Topic
Agents & Computer Use
Agent workflows, browser control, memory, loops, frameworks, and customer-facing voice flows.
33 stories (14 articles · 19 videos)
Start here
A few good first pieces before you browse the full feed.
11 min readArticle
Build your first AI agent in n8n: a lead-triage workflow end-to-end
A complete walk-through of building a real AI agent in n8n — one that triages incoming leads, enriches them, scores them, and routes them. Every node, every prompt, every gotcha.
Design a lead-triage agent with explicit tools, schemas, routing rules, logging, and human review.
Intermediate
11 min readArticle
Browser agents and computer use: what they can actually do today
Browser agents and computer-use AI promise to operate your computer the way you do. The reality in 2026 is more useful and more limited than the demos suggest. A grounded guide to what works, what doesn't, and where to apply them.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Intermediate
13 min readArticle
Designing agents that don't loop forever
The most common production agent failure is infinite or pseudo-infinite loops — agents that retry, branch, and burn through tokens without making progress. The architectural patterns that prevent this and produce agents that finish, even on hard tasks.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Advanced
More in this topic
6 minutesVideo
AI Voice Agents: How They Actually Work & Why They Sound So Human
CX Foundation. Breaks voice agents into the practical pipeline: speech recognition, language model, business-system APIs, text-to-speech and interruption handling. That gives the article's rollout framework a concrete technical foundation before readers choose Twilio, Retell, Vapi, LiveKit or another platform.
Advanced
9 min readArticle
AI-native IDEs and repository-aware coding workflows
Cursor, Copilot, Claude Code, and repository-aware agents change software work only when teams add boundaries. A practical workflow for codebase context, planning, tests, review, secrets, and production safety.
Design a repository-aware AI coding workflow that improves delivery speed without weakening review, security, tests, or ownership.
Advanced
9 min readArticle
Voice agents for customer flows: where they work and where they fail
Voice agents are useful when the flow is bounded, the data is available, and the fallback is clean. A practical decision framework for Twilio/Retell-style systems, disclosure, handoff, testing, and rollout.
Decide whether a customer voice agent is appropriate and design the first rollout with disclosure, escalation, testing, and monitoring.
Advanced
12 min readArticle
Computer use and browser agents in production
Computer use and browser agents have demos that go viral. Production deployments at scale have a different shape — narrow scoping, heavy guardrails, careful UX. The patterns that work, the failures we keep seeing, and the honest economics.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Advanced
12 min readArticle
Building memory for long-running agents
Agents need memory beyond the context window. Long-term memory architecture — what to store, when to retrieve, how to forget — determines whether agents feel like they 'know' you or start fresh every conversation. The patterns and the production trade-offs.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Advanced
11 min readArticle
LangGraph vs CrewAI vs direct API: choosing an agent framework in 2026
The agent framework landscape in 2026 is more mature but no clearer. LangGraph, CrewAI, Pydantic AI, OpenAI Agents SDK, and direct API — each fits some teams and projects, none fits all. A honest comparison and a decision framework.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Advanced
12 min readArticle
RAG beyond chunks: graph RAG, agentic RAG, long-context RAG
Classic chunk-based RAG has limits. Graph RAG, agentic RAG, and long-context RAG each break those limits in different ways. When each is the right tool, how they actually work, and the production trade-offs that matter.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Advanced
10 min readArticle
Building an always-on briefing or newsletter with AI
An automated daily briefing or newsletter that arrives in your inbox, with content actually worth reading, is one of the highest-leverage AI builds. The architecture, the prompts, and the discipline that makes it sustainable.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Intermediate
10 min readArticle
MCP for the non-engineer: connect Claude or Cursor to your tools
MCP is the new standard for connecting AI to your tools. You don't need to write one to benefit. A non-engineer's guide to what MCP is, which servers to install, and what becomes possible once your AI can actually act.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Intermediate
10 min readArticle
Connecting AI to your email, calendar, and CRM safely
Connecting AI to your real tools — email, calendar, CRM — is the productivity unlock and the risk. A practical guide to the integrations that work in 2026, the patterns that are safe, and the lines you should not cross.
Connect AI to email, calendars, and CRMs with least privilege, approval gates, and audit trails.
Intermediate
11 min readArticle
The AI customer support agent that resolves 70% of tickets
A realistic design for an AI customer support agent that resolves the common cases, escalates the hard ones, and doesn't make the kind of mistake that ends up on Hacker News. The architecture, the prompts, the guardrails.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Intermediate
9 min readArticle
n8n vs Zapier vs Make: picking the right automation stack
An honest comparison of the three main automation platforms for AI workflows in 2026. What each is good at, where each breaks, and the decision rules for choosing without regret.
Evaluate the implementation pattern, failure modes, and guardrails before building.
Intermediate
8 minutesVideo
Anthropic's Claude Computer Use Is A Game Changer | YC Decoded
Y Combinator. Garry Tan walking through what computer use actually changes for the unautomatable long tail of software — legacy apps, internal portals, anything without an API. The framing here is exactly the article's "browser is the universal interface" argument, with a more business-realistic view of where it pays off first.
Advanced
5 minutesVideo
Claude has taken control of my computer...
Fireship. The clearest short explanation on YouTube of the screenshot–action–screenshot loop, including the honest failure modes (Claude wandering off to look at Yellowstone, token burn, latency per step). Fireship is light on production detail by design — read the article for that — but it leaves you with the right intuition for why these systems are expensive and brittle before you commit one to your stack.
Advanced
44 minutesVideo
Building Brain-Like Memory for AI | LLM Agent Memory Systems
Adam Lucek. A longer implementation pass through the cognitive-science-inspired categories — episodic, semantic, working, procedural — wired into an agent in code. Worth watching after the LangChain conceptual video if you want a more opinionated mental model and a working example to crib from.
Advanced
7 minutesVideo
Memory for agents (conceptual video)
LangChain. Short, no-code walkthrough of the short-term-vs-long-term split, the three shapes long-term memory tends to take (instructions, profile, list of objects), and the hot-path-versus-background trade-off for when to write. The article's memory-architecture section assumes exactly this taxonomy.
Advanced
66 minutesVideo
CrewAI Tutorial: Complete Crash Course for Beginners
aiwithbrandon. The same kind of build, but in CrewAI's role-goal-backstory style — agents as team members, tasks as deliverables, the framework hiding the execution loop. Watch it immediately after the LangGraph course; the contrast in how much the framework decides for you is exactly what the article is asking you to weigh.
Advanced
190 minutesVideo
LangGraph Complete Course for Beginners – Complex AI Agents with Python
freeCodeCamp.org. A long, code-along build through LangGraph's state graphs, nodes, edges, conditional routing, checkpoints, and tool use. By the end you have enough feel for the typed-state, "every transition is explicit" model that the article's comparison to CrewAI and to direct-API code stops being abstract.
Advanced
18 minutesVideo
Tips for building AI agents
Anthropic. Three Anthropic engineers walking through the most common pitfalls they see — agents that don't know when to stop, over-prompting in the system prompt instead of fixing the environment, the cost of multi-agent designs nobody actually needed. Useful right after Barry's talk; you will recognise the same patterns from a different angle.
Advanced
15 minutesVideo
How We Build Effective Agents: Barry Zhang, Anthropic
AI Engineer. Barry Zhang on three rules — don't build an agent when a workflow would do, keep the loop as simple as possible, and "think like your agent" (sit in its context window and notice that it is making decisions in the dark between screenshots). The simplicity argument and the "is this task even worth an agent" checklist are exactly the discipline the article asks for.
Advanced
2 minutesVideo
Claude | Computer use for orchestrating tasks
Anthropic. A two-minute Anthropic demo of Claude planning a small multi-app task — search the web, check Maps, drop a calendar invite — by driving the desktop directly. Useful contrast to Operator's cloud-browser-only model and a good gut check on the article's point that computer-use agents work best on short, well-bounded chores rather than open-ended work.
Intermediate
24 minutesVideo
Introduction to Operator & Agents
OpenAI. The actual launch demo of Operator, where the team books restaurants, orders groceries, buys event tickets, and lets Operator stall on a redirect or hand control back when it hits a login. It is the clearest picture you will find of how a browser agent feels in practice — the screenshot-and-click loop, the confirmations before "stateful" actions, the prompt-injection guard rails — which is exactly the texture the article is trying to set expectations for.
Intermediate
20 minutesVideo
The Model Context Protocol (MCP)
Anthropic. The protocol's designers — Theo Chu, David Soria Parra and Alex Albert — walking through why MCP exists, the components (server, client, transport), the reception since the November 2024 release, and which servers they actually use day-to-day. Useful as the canonical source after the Nate Herk overview.
Intermediate
16 minutesVideo
How MCPs Make Agents Smarter (for non-techies)
Nate Herk | AI Automation. A 16-minute, no-jargon explanation of what an MCP server is, how clients like Claude and n8n use them, and what you actually do differently once you have one. Lines up almost exactly with the article's "connect Claude or Cursor to your tools" framing.
Intermediate
25 minutesVideo
OWASP's Top 10 Ways to Attack LLMs: AI Vulnerabilities Exposed
IBM Technology. Zooms out from prompt injection to the wider OWASP Top 10 for LLMs — insecure output handling, sensitive information disclosure, excessive agency — which is exactly the failure-mode catalogue you want in mind before you grant Gmail or HubSpot scopes to anything.
Intermediate
11 minutesVideo
What Is a Prompt Injection Attack?
IBM Technology. Jeff Crume's "buy an SUV for $1" example is the cleanest 10-minute explanation of why direct and indirect prompt injection are different problems, and why filtering can't fully solve either. It pairs directly with the article's argument that you need least-privilege scopes, a dedicated agent account, and a human in the loop on anything irreversible — not a cleverer system prompt.
Intermediate
31 minutesVideo
How To Build An AI Customer Support Agent with n8n (free template)
Bart Slodyczka. A practical n8n workflow that plugs into Zendesk, Gorgias or Freshdesk, replies to tickets with a RAG-backed answer, and feeds solved tickets back into the knowledge base. Closest match on YouTube to the architecture the article describes.
Intermediate
231 minutesVideo
How to Build & Sell AI Agents: Ultimate Beginner's Guide
Liam Ottley. Customer support agents are Liam's bread-and-butter use case, and a sizeable chunk of this course is given over to RAG-grounded chatbots, escalation logic, and the no-code stack he uses with real clients (Botpress, Voiceflow, Make, n8n). The "anatomy of an agent" section in particular maps almost one-to-one onto the article's triage → answer → action → handoff structure.
Intermediate
92 minutesVideo
n8n Masterclass: Build AI Agents & Automate Workflows (Beginner to Pro)
Nate Herk | AI Automation. Nate is one of the most-watched practitioners on n8n specifically, and this masterclass goes deeper than the Futurepedia primer — memory, multi-agent setups, error handling, real business workflows. Reach for it once your lead-triage agent works and you want to extend it.
Intermediate
26 minutesVideo
From Zero to Your First AI Agent in 25 Minutes (No Coding)
Futurepedia. Builds a working n8n agent from scratch in one sitting and stops to explain what an agent actually is, how it differs from a linear workflow, and where the guardrails go. After this you'll recognize every node in the article's diagram and have a feel for what reasonable defaults look like.
Intermediate