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April 1, 2026

The Path to Autonomous Agents Was Mapped Decades Ago. Nobody Noticed.
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A practical methodology for building autonomous AI agents — not by making models smarter in isolation, but by observing how humans guide AI in real conversations, extracting the control patterns, and replacing them one at a time with programmatic equivalents. Draws on decades of practice from aviation, call centers, and education.

  • The Control Protocol You Didn't Know You Had
  • What Toyota Knew That We Keep Missing
  • The Tools Are Here. The Protocol Is New.
  • A Switch Would Kill. They Built a Dial.
  • The Seat Next to the Expert
  • The Blackboard Had It First
  • Maybe the Human Was Never Supposed to Leave
  • Simple Workflows Bend to AI Easily. Enterprise Operations Don't.
  • Hunt the Hunter
March 24, 2026

AI Reads Every Word You Say. It Still Gets You Wrong.
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Why even the most carefully worded prompts and rules fail to capture intent — and what to do about it. This post examines the fundamental gap between specifying instructions and conveying meaning, arguing that better structure (not more rules) is the path forward.

  • The Specification Problem
  • AI Doesn't Have Your Common Sense
  • The Confidence Problem
  • You Can't Even Watch It Fail
  • The Rules Trap
  • Intent Over Instruction
  • The Harness: Better Structure, Not More Rules
  • The Framework: Beyond Prompts
March 18, 2026

One Million Lines of Code. Zero Keystrokes. Welcome to Harness Engineering.
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An in-depth look at the emerging discipline of harness engineering — the practice of wrapping AI models in layered structures that control prompt context, architectural constraints, entropy, and verification loops to produce reliable, production-grade software at scale.

  • The Drone Operations Center
  • What a Harness Actually Is
  • The Three Nested Layers: Prompt, Context, Harness
  • Context Engineering Layer
  • Architectural Constraints
  • Entropy Management
  • Verification and Feedback Loops
  • Security
  • Frameworks and Tools
March 11, 2026

One Sentence Can Hijack Your AI. Here's How to Stop It.
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A comprehensive guide to AI security in enterprise settings, covering the attack surface of AI agents, the top three attack vectors (direct injection, indirect injection, agent-to-agent propagation), and six practical defense techniques drawn from military intelligence and zero-trust principles.

  • The Attack Surface
  • The Top Three Attack Vectors
  • Trusted Data as the Real Threat
  • Zero-Trust Security
  • Technique 1: Compartmentalization
  • Technique 2: Source Verification
  • Technique 3: The DMZ Architecture
  • Technique 4: Human-in-the-Loop
  • Technique 5: Observability and Audit Trails
  • Technique 6: Rate Limiting and Anomaly Detection
March 5, 2026

100% AI Code at Anthropic. 19% Slower Everywhere Else. Why?
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Explains the dramatic gap between AI-native labs reporting near-total AI code generation and the measured slowdown experienced by most engineering teams. The difference comes down to architectural complexity — and the post argues that every engineer must now develop real architectural thinking to use AI effectively.

  • Greenfield vs. Maintenance
  • Enterprise Complexity
  • Enforcement vs. Architecture
  • The Cognitive Juggling Limit
  • Why Every Engineer Needs Architectural Thinking
  • The Limits of AI Skills
  • Keep Coding
February 26, 2026

Two Roads for AI in Software Engineering — and Neither Is What You Think
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Identifies two emerging use cases for AI in software development: bottom-up (AI as a developer productivity tool) and top-down (AI building entire systems from a prompt). Examines trust, risk compensation, compartmentalization, and domain-specific languages as key considerations.

  • Bottom-Up: AI as Productivity Tool
  • Trust and Risk Compensation
  • Compartmentalization in Architecture
  • Top-Down: AI Building Entire Systems
  • Domain-Specific Languages
  • Formalizing the Handoff Between Phases
February 26, 2026

Beyond Chatbots: The Case for AI-First Software Architecture
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Makes the case for purpose-built AI-first architectures by walking through the challenges of integrating AI capabilities — phone calls, SMS, decision-making — into traditional enterprise systems. Covers long-running activities, human-in-the-middle workflows, security, and the shift from deterministic to probabilistic software.

  • Autonomous AI Phone Calls
  • Long-Running Activities
  • Human-in-the-Middle
  • Security and Zero-Trust
  • AI Decision-Making in Applications
  • Deterministic vs. Probabilistic Architecture
February 26, 2026

The Bedridden Genius: A Mental Model for What AI Can Actually Do
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Develops a practical mental model for understanding AI capabilities and limitations: think of the LLM as a statistically average person with tools (aides) and a persistent overconfidence problem. Covers what AI can and cannot do for both business operators and software engineers.

  • LLMs as Statistical Text Prediction
  • Tools as "Aides"
  • The Dunning-Kruger Effect in AI
  • AI as a Junior Engineer
  • What AI Can Do for Business
  • What AI Cannot Do
  • What AI Can Do for Engineering