Key terms and concepts in AI-native product ownership, context engineering, and multi-agent systems.
The practice of coordinating multiple AI agents to accomplish complex goals. Involves defining agent roles, communication protocols, shared state management, and escalation patterns. The orchestrator determines which agent handles which task and how results are synthesized.
The execution environment in which AI agents operate. Includes the model, available tools, persistent context (CLAUDE.md), working directory, and configured hooks. The runtime determines what an agent can do, what it knows, and how it interacts with external systems.
Using AI agents to enhance backlog refinement ceremonies. Agents pre-analyze stories, identify edge cases, suggest acceptance criteria, estimate complexity, and detect dependencies — transforming refinement from a discovery exercise into a validation exercise.
A composite metric (0-100) measuring how effectively a team or individual is positioned to benefit from AI agent integration. Factors include knowledge architecture maturity, process readiness, technical infrastructure, and organizational alignment.
A knowledge management system designed specifically for AI agent consumption. Built with typed frontmatter, explicit relationships, and machine-traversable structure so agents can reason with your knowledge rather than just search it. Typically implemented as an Obsidian vault or structured markdown repository.
A product owner who designs AI agent systems rather than just using AI tools. They shift from writing stories to writing context, from attending ceremonies to designing agent workflows, and from managing backlogs to orchestrating knowledge architectures that enable autonomous agent work.
A hierarchical decision framework where axioms (unchangeable truths) derive principles (guiding beliefs), which derive rules (actionable constraints). This structure gives AI agents a composable reasoning chain: they can trace any operational rule back to the foundational belief that justifies it.
A persistent context file that serves as the system prompt for Claude Code sessions. It defines project conventions, agent behaviors, available tools, and operational constraints. Functions as the "operating manual" that transforms a generic AI into a specialized agent tuned to your project.
The speed at which a team can produce high-quality content. AI-augmented content pipelines dramatically increase velocity by automating research, first drafts, quality scoring, and distribution — while humans focus on voice, strategy, and final review.
The practice of designing structured information environments that enable AI agents to reason effectively. Unlike prompt engineering, which optimizes individual instructions, context engineering focuses on the surrounding architecture — what knowledge is available, how it connects, and what constraints guide decision-making. It shifts the focus from "what to say" to "what to know."
AI agents designed to monitor system health, detect anomalies, and enforce quality constraints without human intervention. They operate as defensive agents — observing patterns, flagging degradation, and triggering alerts when systems drift from defined standards.
A structured approach to making consistent decisions. For AI agents, decision frameworks encoded as knowledge graphs allow autonomous reasoning — agents can navigate from high-level principles to specific rules without human intervention at each step.
Grouping similar AI tasks together to maintain cognitive flow and reduce context-switching overhead. Instead of interleaving research, coding, and review, you batch all research into one session, all coding into another, maximizing both human and agent productivity.
A quality control framework where work must pass through defined checkpoints before progressing. Each gate has specific criteria, responsible reviewers, and escalation paths. In AI-augmented teams, gates are often enforced by guardian agents that automatically validate against checklists and policies.
An AI agent whose primary role is defensive — monitoring for quality degradation, policy violations, or system drift. Guardian agents observe rather than create, intervening only when defined thresholds are crossed. Examples include gate wardens, validation agents, and sentinel watchers.
A network of interconnected knowledge entities with typed relationships. In the context of AI agents, a knowledge graph provides traversable decision paths — agents can follow links from principles to policies to checklists, understanding not just what to do but why.
Patterns for orchestrating multiple AI agents to work together on complex tasks. Includes shared context management, task delegation, conflict resolution, and result synthesis. Key patterns include hub-and-spoke, pipeline, and swarm architectures.
A multi-agent architecture specifically designed for product ownership tasks. Includes specialized agents for refinement, analysis, validation, research, and ceremony preparation — all operating on a shared knowledge graph with defined roles, permissions, and coordination patterns.
The practice of crafting individual instructions to get better outputs from AI models. While valuable for simple interactions, it reaches a ceiling with complex multi-step tasks where context engineering — designing the information environment around the prompt — becomes more impactful.
Retrieval-Augmented Generation — a technique where AI models retrieve relevant documents from a knowledge store before generating responses. Combines the breadth of a large knowledge base with the reasoning capability of language models. Often uses vector embeddings for semantic search.
A Next.js pattern where functions marked with "use server" execute on the server but can be called from client components. Used for mutations like form submissions, database writes, and API calls. Provides type-safe RPC without separate API routes.
A productivity pattern that categorizes AI working sessions by their cognitive mode: deep work sessions (complex problem-solving), flow sessions (steady output), review sessions (quality checking), and research sessions (information gathering). Each type has optimal duration, tooling, and context requirements.
Information that multiple AI agents can access simultaneously to maintain coherent collaboration. Includes project knowledge graphs, state files, configuration documents, and real-time data stores. Effective shared context prevents agents from making contradictory decisions.
Using AI-generated artifacts and data to increase influence with stakeholders. Instead of manually creating reports and presentations, AI agents produce polished stakeholder artifacts — competitive analyses, impact assessments, progress dashboards — that elevate the conversation from status updates to strategic decisions.
YAML metadata at the top of markdown files that follows a defined schema. Each file declares its type (axiom, principle, policy, etc.) and structured fields that AI agents can parse and traverse. This enables knowledge graphs that are both human-readable and machine-navigable.