Why More Context Can Make an LLM Worse
The default response to agent failure is to stuff more context into the prompt. That often makes the system worse. A context window is working memory, not a hard drive.
Topic
agentic-ai
A collection of 32 issues
The Event-Driven Operator: Agents That Wake on Events, Not Cron
Polling-based agents fail by construction — not occasionally, but structurally. The event-driven operator pattern eliminates that failure class by separating wake-up, execution, and recovery.
The Supervisor Pattern: How to Use Cheap Models Without Losing Control
Cheap models can expand execution, but the architecture must preserve scarce judgment. The Supervisor Pattern separates bounded work from consequential review.
When Validators Matter More Than Better LLMs
In a multi-step AI pipeline, the dangerous failure is not just a bad model output. It is a bad intermediate result becoming trusted state. Validators matter because they stop bad state at the boundary.
Text-First Extraction Is an Epistemic Error
RAG pipelines degrade in a pattern consistent enough to be structural: a text extractor destroys semantic layout before a single token reaches the model. The failure is not in the retriever. It is at the extraction boundary — where two-dimensional structure gets linearized into noise.
Your Company's "Agentic Surface Area": The New Metric for Competitiveness
Your CEO asks: "How much of our operation is AI-powered?" The uncomfortable part is that the question sounds simple and usually has no clean answer. Teams can name pilots, copilots, and workflows, but not how much decision-making has actually moved to agents.
Security teams have "attack surface
The API Is the New Command Line: A Manifesto for Agentic Design
The command line gave humans a grammar for commanding machines. The API gives agents the same grammar — at scale, in parallel, without a human in the loop. Every design decision downstream flows from whether you've internalized that shift.
Every AI Agent is a Liar, a Slacker, and a Thief
The industry is currently stuck in the Prototype Trap. We can build a magical AI demo in a weekend, but we spend six months failing to make it reliable in production. Why? Because we assume the model wants to help us. It doesn't. By default, every AI agent is a liar, a slacker, and a thief.
The Probabilistic State Machine: Why Linear Chains Fail
In data engineering, we build linear pipelines. In AI engineering, linear pipelines are fatal. If you have three steps, each with 90% accuracy, your system is only 72% reliable. To fix this, we must abandon the Chain and embrace the Probabilistic State Machine.
The Agentic Contract: Why a Prompt Is Not a Specification
A raw prompt can express intent, but it cannot define a real behavior contract. The Agentic Contract turns one agent capability into a typed, executable specification with explicit inputs, outputs, failure modes, and economic bounds.