Field Notes · GetLatest AI
Field notes.
Practical notes on AI agents, AI marketing, and automation, from the team that runs this stack on its own companies first. What we learn, written down.
Microsoft 365 AI Agents: Build, Deploy, and Ship to the Agent Store
Most shops can build a Microsoft 365 agent. Far fewer can deploy it into your tenant and commercialize it on the Agent Store. Here are the four layers of Microsoft agents and how we close the full loop.
Read the noteClaude Cowork for SMB Teams: 48 Capabilities, Wired to Your Tools
Claude Cowork is powerful out of the box and generic out of the box. We configure it around how your team actually works, connected to your existing tools, so everyone has it on day one.
Google Agent Garden, Configured for Your Team (Not Just a Demo)
Google Agent Garden, Gemini, and Vertex AI give you strong starting blueprints. We turn them into agent workflows that connect to your real systems and keep working after the demo.
AI Governance and Guardrails: Shipping Agents That Survive the Real World
An AI agent with real access and no controls is a side door into your business. Here are the guardrails, traceability, security, and oversight that make agents safe to run in real workflows, and how we design them.
AI Go-to-Market Engine: Turn Buying Signals Into Booked Meetings
Most outbound chases everyone equally and converts nobody. Here is how we run go-to-market as an engine that watches for buying signals, researches the prospect, scores them against your ICP, and drafts the outreach, before a rep touches it.
AI Competitive Intelligence: A Living System, Not a One-Time Report
Most competitive research is a deck that's stale a week after you build it. Here is how we run competitive intelligence as a system that refreshes every month: 95 files per target, four customer perspectives, and a register that flags when your assumptions stop being true.
The Platform the Taxonomy Demands: How SnappyClaw Delivers on the Agent Promise
Most platforms that claim autonomous agent capabilities cannot answer more than two or three of the six questions that define real agency. This article examines what it takes to build a platform that answers all six - and introduces the architectural capability no other platform has attempted: personal agent collaboration within a shared business context.
Why Most AI Systems Aren't Actually Agents: A Taxonomy of What Qualifies - and the Architecture Gap That Explains the Reliability Problem
Everyone calls everything an 'agent' now. A research-backed look at what actually qualifies - from session assistants to autonomous systems - and why even the most capable agents in production today still fall short of enterprise-grade reliability.
Why AI Agents Still Disappoint: The Gap Between Automation and Intelligence
People don't actually want pure AI agents. They want deterministic systems that reason when they need to. That product doesn't exist yet - and here's why every current option falls short: n8n, MAF, Claude Cowork, and the developer frameworks.
Why Your AI Forgets You Every Time (And What Real Memory Actually Looks Like)
ChatGPT and Claude both have memory features now. But personal continuity memory and agent working memory are solving completely different problems. Here's the full landscape in 2026: semantic/vector, graph, episodic, observational, and validated knowledge base architectures - and what to look for when evaluating AI systems built on any of them.