Agent Engineering
The practical craft of building agents that survive contact with real work: tool design, context, evaluation, failure modes and the boundaries worth enforcing.
Page 1/3

AI Observability Is Decision Reconstruction
How to trace context, model decisions, tool effects, policy, and evidence so agent behavior can be understood and improved after the run.- Published on

The OpenAI Agent That Hacked Hugging Face
What the July 2026 Hugging Face intrusion reveals about long-horizon AI agents, benchmark incentives, containment, machine-speed offense, and the limits of model-level safety.- Published on

Prompt Injection Is a Trust Boundary Problem
Why agent security depends on separating instructions from untrusted content, constraining authority, tracking provenance, and verifying effects outside the model.- Published on

The AI-Native Team Is a Control System
A practical model for teams that use AI agents as an execution layer while humans set intent, shape constraints, evaluate outcomes, and improve the system.- Published on

Idempotency for AI Agents
Why retries, partial failures, and long-running agent loops make idempotent actions, reconciliation, and explicit operation identity essential.- Published on

Human Approval Is an Architectural Boundary
How to place human judgment at consequential transitions without turning AI workflows into notification queues or rubber-stamp theater.- Published on

Evals Are the AI Delivery Pipeline
Why AI evaluations should operate as a continuous delivery system for behavior, with representative cases, evidence, release gates, and production feedback.- Published on

Agent Memory Without the Mythology
A practical architecture for AI memory built from working state, durable facts, episodic records, retrieval policy, and deliberate forgetting.- Published on

Context Engineering Is Interface Design
How to design the information boundary around an AI agent so instructions, evidence, tools, and working state remain legible under pressure.- Published on