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LLM Programming Agents

~3m skim, 490 words, updated Jun 25, 2026

Claude, Bob, Codex, and others.

Contents

The Problem with LLM Programming¶

Language models have no clue what they are doing. They don’t think like you.

LLM generated code is riddled with subtle dangers that can only be spotted by experienced programmers with domain knowledge. The output looks great to both the untrained and trained eye, and the dangers only become apparent when reading it line by line and seeing the amalgamated assumptions of thousands of different codebases.

If you are a student, do not use LLMs (with the exception of searching through documentation,) or your critical thinking skills will be heavily eroded. LLMs do program better than those who only skim docs and copy-paste, who fail to attain a deep understanding of the runtime or machine beneath a programming language, and ultimately those who fail to become competent computer programmers. Don’t end up like these lobotomized drones, and develop your critical thinking!

Most of the domain-specific knowledge in this space is made from the softest, fluffiest, most irritating fairy dust you can imagine. Structured thinkers (like Computer Engineers and programmers in general,) are used to highly structured layers of systems with highly predictable outputs for given inputs. This reliability matters a lot when systems, from the code to the silicon, are tens of abstractive layers deep. Giving one million different whimsical names to concatenated text inputs and text outputs is something that Satan certainly dreamed up just to violate our pragmatic virtues.

Use discernment - cut to the meat of what is underneath the marketing crap.

Ironically, it is this mindset that enables my success as the GenAI Enablement Leader in my [IT Consulting] practice. Having a grounded understanding of the real capabilities of LLMs is essential for implementing and utilizing them for clients - whether the use case is graph-rag agents, enabling developers, etc.

Stay grounded (and your LLM as well,) and may your prompts have clarity, context, and good structure.

Claude Certified Architect (CCA-F)¶

The Anthropic CCA (Claude Certified Architect) Certification validates Claude knowledge with a 120-minute exam containing 60 multiple-choice questions¹:

1. Agentic Architecture (27%)
2. Tool Design & MCP Integration (18%)
3. Claude Code (20%)
4. Prompt Engineering (20%)
5. Context Management & Reliability (15%)

The questions are heavily weighted toward practical implementation.

anthropic.skilljar.com/claude-certified-architect-foundations-access-request

IBMer? Check the IBM learning plan for this certificate.

Claude Tips¶

1. Keep CLAUDE.md under 500 lines
2. Use YOLO mode for experimentation

The Anthropic AI Fluency Framework¶

This section is from an Anthropic training session - Copyright 2025 Rick Dakan, Joseph Feller, and Anthropic. Released under the CC BY-NC-SA 4.0 license.

Four interconnected competencies neccessary to ensure our interactions with AI are effective, efficient, ethical, and safe.

1. Delegation: Setting goals and deciding whether, when, and how to engage with AI
2. Description: Effectively describing goals to prompt useful AI behaviors and outputs
3. Discernment: Accurately assessing the usefulness of AI outputs and behaviors
4. Diligence: Taking responsibility for what with do with AI and how we do it