Memos
Technical memos on production AI agent reliability. State drift, context engineering, decision architecture, and structural failure modes.
Every Multi-Agent Framework Ignores the Same Problem
Read →CrewAI, AutoGen, LangGraph — they all orchestrate agents. None of them govern what context travels between agents. That's the gap where production failures hide.
The Vocabulary Problem in Agent Infrastructure
Read →Categories are made of words. Agent infrastructure has no canonical vocabulary, so every vendor invents one and every buyer compares apples to oranges. The category cannot mature until the language does.
MCP Solved the Pipes. Who Solves the Water Quality?
Read →The Model Context Protocol connects agents to everything. But connecting is not governing. Without a context graph, MCP delivers raw, unvalidated, unscoped data to every decision.
Context Engineering in 2026: From Karpathy's Tweet to Production Infrastructure
Read →Everyone talks about context engineering. Nobody says how to build it. The context graph is the missing implementation. Not an opinion. An architecture.
How Context Graphs Prevent the 7 Silent Agent Failures
Read →Production agents fail silently — not from bad prompts, but from bad context structure. Here are the 7 failure modes that context graphs eliminate before they compound.
Gartner 2026 Confirms It: The Context Graph Is the Missing Layer in Autonomous AI Agents
Read →Gartner's 2026 predictions for data and analytics describe an autonomous agent future. Every prediction points to the same architectural gap: agents need context graphs to make reliable decisions at scale.
Why Your Data Agents Need a Context Layer
Read →The bottleneck isn't model capability — it's context. Without a structured context layer, data agents fail because enterprise data is messy and undocumented.
AI Agent Evaluation Is Broken: 5 Structural Gaps Between Evals and Production Reality
Read →Most AI agent evaluation frameworks test wrong things. Discover 5 structural gaps between passing evals and production-ready agents, and how to fix them.
AI Agent Failure Patterns Atlas (2026): 12 Structural Breakpoints
Read →A practical atlas of 12 recurring AI agent failure patterns in production, with root causes, detection signals, and architecture fixes.
Why Your AI Agent Test Suite Is Lying to You: 4 Testing Gaps That Only Show Up in Production
Read →AI agent testing in production reveals structural failures that staging environments can't catch. Learn the 4 testing patterns that undermine reliability.
AI Agent Monitoring Is a Lie: 5 Observability Gaps That Let Production Failures Through
Read →Discover why traditional monitoring fails AI agents in production. 5 structural patterns expose how teams miss decision failures while dashboards stay green.
Why RAG Is Not Enough for Production AI Agents
Read →RAG improves recall but does not govern decisions. For production agents that take action, the gap between retrieval and reliability is structural.
Why Agent Memory Architectures Fail at Scale
Read →Session memory, vector stores, and thread-based memory all degrade under production load. The problem isn't storage — it's structure.
Production AI Has a State Problem
Read →The next wave of AI failures won't come from the models — it comes from state drift.
Context Graph vs Knowledge Graph
Read →A knowledge graph maps reality. A context graph governs decisions.
What is Context Graph?
Read →The missing infrastructure for reliable AI agents.