DEV Community: Daniyal

The Black Box Gamble: Why Enterprises Shouldn’t Blindly Trust Big AI

Daniyal — Sun, 02 Aug 2026 19:58:46 +0000

Its interesting to see how the closed weights Frontier LLMs providers place their chatty products, as a solve anything tool, for any business to ever exist. Asking any Business to blindly trust a blackbox from their upcoming vendor (and btw those vendors are burning double digit billions of dollars each year) to have their business decisions influenced through these LLMs and their responses, choices, decisions and facts and figures. The Audacity of these vendors just went straight through the roof and to the moon! HODL!!

Yet the contrast is stark: These Vendors are pitching their closed-weight, proprietary Large Language Models (LLMs) as universal, omnicompetent business engines, while enterprises are being asked to stake their compliance, reputation, and operations on a system they are structurally forbidden from fully understanding.

Just like its forbidden to ask these LLM vendors that how could they procure exabytes of organic training data, in form of millions of e-books and self-scanned printed-only books, while i am not even considering the online storage mediums provided by some social networking platforms which store posts, photos, videos, comments, shares, likes and subscribes for billions of active daily users. however the coprights debate is over, if you wanna avoid data piracy do not harbor your data near the west coast.

The Core Friction Points of “Black Box” Trust

When an enterprise adopts a closed-weight foundational model to make decisions, analyze data, or interact with customers, they are inheriting several specific risks that they cannot directly mitigate:

The “Clever Hans” Effect (Right Output, Wrong Reason): A model might provide a seemingly accurate answer, but base it on spurious correlations in its training data rather than sound logic. In high-stakes environments like healthcare or finance, an AI making decisions based on irrelevant factors is a massive liability.
Irreproducibility and Unpredictability: Because the weights and the specific training data are hidden, businesses cannot predict how the model will react to novel situations or edge cases. If an AI denies a customer a loan or flags a legitimate transaction as fraudulent, the enterprise must explain why — but the vendor’s API won’t provide the underlying reasoning.
Hidden Vulnerabilities (Security and Prompt Injection): Closed systems can harbor unseen vulnerabilities. If a model is susceptible to prompt injection (where malicious inputs force it to bypass safety filters or leak data), the enterprise may not know until a breach occurs. They cannot proactively audit the model’s architecture.
The Shadow AI Crisis: Because these models are often easily accessible via APIs or web interfaces, employees frequently bypass IT to use them (Shadow AI). A recent report noted that up to 65% of AI tools operate without IT approval, creating massive data exposure risks when sensitive corporate information is pasted into consumer-facing prompts.

It is absurd to expect businesses to entrust their autonomy to systems that are fundamentally incapable of explaining their own reasoning or citing their own sources, especially while the vendors themselves fight tooth and nail against basic transparency laws (like X.AI’s recent, failed challenge against California’s transparency act).

An AI Model, that is culturally foreign to your companies values, is infiltrating your permisis at light speeds, manipulating yet convincing, and not just affecting your business processes but it also could contaminate less known details of your companies data.

When we try to understand these LLM Blackboxes we notice they a*re nothing less than some highly optimized machines which generate sequences of tokens that are stochastically plausible* (statistically likely), this statistical likelihood does not inherently equate to logical or factual correctness. While a vendor pitches an LLM as a universal business engine, they are asking enterprises to ignore a few critical flaws:

Stochastic Plausibility vs. Factual Grounding: LLMs generate sequences of tokens that are statistically likely. They do not “know” things. An enterprise relying on this for critical operations is confusing eloquence with accuracy.
The Provenance Black Hole: If an enterprise cannot audit the training data, they cannot audit the model’s biases, its copyright liabilities, or its factual foundations.
The Regulatory Target: By integrating these black boxes, enterprises aren’t just adopting new technology; they are importing the vendor’s unresolved copyright and compliance liabilities directly into their operations.

As long as all this isn’t solved we arent going jobless, nobodies losing it! relax!

The battle over cognitive autonomy versus corporate control

While the EU is busy, enforcing online business to show a cookies banner, or hand over data copies to users for their online accounts. a soft copy! let that sink in……

There’s a glaring asymmetry: regulators will hound a small business for a missing cookies banner or a slightly delayed data subject access request, while exabytes of uncompensated, unconsented data are ingested into multi-billion dollar black boxes under the guise of “fair use” or “text and data mining.”

And new EU policies, as of August 2026, the EU AI Act’s Article 50(2) enters into force, they are now suggesting vendors i.e. General Purpose AI (GPAI) providers now to publicly summarize their training data (using an AI Office template) and respect machine-readable opt-outs.

The solution to all regulatory problems is simple, allow independent third party software providers to intervene , and let them provide automated and digital auditing tools for the modern era, that connects directly over api with the tech vendors and not just oldfashioned human in the loop methods. They could be simple remote control providers for data sovereignty and privacy.

For example, If we leave the cambridge analytica scandal aside and let regulators mandate tech giants to open their APIs, for independent “middleware.” This would fundamentally enable end users to observe and control what content users actually are consuming, what chunk of user data is being used for LLMs Trainings, and vitally, how it affects the behaviour and persona of that User. Instead of relying on a corporation’s black-box algorithm to curate a feed designed for outrage and engagement, a user could employ a third-party application to filter out manipulative behavioral trackers, block algorithmic rabbit holes, and curate a healthy digital diet.

Don’t Let the Company infecting your processes with a Virus, also Sell the Antivirus. Independant Third Party Complaince Enablement Software Providers must be supported by the Regulators, to proctect users data from becoming users digital effigy.

The Performance Analysis skill your AI Agent never knew it needed

Daniyal — Tue, 28 Jul 2026 23:29:41 +0000

This Skill Teaches It the Brendan Gregg Method.

How I packaged the USE Method (Utilization, Saturation, Errors) and the TSA Method (Thread State Analysis) into a vendor-agnostic skill that turns Claude Code, Kimi Code, Codex, or any LLM agent into a methodical performance investigator — complete with root-cause analysis and evidence-backed postmortems.

It's 2 a.m. Latency on your checkout API just went 20×. You open your AI coding agent — Claude Code, Codex, whatever you run — and type: "the server is slow, figure out why."

And the agent does what agents do. It runs top. It runs df -h. Maybe ps aux | grep java. Three commands it happens to know, a shrug dressed up as analysis, and a confident suggestion to "restart the service and see."

There's a name for that, and Brendan Gregg coined it: the Streetlight Anti-Method — searching for the problem only where the light happens to be, because those are the tools you know. Its cousin is the Drunk Man Anti-Method: change things at random until the problem goes away. Most AI agents debug performance exactly like this, because nothing in their context tells them not to.

Meanwhile, the actual discipline of performance analysis has been solved for over a decade. Gregg's USE Method finds roughly 80% of server issues with 5% of the effort. His TSA Method decomposes where every thread's time actually goes. These are procedures — checklists, like the emergency ones in a flight manual — and procedures are exactly what an LLM agent executes beautifully, once you write them down.

So I wrote them down. Meet brendangregg-use-tsa-skill — a vendor-agnostic, MIT-licensed agent skill that gives any AI coding agent the full methodology: systematic performance troubleshooting, root cause analysis, and the postmortem report at the end.

What the Skill Actually Teaches

The core is two complementary methods, plus the supporting toolbox:

USE — Utilization, Saturation, Errors. Resource-oriented. For every resource — CPUs, memory capacity, network interfaces, storage I/O and capacity, controllers, interconnects, and the ones everyone forgets (mutex locks, thread pools, file descriptors, and cgroup/hypervisor limits) — the agent checks three things: how busy is it, how much work is queued behind it, and is it erroring. It's deliberately question-first: instead of starting from whatever metrics a tool happens to show and working backwards, you start from the complete list of questions and find the metrics to answer them. What can't be measured becomes a documented known-unknown instead of an unknown-unknown.

TSA — Thread State Analysis. Thread-oriented, USE's mirror image. For each thread of interest, split time into six exclusive states: Executing, Runnable, Anonymous Paging, Sleeping, Lock, Idle. Then investigate the biggest states first with state-appropriate tools. The killer heuristic: if more than ~10% of time is Runnable or Anonymous Paging, fix those first — latency states can be tuned to zero, and they're often the cheapest wins in all of performance engineering.

Around that core, the skill packs the rest of Gregg's toolbox: off-CPU analysis (why threads block, with stack traces), flame graphs (where on-CPU time goes, visually), workload characterization, drill-down analysis, the RED method for microservices, the 60-second Linux triage (the famous "60,000 milliseconds" checklist), 24 methodologies, 6 anti-methodologies to refuse, and the performance mantras for deciding the fix.

The Procedure Is the Product

Here's what happens when the skill is installed and you say "prod is slow, triage it":

Phase 0  PROBLEM STATEMENT   What changed? Quantify it. Who's affected?
Phase 1  60s TRIAGE          uptime, dmesg, vmstat, mpstat, pidstat,
                             iostat, free, sar ×2, top — errors first
Phase 2  USE SWEEP           every resource: utilization, saturation, errors
Phase 3  TSA SWEEP           every thread: executing/runnable/paging/
                             sleeping/lock/idle
Phase 4  DRILL DOWN          flame graphs, off-CPU stacks, time division
Phase 5  ROOT CAUSE          causal chain, every link evidence-backed
Phase 6  FIX & VERIFY        re-measure with the SAME instruments
Phase 7  REPORT              triage note / RCA / postmortem

The golden rules baked into SKILL.md are what change agent behavior: diagnose before touching anything; installed ≠ working; rule out alternatives explicitly and keep the evidence; quantify everything; the first bottleneck you find may be a problem, not the problem; be candid about what you can't measure; absolute dates in all reports.

A Real Shape of a Real Investigation

The repo includes a worked example postmortem — a fictional-but-realistic incident that shows the expected depth. Short version:

API p99 goes 95ms → 1.9s after a 14:02 deploy. The naive agent says "the deploy broke the code, roll back." The methodical agent works the phases. The 60-second sweep exonerates the host: CPU 48%, no iowait, zero retransmits, clean dmesg. TSA on the application threads shows 61% of time in Runnable — threads ready to run, waiting for a CPU — on a half-idle host. That combination is the signature of exactly one thing: a resource-control limit. Two cat commands later: cpu.max allows 1.5 CPUs, and cpu.stat nr_throttled is climbing 54,000 times per minute. The deploy had doubled the replica count; nobody resized the cgroup quota.

Root cause: not the code, not the host — the quota. Fix: raise the limit. Verified: nr_throttled flat at zero for 72 hours, p99 back to 110ms under a 1.4× load test. And the postmortem records what was ruled out — host CPU, memory, disk, network — each with the command and output that exonerated it.

That last part is what separates an RCA from a guess. The skill's report templates — triage, RCA, and postmortem — demand an evidence table where every claim maps to a command and its output, and an investigation log where dead ends stay on the record.

An Honest Word About the Sources

Everything in the skill is an original distillation — with attribution — of Brendan Gregg's publicly published work: the USE and TSA method pages, the USE Linux checklist and Rosetta Stone, the off-CPU analysis and flame graph pages, the methodology list, and Systems Performance: Enterprise and the Cloud.

One discovery from the research worth sharing: Gregg's OS-specific TSA checklists were never actually published. His tsa-linux.html page has been an "Under construction" stub since 2014 — I verified it against a decade of Wayback Machine snapshots. So the TSA-on-Linux instructions in the repo are a labeled reconstruction, assembled from his TSA method page, the USE-Linux checklist, and his off-CPU work — and marked as such, because a skill that preaches "evidence over guessing" has to hold itself to the same standard.

Install It in 30 Seconds

The skill is a plain directory with a SKILL.md — no build, no dependencies, nothing executable. Pick your agent:

# Claude Code
git clone https://github.com/thecsdoctor/brendangregg-use-tsa-skill \
  ~/.claude/skills/brendangregg-use-tsa

# Kimi Code
git clone https://github.com/thecsdoctor/brendangregg-use-tsa-skill \
  ~/.kimi-code/skills/brendangregg-use-tsa

# OpenAI Codex
git clone https://github.com/thecsdoctor/brendangregg-use-tsa-skill \
  ~/.codex/skills/brendangregg-use-tsa

# Universal (agentskills-compatible agents)
git clone https://github.com/thecsdoctor/brendangregg-use-tsa-skill \
  ~/.agents/skills/brendangregg-use-tsa

OpenClaw discovers it from your workspace skills/ directory; for Gemini CLI, Cursor, Windsurf, and Aider the README has per-tool instructions — and for any other LLM, pasting SKILL.md into the system prompt works too. Then just talk to it: "this box feels slow, triage it and tell me what you ruled out."

Why This Matters Now

AI agents are being handed production access faster than they're being handed engineering discipline. The gap between "can run commands" and "knows how to investigate" is exactly where 3 a.m. incidents get longer, rollbacks get riskier, and postmortems get vaguer. Agent skills — reusable, versioned, shareable instruction packs — are how that gap closes: not by making models smarter in general, but by giving them the specific procedures senior engineers carry in their heads.

Performance analysis is the perfect test case, because the methodology literature is explicit that guessing is the failure mode. Gregg's line on methodology-free analysis: it "can become a fishing expedition, where metrics are examined ad hoc, until the issue is found – if it is at all." The skill's job is to make the fishing expedition structurally impossible.

Star it, fork it, tear it apart: github.com/thecsdoctor/brendangregg-use-tsa-skill. Contributions are welcome — especially per-OS checklist expansions and new worked examples. And the next time your agent suggests "restart it and see," you'll know exactly which skill it's missing.

Tags: AI Agents, Claude Code, LLM Tools, Performance Engineering, Root Cause Analysis, Brendan Gregg, USE Method, TSA Method, Linux Performance, Observability, SRE, DevOps, Incident Response, Postmortem, Flame Graphs, eBPF, Off-CPU Analysis, OpenAI Codex, Kimi Code, OpenClaw, Agent Skills, SKILL.md, Site Reliability Engineering, Debugging, Troubleshooting

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