AI agents are systems problems
The difficult part of useful agentic AI is not the model call. It is everything required to make judgment and action reliable.
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Technical articles, research, engineering notes, and professional analysis about intelligent systems and the real engineering required to make them useful.
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AI agents are systems problems
Models matter. But architecture, context, permissions, evaluation, and operations decide whether an agent becomes infrastructure or remains a demo.
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articleA practical guide to agentic engineering for DevOps practitioners, covering harness, context, prompts, RAG, memory, tools, control loops, sandboxes, guardrails, observability, and evaluation.The model may supply the reasoning, but the harness makes an agent operational. Here is how prompt, context, runtime, tools, memory, safety, and observability fit together—and why the stack should feel familiar to DevOps engineers.
articleA practical guide to the difference between agents, skills, and AGENTS.md—including how definitions, tools, permissions, and reusable workflows fit together in Codex.Agents provide judgment, context, and action; skills package repeatable methods. Understanding the boundary makes both easier to design, secure, and reuse.
articleA practical guide to tokenization and embeddings, covering BPE, WordPiece, Unigram, SentencePiece, word2vec, GloVe, fastText, contextual Transformers, and modern retrieval models.Tokenization breaks text into model-readable units. Embeddings place those units—or entire passages—into learned numerical spaces. Here is how the major approaches work and why the distinction matters.
articleRyan Powers reflects on his path from Marine cyber operations to cybersecurity graduate study and applied research at USF.How military service, resilience, and an applied research mindset shape my work in cybersecurity and AI at the University of South Florida.