Shared context, field notes.
Notes on giving human + AI teams one place for the context behind the work.
Context Engineering for AI Agents: A Practical Architecture
A practical context-engineering architecture for AI agents: frame the task, select sources, compress safely, observe decisions and learn from outcomes.
Agent alignmentContext Engineering vs Prompt Engineering: What Changes for AI Agents?
Prompt engineering shapes instructions. Context engineering shapes the task state, tools, memory and organizational context an AI agent can use.
Agent alignmentKnowledge Graphs for AI Agents: Where They Help and Where They Do Not
Knowledge graphs help agents traverse entities and relationships, but graph structure alone does not establish authority, freshness or applicability.
Agent alignmentMCP and Organizational Context: What the Protocol Solves — and What It Does Not
MCP connects agents to tools and resources. It does not decide which organizational knowledge is trusted, current or applicable.
Agent alignmentRAG vs Agent Memory vs Context Engineering
RAG retrieves information, agent memory preserves experience, and context engineering composes the right task-specific input from both.
Agent alignmentVector Database vs Knowledge Graph for AI Context
Vector databases find semantically similar content; knowledge graphs expose relationships. Compare where each helps AI context and why they can work together.
Agent alignmentWhat Is AI Agent Memory? Working, Episodic, Semantic and Procedural Memory
AI agent memory includes working, episodic, semantic and procedural state. Learn what each type stores and how it should enter a task.
Agent alignmentWhy Bigger Context Windows Do Not Solve Context Management
Larger context windows add capacity, but they do not solve relevance, placement, stale information, permissions or the cost of noisy context.
Agent alignment.cursor/rules, CLAUDE.md aren't decision memory
Cursor rules and CLAUDE.md can be versioned in Git. See where cross-tool context drifts and how teams keep decisions current, scoped and reviewed.
Agent alignmentWhy AI coding agents ignore your team's standards
AI coding agents optimize against the code they can see, not the decisions your team already made — producing technically-correct, organizationally-wrong code.
AI Knowledge Management for Agents: From Documents to Usable Context
AI knowledge management must turn documents into trusted, task-applicable context instead of stopping at search and document storage.
Team workflowsHuman-AI Collaboration Needs Shared Context, Not Just a Chat Interface
Human-AI collaboration improves when work can carry shared context and reviewed learning across sessions, people and agents, not just chat windows.
Team workflowsMulti-Agent Systems vs MAMU Systems: The Missing Human Dimension
Multi-agent systems coordinate agents. MAMU systems also model human roles, access, ownership and organizational authority around shared state.
Team workflowsOrganizational Knowledge for AI Agents: What Must Be Structured?
AI agents need typed organizational knowledge: decisions, rules, skills, owners, scope, evidence and lifecycle, not only document chunks.
Team workflowsShared Memory vs Organizational Knowledge for AI Agents
Shared memory helps agents coordinate. Organizational knowledge adds authority, ownership, scope and lifecycle before information is reused.
Team workflowsWhat Is a Multi-Agent, Multi-User System (MAMU)?
A MAMU, or multi-agent, multi-user system, must coordinate private and shared state, identity, permissions and human approval as both sides scale.
Team workflowsWhy Multi-Agent, Multi-User Systems Need Shared Organizational Context
Multi-agent, multi-user systems need shared organizational context so locally consistent agents do not act on incompatible assumptions.
Team workflowsWhat is an AI-native company?
AI-native companies don't just use AI tools — they restructure work so agents and humans share context, hand off tasks, and are accountable to the same memory.
Team workflowsWhy multi-agent teams need shared memory
Add a second agent and coordination costs stop being linear. Multi-agent and human-agent teams need one shared, governed memory — not more context windows.
Context Engineering Starts Before Retrieval
Context engineering starts before retrieval: select evidence, scope, authority and lifecycle before assembling useful context for an AI agent.
GovernanceContext Observability: What Did the Agent Know and Why?
Context observability records what an agent could use, what it received, what was excluded and why, alongside tools and outcomes.
GovernancePrivate vs Shared Memory in Multi-User Agent Systems
Private and shared memory need explicit visibility classes, permissions and promotion paths so “remember this” has a clear audience.
GovernanceProvenance, Authority and Freshness in Agent Context
Provenance, authority and freshness are different tests for trustworthy agent context. A source can be traceable without being current or authorized.
GovernanceRaw Data Is Not Context: From Evidence to Trusted Knowledge
Raw data is evidence, not context. See how review, provenance, scope and task applicability turn source material into reusable knowledge.
GovernanceThe Knowledge Lifecycle for AI Agents
The knowledge lifecycle for AI agents runs from evidence and candidate formation through review, activation, change and supersession.
GovernanceWhat Is Context Governance for AI Agents?
Context governance decides which information is eligible to influence an AI agent, before relevance ranking and task-time composition.
GovernanceWhy Relevant Context Can Still Be Wrong
Relevant context can still be wrong for the action. Applicability also depends on scope, authority, freshness, permissions and environment.
GovernanceAI can extract rationale — not approve memory
AI is good at pulling candidate decisions and rationale out of messy evidence. Whether that becomes official organizational memory still needs a human.
GovernanceEnterprise knowledge fragmentation: the real cost
Fragmented decisions across chat, tickets and documents cost teams time and context. Learn how reviewed, applicable knowledge helps people and AI work together.
GovernanceWhy decision-making still breaks in modern teams
Teams have more tools and data than ever, yet decisions stay slow and get re-litigated. The problem isn't a lack of information — it's lost decision context.
GovernanceWhat Is DM?
DM governs organizational context for human + AI teams — not just agent memory, and not the same as the 'decision memory' concept it builds on.
Decision memory vs. task management
Jira, Asana, and Linear track what needs to get done. None of them preserve why a decision was made — which is exactly the part teams need six months later.
Decision memoryTypes of AI memory, and where decision memory fits
Episodic, semantic, procedural, and structured memory each solve a different problem for AI agents. Decision memory is the governed layer none of them cover.
Decision memoryWhy meeting notes are not decision memory
Meeting notes capture conversation. Decision memory preserves what was decided, why, who approved it, what evidence backs it, and whether it still holds.
Decision memoryWhy agile teams lose decision context
Sprint boards track delivery, but decisions can lose their reasoning across iterations. Preserve scope and architecture context without replacing agile tools.
Decision memoryWhy architecture rationale gets lost
Code shows what exists, not why it was chosen. See how ADRs preserve trade-offs, ownership and architectural decisions for people and AI agents.