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This Completely Changes the Way We Build Production AI Agents (Vercel Eve)
Cole Medin
Vercel har udgivet et nyt open source AI-agentframework kaldet Eve, der bygger på en "file system first"-tilgang, hvor en hel AI-agent defineres som en mappestruktur med separate undermapper til systemprompter, færdigheder, MCP-servere, underagenter, kanaler og tidsplaner. Et kompileringstrin sørger automatisk for at koble alle disse elementer sammen, uden at udvikleren selv skal importere eller sammenkæde dem manuelt. Eve er designet til produktionsbrug i stor skala og tilbyder holdbare sessioner med checkpoint-genoptagelse, isoleret sandboxing til kodeudførelse, "human in the loop"-godkendelse og evals som deployment gate – alt understøttet af Vercels infrastruktur. Værten fremhæver Eve som en potentiel branchestandard for filsystembaserede agenter og ser det som et naturligt komplement til andre åbne standarder som MCP og OKF.
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Agent AI System Design Explained in 27 Minutes
Aishwarya Srinivasan
This video provides a comprehensive systems-design overview of production-grade agentic AI systems, going beyond basic LLM concepts. The presenter covers the core building blocks: model routing (using cheaper models for simple steps, stronger models only for complex reasoning), tool design (strict input/output contracts, permission boundaries, and separating read vs. write operations), and memory/state management (choosing the right storage—Redis, vector DBs, object storage—based on access patterns rather than defaulting to a vector database). Additional topics include orchestration (graph-based control flows, state machines, and explicit routing for multi-agent systems), trace-level evaluation (assessing each step of the agent's trajectory, not just the final output), and approval gates for high-risk actions. The video concludes with production principles covering reliability, cost/latency optimization through model routing and caching, RAG/context design, observability (logging every step's tokens, latency, and errors), and security/privacy (treating all model inputs as potentially adversarial and minimizing PII sent to models).
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You’re Not Behind (Yet): How to Build Your First AI Agent (Full Guide)
Dan Martell
Dan Martell explains the difference between AI chatbots (reactive, single-response tools) and AI agents (autonomous systems that diagnose, plan, execute, and self-assess in a continuous loop), arguing that agents can take over entire workflows rather than just answering questions. He introduces two frameworks: the "Rule of R" (Repetitive, Rules-based, Return on time) to decide whether a task is worth automating, and the "AGENT" acronym—Aim for a specific outcome, Give it an identity, Equip it with context and tools, Narrow the scope using sub-agents, and Trust it in stages. Using an inbox-management agent as a running example, he walks through creating three identity files (soul, identity, user), reverse-engineering workflows from historical data, assigning specialized sub-agents coordinated by a manager agent, and gradually granting the agent more autonomy only after verifying its outputs at each step.
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She Staffed Her Profitable Startup With AI Agents | Claire Vo, ChatPRD
Solo Founders
Claire Vo, solo founder of ChatPRD, built a profitable AI-powered product management tool while working as a CPTO at companies like Color Health and Launch Darkly, only going full-time on it about a year ago. The product originated when she used early ChatGPT to write a detailed product spec in under three hours, then monetized it by simply placing a Stripe checkout between a landing page and a publicly available GPT. ChatPRD remains fully bootstrapped with no outside investment, and Vo runs it entirely with the help of a constellation of nine AI agents (marketing, EA, sales, support, etc.) rather than human employees. She attributes her ability to do this solo to two decades of accumulated product expertise, network, and distribution, cautioning others that bootstrapping this way requires significant pre-built resources and isn't easily replicable without them.
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AI AGENTS FULL COURSE 8 HOURS: Build & Sell (2026)
Albert Olgaard
En 8-timers fuldstændig kursus om AI-agenter, der guider seerne gennem at bygge og sælge AI-agentbaserede løsninger frem mod 2026. Kurset dækker sandsynligvis praktiske metoder til at udvikle autonome AI-systemer samt strategier for at monetisere og sælge disse løsninger som produkter eller services.
summary from description (full transcript skipped)
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AI Agents That Small Businesses Actually Need in 2026 (Claude)
Jake One Page
Videoen argumenterer for, at de fleste udviklertutorials om AI-agenter er irrelevante for ejere af små virksomheder, fordi de fokuserer på teknisk opsætning som kode, API-nøgler og terminaler frem for praktiske forretningsresultater. Som løsning præsenteres platformen Base 44, der lader brugere bygge og implementere AI-agenter ved hjælp af naturligt sprog uden kode eller komplekse integrationer. Værten demonstrerer live, hvordan man bygger tre konkrete forretningsagenter: en leadgenereringsassistent til hjemmesider, en præstationsovervågningsagent og en operationel koordineringsagent – alle designet til at kunne sælges som tilbagevendende månedlige services til lokale virksomheder.
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95% Of "AI Agents" Are Fake
Julia McCoy
Ifølge Julia McCoy er 95 % af det, der sælges som "AI-agenter", i virkeligheden blot chatbots eller automatiseringsværktøjer med et nyt navn – et fænomen hun kalder "agent washing". Hun præsenterer data fra Gartner, MIT og S&P Global, der viser, at over 40 % af agentiske AI-projekter forventes aflyst inden 2027, og at fejlraten ikke skyldes teknologien, men at grundlæggere køber falske agenter og springer vigtige forberedelsestrin over. McCoy anbefaler at stille leverandører kritiske spørgsmål – bl.a. om agenten kan træffe beslutninger uden forhåndsgodkendelse – og understreger, at man skal bygge i rækkefølgen: data, indhold, CRM, arbejdsgange og til sidst agenten. Grundlæggere, der bygger det underliggende system korrekt nu, vil ifølge hende være dem, der vinder, når fejlbølgen rammer i 2027.
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5 AI Myths & The Truth Behind Them: ML, Context, Agents & More
IBM Technology
Videoen gennemgår fem udbredte myter om AI og nuancerer dem med fakta. Myte 1 handler om hallucinationer: moderne frontiermodeller hallucinerer nu kun ca. 3% af tiden takket være værktøjsbrug, kalibreret afvisning og ræsonnementsmodeller. Myte 2 afklarer, at man ikke reelt "ser AI tænke" – ræsonnementssporet er en post hoc-rationalisering og ikke et præcist billede af modellens interne beregning. Myte 3 viser, at det ikke længere er træning, men inferens, der dominerer AI-beregningsomkostningerne og forventes at udgøre to tredjedele i 2025. Myte 4 forklarer, at store kontekstvinduer ikke fungerer som databaser, da modellerne klarer sig godt ved at finde én enkelt information, men fejler markant ved at sammenkæde information spredt over mange tokens. Myte 5 slår fast, at AI-agenter endnu ikke kan arbejde fuldt autonomt over længere forløb på grund af sammensatte fejl, og at "human in the loop" stadig er nødvendigt.
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I Tried Making $800 in 4 Hours with AI Agents (To See If It Works)
Mikey No Code
This video follows a self-described beginner who attempts to build an AI-powered booking and client management system for a hair salon using a no-code tool called Base44, with the stated goal of creating something capable of generating $800 in four hours. The creator walks through building a virtual receptionist agent that answers common inquiries, connects to Google Sheets and Gmail for automated booking confirmations, and sets up a Telegram bot to send appointment reminders and follow-up messages to reduce no-shows. Additional integrations covered include Google Calendar for real-time availability checking and Stripe for collecting deposits to reduce last-minute cancellations. The video frames the system as applicable beyond salons to any appointment-based business, and serves largely as a promotional vehicle for the creator's paid Base44 masterclass, which is offered free to viewers via a description link.
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