FBDetect (behavioral) | 🟡 Separate evaluator (still a model) |
| Casing (Boundaries) | ❌ None | ❌ Friday slop cleanup | 🟡 Service boundaries | 🟡 Sprint scope |
✅ Present | 🟡 Partial | ❌ Absent
A viability law — independently confirmed by engineering (TRIZ), cybernetics (Beer), and economics (Nalebuff/Brandenburger) — names five elements every viable system must contain. If any element is missing, the system does not survive.
Mapped against the four most advanced published AI agent architectures
I read a recent blog post: When Your AI-Powered RCA Spews Pages of Useless Text
Here is the summary:
AI-powered root cause analysis fails in practice not because models are weak, but because teams underestimate the unglamorous data engineering behind it. Engineers ignore hallucinated RCAs mid-incident. The issue is context: out of the box, models give generic answers, useless when a root cause is buried under symptoms. RCA is exploration, not one-shot retrieval. Just wiring up tools like Datadog isn't enough—without understanding the data, models query nonexistent fields and burn tokens. Accuracy requires pre-incident work: curating scattered data into something an LLM can reason over, and proactively discovering schemas so the agent knows where to look.
My take on this topic:
The article asks how to make AI better at archaeology, never why archaeology is needed. RCA is hard because the system never declared what "correct" meant—intent and invariants lived in engineers' heads, so at 2 a.m. you reconstruct a theory that was never written down. Prevention isn't better RCA; it's externalizing that theory before the incident. Declare the properties that must hold, check them continuously, and a violation announces itself—"this truth broke, here"—instead of surfacing as a symptom to trace. Exploration collapses into a lookup. Caveat: this only covers failures you declared; genuine unknowns still need investigation.