AI Concepts

MCP is a deployment and integration concept for exposing tools and workflows to AI systems. In the newsletter it is mentioned as a way to deploy an analytics tool everywhere.

MCP is emerging as a standard way to expose tools and workflows to AI systems across products and environments.

Systems that use models plus tools, memory, and planning to perform multi-step tasks autonomously or semi-autonomously. The newsletter references both agent architectures and agentic coding/workflows.

AI agents combine models, tools, memory, and planning to complete multi-step tasks with varying degrees of autonomy.

An AI development pattern where models act more like autonomous coding agents. The newsletter uses it to describe both NVIDIA Dynamo’s target workload and GPT-5.5/Codex improvements.

Agentic coding refers to AI systems that can plan, use tools, inspect code, test, and iterate like semi-autonomous software agents.

An AI-native development approach where builders use AI tools to rapidly create software. The newsletter treats it as a growth and product-building methodology.

Vibe-coding is an AI-native way of building software that uses prompts and AI tools to rapidly create prototypes and products.

An OS-based agent framework referenced as portable across runtimes. The newsletter emphasizes that it can run in multiple environments without runtime lock-in.

DeepAgents is positioned as an OS-based, portable agent harness rather than a runtime-locked platform.

A retrieval-and-orchestration approach focused on getting the right context into the model. The newsletter frames it as largely about agentic search and tool composition.

Context engineering extends prompt engineering by orchestrating instructions, memory, retrieval, tools, and tool outputs around the model.

A pattern for grounding model outputs in retrieved context rather than relying solely on model weights. The newsletter frames it as often outperforming fine-tuning for practical product work.

RAG grounds LLM outputs in retrieved external information, making domain-specific AI products more accurate and current.

Reusable behavior modules or instructions for guiding AI agents. The newsletter mentions skills as one of the steering mechanisms for Claude Code and other agents.

Skills are reusable instruction or behavior modules that help AI agents perform tasks more consistently.

Specialized subordinate agents used to break down and orchestrate tasks. The newsletter mentions them as part of Claude Code steering controls.

Subagents are specialized subordinate agents used to break larger tasks into smaller, manageable units.

An attack technique where malicious instructions manipulate model behavior, often by hiding within content or privileged roles. The newsletter frames role confusion as a core challenge for defending against it.

Prompt injection is a product-security issue as much as a model-behavior issue, especially for agents with tools and data access.

A research capability embedded into Perplexity Computer as a built-in skill. For PMs, it indicates the packaging of advanced research into agent workflows.

Deep Research is evolving from a standalone mode into an embedded capability inside AI agent workflows.

Agents that perform coding tasks and can increasingly orchestrate adjacent workflows like design. The newsletter uses them as the execution layer for Design.md scripts.

Coding agents are evolving from code assistants into autonomous execution systems for software and adjacent workflows.

A workflow for using AI agents to plan, build, test, and update software with minimal manual intervention. The newsletter treats it as a practical product-development paradigm.

Agentic engineering centers on building software with agents that can plan, write, and execute code autonomously.

A framework for measuring whether AI agents reliably complete tasks across real inputs, edge cases, and version changes. It emphasizes step-level traces and component-level decisions, not just final output quality.

Agent evaluation measures not only final outputs but also the steps, tool calls, and component decisions behind them.

Benchmarking methods for evaluating AI coding agents in realistic software tasks. The newsletter notes that infrastructure variability can materially affect scores.

Agentic coding evals measure AI coding agents on realistic software tasks rather than isolated prompt-response coding tests.

The class of models discussed as having a blind spot with continuous, high-dimensional, noisy data. This concept is used to frame a limitation in current AI capabilities.

LLMs are foundational to many generative AI products but are best understood as powerful language models, not universal intelligence systems.

A technique for grounding model outputs in retrieved information. It is cited here as a component of a modular agent framework.

RAG improves model outputs by retrieving relevant external information at generation time.

Simon Willison’s command-line LLM tool for interacting with models and APIs. This release adds support for OpenAI’s Responses endpoint and better reasoning-token handling.

LLM here refers both to large language models broadly and specifically to Simon Willison’s `llm` command-line tool in the newsletter context.

Agent Skills are reusable capability modules or instructional patterns for agents. The newsletter references a React best-practices tutorial framed as an agent skill.

Agent Skills provide a modular framework for defining and maintaining what AI agents can do.

A plugin/pattern used to manage build loops and goal-driven agent workflows. Here it is tied to Codex Desktop and the LFG loop for prototype completion.

Compound Engineering is the practice of capturing prompt and agent learnings so future AI outputs improve over time.

A test-driven development pattern adapted for coding agents. It emphasizes an iterative failure/success loop that can make agentic coding more reliable.

red/green TDD adapts classic test-driven development into a structured workflow for coding agents.

A lexical retrieval ranking function used here to select relevant tool definitions. In PM tooling, it helps improve retrieval accuracy and reduce context-window bloat.

BM25 is a lexical ranking function that helps AI systems retrieve the most relevant documents or tool definitions.

A paradigm that treats cloud infrastructure as autonomous coding agents to automate deployment and operations. For AI PMs, it reframes infrastructure as an agentic workflow rather than a static system.

Agentic Infrastructure reframes cloud operations as autonomous agent workflows rather than static systems.

AGI refers to broadly capable artificial general intelligence. Here it is discussed as becoming usable in 2026 and requiring contextual systems around it to be effective.

AGI is a strategic concept that shapes AI roadmaps, governance, and stakeholder expectations more than a single agreed technical milestone.

Reusable Claude-based skill modules that package agentic workflows into portable components. The newsletter frames them as a way to avoid building AI agents from scratch.

Claude skills package repeatable AI workflows into reusable modules, reducing the need to build agents from scratch.

A collection of techniques and patterns for building agentic systems. The newsletter frames it as a guide page for AI builders.

Agentic Engineering Patterns is a guide that collects practical techniques for building and operating agentic systems.

A security risk pattern where AI agents have private data access, ingest untrusted content, and can exfiltrate data. For AI PMs, it is a key framework for designing safe agent features.

The lethal trifecta describes the dangerous combination of private data access, untrusted content ingestion, and exfiltration capability in one AI system.

An approach to AI systems where agents perform tasks autonomously with tools and browser interaction. The newsletter frames 2026 as a year focused less on novelty and more on trust in deployed agentic systems.

Agentic AI describes autonomous AI systems that can plan, use tools, and complete multi-step tasks across software and browser environments.

A pattern for agent-to-agent communication and collaboration. The newsletter mentions it as part of a step-by-step approach to building multi-agent systems.

A2A is a pattern and protocol framing for how multiple AI agents communicate and collaborate.

Leading AI labs that control high-demand model APIs and compute. The newsletter uses the term to describe vendors that might restrict API access to prioritize their own products and customers.

Frontier AI labs control highly demanded model APIs and may prioritize their own products or top customers when compute is scarce.

A framework for specifying goals, context, and guardrails in multi-agent systems. It helps PMs guide autonomous agents with explicit objectives and stop rules rather than rigid control.

Intent Engineering helps PMs specify objectives, context, and guardrails for autonomous agents.

The practice of connecting agents to external developer tools such as linters and debuggers. It is highlighted here as a building block for effective coding agents.

Tool integration connects AI agents to external developer tools such as linters, debuggers, and test runners.

Programmable interfaces that let AI agents and software systems access services and complete tasks. The newsletter positions APIs as one of the means for agents to act on behalf of users.

APIs let AI agents access services and take actions on behalf of users.

A workflow framework for building customizable agentic systems. It is highlighted as integrating with ACP.

Agent Workflows is a framework for building customizable agentic systems within the LlamaIndex ecosystem.

A structured-prompt framework for improving the consistency and quality of outputs from Claude Code. It is positioned as a way to turn an AI coding assistant into a more reliable development partner.

SuperClaude is a community framework that uses structured prompts to improve the consistency of Claude Code outputs.

A modular layer that adds tools, guardrails, and custom instructions to AI agents. It is described as a composable harness for production agent systems.

Agent middleware is a modular layer for adding tools, guardrails, and instructions to AI agents.

An agent design pattern where work is split into sub-tasks and assigned dynamically. In the newsletter, it is one of the core ingredients for building autonomous coding agents.

Task delegation breaks complex agent objectives into smaller sub-tasks assigned dynamically across tools or specialized components.

The process of updating legacy COBOL systems, often for enterprise migration and maintenance. AI agents are increasingly positioned as tools to accelerate this high-friction modernization work.

COBOL modernization focuses on updating legacy mission-critical systems for maintainability, integration, and migration.

A product and engineering concept describing the hidden cost of AI-accelerated development when teams lose shared understanding of the system. It reframes debt from code maintenance to team cognition and system comprehension.

Cognitive debt describes how AI-accelerated development can shift costs from code maintenance into lost team understanding.

OpenTelemetry is an observability standard for traces, logs, and metrics. The newsletter mentions Codex exporting agent-aware telemetry through it for auditing and monitoring.

OpenTelemetry is a standard for collecting traces, logs, and metrics across software and AI systems.

Systems composed of multiple cooperating AI agents, often designed to divide work and collaborate through structured patterns. The newsletter references building these systems with Python and agent-to-agent communication patterns.

Multi-agent systems divide work across specialized AI agents that coordinate through structured communication patterns.

AI models whose weights or availability are open enough to encourage broad reuse and experimentation. The newsletter frames them as a driver of innovation across the ecosystem.

Open models are framed in the newsletter as a major driver of AI innovation across startups, researchers, students, and industries.

A tool interface used with skill.md to reduce token usage and run MCP commands in a more efficient way.

CRI is a lightweight interface for running MCP commands with lower token overhead.

An architecture where multiple specialized agents collaborate instead of one general-purpose agent. The newsletter includes debate over whether this is necessary versus using a single tool-loaded agent.

A multi-agent system uses several specialized agents to collaborate instead of relying on one general-purpose agent.

A training approach used here to teach Composer to self-summarize, reducing reliance on handcrafted prompts.

Reinforcement learning helps AI systems improve behavior based on outcome feedback rather than prompts alone.

A legacy programming language often targeted for modernization and migration efforts. For PMs, it represents enterprise technical debt and transformation risk.

COBOL remains core to many enterprise systems despite being viewed as legacy technology.

A concept covering how organizations evaluate large language models consistently and meaningfully. The newsletter frames standardization of benchmarks as a major enterprise challenge.

LLM benchmarks give organizations a repeatable way to compare model performance on real product tasks.

A programming language commonly used for building AI systems and agent workflows. The newsletter references it in the context of constructing multi-agent systems from scratch.

Python is the dominant implementation layer for modern AI experimentation, orchestration, and agent workflows.

A test introduced by Andrew Ng for evaluating economic utility. It is framed as a way to assess whether AI systems provide meaningful real-world value.

The Turing-AGI Test evaluates AI progress based on economic utility rather than abstract intelligence claims.

A search tool mentioned as part of ingesting PM work into Claude Code. It appears to support retrieval over a large personal knowledge base.

QMD appears to be a search and retrieval layer used to access large personal or operational knowledge bases.

A software architecture paradigm where engineers orchestrate agents instead of hard-coding decision trees. For PMs, it suggests product teams may design systems around LLM behavior rather than deterministic logic.

Agent-first software design shifts software building from hard-coded decision trees to orchestrated agent behavior.

A memory architecture that mimics human memory instead of relying on RAG or vector search. For PMs, it suggests alternative approaches to long-context recall and personalization.

Large Memory Models are described as a memory architecture that mimics human memory rather than relying on RAG or vector search.

A lightweight skills-based pattern for packaging agent capabilities in small context-efficient files.

skill.md is a lightweight pattern for packaging agent capabilities into small, context-efficient files.

A protocol for connecting AI models to external tools and servers. The newsletter references discovery of MCP servers and reducing MCP token usage.

Model Context Protocol standardizes how AI models and agents connect to external tools, servers, and data sources.

A defensive technique mentioned as part of Claude Code's strategy to deter model distillation by misleading competitors' training runs.

Anti-distillation poison pills are designed to make model outputs less useful for competitors attempting distillation.

A memory architecture pattern for AI agents that separates different memory layers to improve context retention and task performance. It is presented as part of the design of autonomous coding assistants.

Layered memory separates short-term, task-level, and longer-term memory to improve AI agent performance.

A PM framework focused on user value, tradeoffs, and outcomes rather than just technical implementation. Mentioned here as a skill engineers should develop in AI product teams.

Product-thinking emphasizes user value, tradeoffs, and outcomes over pure implementation.