Agent Memory is a Cloudflare private beta service for letting agents remember users, teams, projects, and task context across conversations. It fits facts, events, instructions, and tasks; RAG documents, product data, files, and audit logs should still live in AI Search, Vectorize, D1, or R2.
Cloudflare Agents turns an agent session into a durable runtime: each agent instance has stable identity, local SQLite, WebSockets, scheduled work, recoverable execution, and tools. It is not just a chat example; it composes Workers, Durable Objects, AI models, Browser, Sandbox, AI Search, and MCP into a deployable agent app.
An AI app should not put conversations, artifacts, memory, retrieval documents, locks, and eval traces into one store. D1 fits queryable product data, R2 fits large files and artifacts, Durable Objects fit named coordination and per-session state, and Agent Memory / AI Search / Vectorize handle memory and retrieval.
The Cloudflare AI Stack series covers the infrastructure around AI apps: where models run, how gateway control works, how RAG is built, how agents keep running, how memory is governed, and how browser, sandbox, secrets, data, and observability fit into a product.
Browser Run gives Workers access to Cloudflare-managed headless Chrome. Quick Actions fit one-shot tasks such as screenshots, PDFs, HTML, JSON, and crawls; Browser Sessions fit Puppeteer, Playwright, CDP, and Stagehand automation where you need full control.
Lecture 12 moves from perplexity to exams, chat preferences, agents, reasoning, and safety. Every benchmark changes the capability definition, scaffold, judge, and contamination risk, so evaluation must first say whether it compares a method, model, or complete system.
Across Microsoft's four AI/agent certifications, only AI-103 → AI-500 is an official ladder; everything else is positioning. Three forks decide it: whether you write Python (AI-103/AI-500 vs AB-620), whether you build or judge (AB-100 vs the rest), and whether you can actually start today — AI-500's four official learning paths currently 404, AB-620 has no practice test, AB-100 has a free one. For readers who prefer Chinese there is a fourth fork: AI-103 and AB-620 offer Traditional Chinese, AI-500 and AB-100 are English only. All four cost $165, expire after one year, and renew free but only inside a six-month window.
NVIDIA's generative AI line has four exams: NCA-GENL and NCA-GENM ($125 each, associate), NCP-GENL and NCP-AAI ($200 each, professional). Three decision inputs no other vendor forces on you. One: both professional exams still show 'Coming soon' next to Register, so any near-term plan is down to the two associates. Two: NVIDIA is the only vendor in this series whose official prep courses are all paid — real cost is exam fee plus courses, and the self-paced totals are $390 (NCA-GENL), $210 for only three of five courses (NCA-GENM), and $1,620 list price across NCP-GENL's five. Three: the official documents disagree with themselves — NCP-AAI's weights total 98% on the web page and 92% in the PDF, and two cells of NCP-GENL's web table carry misplaced text, one of it about OpenUSD. Lock-in also varies sharply: NCP-AAI is 7% NVIDIA-specific, NCP-GENL is 31% GPU and model-compression work.
AIP-C01 is AWS's only professional-level certification dedicated to GenAI application development, and it explicitly excludes model development, advanced ML, and feature engineering — it tests integrating someone else's foundation model into a production system. Domain weights are 31/26/20/12/11, and nearly half the skills in the heaviest domain sit in vector stores and RAG. A March 2026 refresh added Bedrock AgentCore and the beta ended March 31, so anything older is stale. Official specs: $300, 180 minutes, 75 questions (65 scored), pass at 750, valid 3 years — and passing it renews AIF-C01, MLA-C01, and Data Engineer – Associate at the same time.
CCDV-F is the engineer's exam among Anthropic's four certifications. The official blueprint has eight domains, and the heaviest — Applications and Integration at 33.1% — is led by Claude Application Design (8.6%) and Software Engineering Foundations (7.4%), meaning a third of the exam is API mechanics and ordinary software engineering. The counterintuitive part: Claude Code is only 3.1% and Eval only 2.6%, while the sibling Architect exam gives Claude Code 20%. Official specs: $125, 53 items, 120 minutes, pass at 720, valid 12 months, registration limited to Claude Partner Network organizations.
AB-100 is the architect tier of Microsoft's agent line, weighted 25-30 / 25-30 / 40-45 with deployment and governance heaviest. Its outline runs on verbs like design, recommend, and propose — it tests judgment, not configuration. Three things to know first: the scope blurb on the official exam page is wrong (it is information-protection and DLP boilerplate, which I verified verbatim), so prepare from the study guide instead; it has a free practice assessment, the only one of Microsoft's three agent credentials that does; and the 15 associate certifications it lists are described as usable, not required. Official specs: $165, English only, pass at 700, one-year validity.
AB-620 is the low-code branch of Microsoft's agent certification line — it tests agent flows, adaptive cards, computer use, MCP tools, A2A, and Fabric data agents in Copilot Studio, not Python. The three skill areas weigh 30-35 / 40-45 / 20-25, with integration the heaviest. Official specs: $165, 120 minutes, pass at 700, one-year validity, and 13 languages including Traditional Chinese — the only localized exam of Microsoft's three agent credentials. It is generally available, but the practice assessment is not out yet.
AI-103 replaces AI-102, retired June 30, 2026, and the objectives were rewritten around Microsoft Foundry — prompt flow, Azure AI Studio, Azure OpenAI Service, and Azure AI Agent Service appear nowhere in them. The five skill areas weigh 25-30 / 30-35 / 10-15 / 10-15 / 10-15, with generative AI and agents the largest. Official specs: $165, 120 minutes, pass at 700, offered in Traditional Chinese, may include interactive components — and it is valid for only one year, though renewal is a free, open-book, unproctored online assessment.
AI-500 is a rare thing among the major clouds — an expert-level certification dedicated to multi-agent systems, weighted 15-20 / 30-35 / 20-25 / 20-25, naming Agent Framework, LangGraph, Hugging Face Transformers, MCP servers on Azure Functions / Logic Apps / API Management, A2A, Key Vault, and the AI Red Teaming Agent. Three constraints come first, though: it is still in beta (scores wait for rescoring), it requires AI-103 before you can take it, and the official training is not live — the four learning paths listed on the exam page all return 404 today, and the instructor-led course opens 2026-09-30.
NCP-AAI is NVIDIA's professional-level agentic AI credential — $200, 120 minutes, 60–70 items, two-year validity. Two things come first: registration is not open (the Register button carries a 'Coming soon' label), and NVIDIA's own web page and PDF study guide disagree on the weights — Deployment and Scaling is 13% on the page and 5% in the PDF, Run/Monitor/Maintain is 5% on the page and 7% in the PDF, and the two versions total 98% and 92% respectively. Both are nvidia.com. This guide treats that as a range and an uncertainty rather than picking one.
The hard part of LLM agents is not building function calling, skills, code interpreter, and document tools individually -- it is assembling them into a system that selects the right tool, writes code when needed, decomposes tasks, verifies results, and resists prompt injection. This post organizes the key papers into six engineering decisions: function calling reliability, tool/skill selection, code-as-action, multi-step planning, skill systems, and safety plus document generation.
Anthropic open-sourced 12 financial-industry Agents and 11 MCP connectors. The real takeaway isn't the Agents themselves but the layered design of 'one prompt, two runtimes' and 'pure-file extensibility.'