The Summary Tax: When Compaction Eats More Tokens Than It Saves
Long-running agents trigger summarization on overflow or hierarchically, and at scale the compaction passes quietly become the dominant inference cost — and the dashboard never tells you.
The Write Side of the Agent: Designing for Reversibility at the Action Layer
Every write operation an AI agent makes is a potential incident. How to design the action layer for reversibility before your agent deletes something you cannot get back.
AI Co-Pilot vs. AI Pilot: The Evidence-Based Product Decision Framework
The specific deployed-system signals — task completion rate, error recovery time, user override frequency, edge-case exposure — that determine whether AI should be advisory or autonomous, and why the wrong default costs you user trust that's hard to recover.
API Documentation Is Reliability Infrastructure: How Your Docs Determine Agent Success Rates
When AI agents consume your API via tool calling, documentation quality becomes a direct reliability variable. Ambiguous parameters and missing error semantics cause measurable failure rates that no amount of prompt tuning can fix.
The Context Format Decision Most Teams Make Accidentally: JSON vs Markdown vs Plain Text
Choosing between JSON, markdown, and plain text for LLM context isn't a stylistic preference — it determines reasoning mode, accuracy, and cost. Here's how to make the decision deliberately.
LLM Self-Debugging: When the Explanation Is the Signal vs. When It's the Lie
LLMs answer fluently when asked why they failed — but the explanation and the actual failure mechanism are often two different things. A practical guide to telling them apart before you act.
MCP Ambient Authority: The Tool-Chaining Attack Surface That Session-Scoped Permissions Create
MCP's session-scoped permission model grants agents access to entire tool surfaces at authorization time. Here's how that creates tool-chaining attack paths, and what least-privilege patterns actually look like in practice.
Tool Call Convergence: Designing Agents That Know When to Stop
Agents that loop through tool calls without a stopping criterion burn tokens for no gain. Here's the engineering discipline for knowing when enough information is enough.
Agent Memory Contamination: How One Bad Tool Response Poisons a Whole Session
A single factually wrong or adversarially crafted tool response can corrupt an LLM agent's reasoning for an entire session. Here's the failure anatomy and the defenses that actually work.
Agentic Systems Are Distributed Systems: Apply Microservices Lessons Before You Learn Them the Hard Way
The failure modes plaguing multi-agent AI systems today are distributed systems problems from 2015 in disguise. Teams that internalized microservices lessons before building agents are shipping more reliable systems.
AI-Native API Design: Building Backends That Agents Can Actually Use
REST APIs were designed for human-authored clients. AI agents break them in entirely predictable ways — hallucinating endpoint names, retrying without idempotency, ignoring sparse error messages. Here's how to build backends that agents can call reliably.
AI-Native Logging: Capture Decisions, Not Just I/O
Conventional logs tell you what your LLM system did. AI-native logging tells you why — capturing the decision logic, rejected alternatives, and confidence signals that explain production failures.