# AI Agent An AI agent is a goal-directed software system that autonomously plans and executes tasks by dynamically using tools and evaluating results. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Why human oversight is shifting from writing code to defining requirements DevFeed: [Why human oversight is shifting from writing code to defining requirements]() Original publisher: [Read original article]() Author: Naseeb Ahmed Mian Published: 2026-09-17T13:00:00Z Content type: opinion Language: en Sources: [The New Stack]() Topics: [Requirements](), [AI Agent](), [Code](), [Availability]() Tags: [agent](), [agents](), [ai-agents](), [ai-engineering](), [andela](), [automated](), [availability](), [code](), [pipeline](), [post-contributed](), [requirements](), [review](), [software-development](), [sponsor-andela](), [sponsored-post-contributed](), [tests]() ### AI overview The article argues that human oversight of AI-assisted software development should focus on defining and reviewing requirements, not only checking whether generated code conforms to them. It illustrates the risk with a flawed availability-related requirement that passed specification review, generated six passing tests, traceability checks, and automated QA while violating the feature's intended outcome. ### Source excerpt This walks through the pipeline our agents operate inside--from a recorded scoping meeting through unit specs, spec review, generated code, The post Why human oversight is shifting from writing code to defining requirements appeared first on The New Stack. ## Build Your Own AI Agent Harness in C#, the MafClaw Live Series DevFeed: [Build Your Own AI Agent Harness in C#, the MafClaw Live Series]() Original publisher: [Read original article]() Author: Bruno Capuano Published: 2026-09-16T21:00:00Z Content type: tutorial Language: en Sources: [.NET Blog]() Topics: [Agent Harness](), [Microsoft Agent Framework](), [AI Agent](), [C#](), [.NET]() Tags: [agent](), [agent-framework](), [agent-harness](), [ai](), [building](), [c-sharp](), [cloud](), [csharp](), [microsoft-agent-framework](), [microsoft-reactor](), [net](), [observability]() ### AI overview This article introduces a four-part live series that builds a complete C# AI agent using the Microsoft Agent Framework harness. It describes adding tools, planning, file access, approvals, memory, skills, shell commands, code execution, background agents, observability, governance, evaluations, and hosted deployment. ### Source excerpt I am building a complete C# agent live, from a single call around an IChatClient to a production-ready, observable, governed agent, using the Microsoft Agent Framework harness in a 4-part Microsoft Reactor series. The post Build Your Own AI Agent Harness in C#, the MafClaw Live Series appeared first on .NET Blog. ## Platform Engineering in the Age of AI DevFeed: [Platform Engineering in the Age of AI]() Original publisher: [Read original article]() Author: Nicole Morgan Published: 2026-09-16T20:28:57.610955Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [Platform Engineering](), [Artificial Intelligence](), [AI Agent](), [internal developer portal](), [API](), [Deployment](), [sdlc]() Tags: [agent](), [ai](), [api](), [deployment](), [developer-experience](), [engineering](), [metrics](), [platform](), [sdlc]() ### AI overview This commentary examines how platform engineering is adapting as AI coding tools and agents become part of software delivery. It discusses the build-versus-provide decisions facing platform teams, how agents may consume internal developer platforms through APIs, the need for guardrails, and the challenge of measuring the results of AI investment. ### Source excerpt 94% of engineering leaders say their AI metrics are missing. Here's how platform engineering is changing to close that gap. | Blog ## AI Agent Governance: Why It Belongs in Your Platform DevFeed: [AI Agent Governance: Why It Belongs in Your Platform]() Original publisher: [Read original article]() Author: Prateek Mittal Published: 2026-09-16T20:28:57.610955Z Content type: article Language: en Sources: [Harness Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [observability](), [audit](), [test](), [Deployment](), [Monitoring](), [pull-requests]() Tags: [ai](), [ai-agent](), [audit](), [deployment](), [governance](), [monitoring](), [observability](), [pull-requests](), [test]() ### AI overview Engineering leaders from Yelp, Platformable, and Harness argue that AI agent governance must be automated, continuously enforced, and built into the platform rather than left to policy documents. The article discusses audit trails, agent-to-agent access controls, experiment tracking, testing, latency monitoring, rollback paths, and observability for agent-driven changes. ### Source excerpt Engineering leaders from Yelp, Platformable, and Harness explain why AI agent governance has to be built into the platform, not a policy doc. | Blog ## Translating CUDA Tile Operations from Python to Rust Using Agentic AI DevFeed: [Translating CUDA Tile Operations from Python to Rust Using Agentic AI]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T16:28:59Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA Tile](), [Rust](), [Agent Skill](), [Artificial Intelligence](), [AI Agent](), [GPU](), [benchmarking](), [Compiler]() Tags: [agent-skill](), [agentic-ai](), [agentic-ai-generative-ai](), [ai-agent](), [benchmarking](), [cuda](), [cuda-tile](), [cutile](), [developer-tools-techniques](), [gpu](), [mixture-of-experts-moe](), [python](), [rust]() ### AI overview This NVIDIA developer article explains a multi-agent workflow for translating cuTile Python and Triton-TileIR GPU kernels into cuTile Rust. The team ported 24 public TileGym operators, covering about 40 kernels, and achieved 99.5% of cuTile Python performance on average, with correctness and performance checks at each stage. ### Source excerpt cuTile Rust (cutile-rs) is a tile-based system for safe, idiomatic GPU kernel authoring in the Rust programming language. Extending the Rust ownership model to... ## Muse review: The personal AI agent that gets consumer UX right DevFeed: [Muse review: The personal AI agent that gets consumer UX right]() Original publisher: [Read original article]() Author: Claire Vo Published: 2026-09-16T12:02:14Z Content type: opinion Language: en Sources: [Lenny's Newsletter]() Topics: [AI Agent](), [User experience (UX)](), [Meta](), [browser](), [pdf](), [codex]() Tags: [agent](), [ai](), [ai-agent](), [browser](), [codex](), [comparison](), [meta](), [muse](), [pdf](), [product-design](), [ux]() ### AI overview This review tests Meta's Muse personal AI agent through onboarding, calendar management, goal setting, document generation, and browser-based shopping. It finds strong consumer UX, permission handling, activity-feed design, and avatar design, while browser shopping performance is mixed. ### Source excerpt Watch now (38 mins) | 🎙 I gave Meta's Muse my calendar, my email, and my kids' chaotic schedule, and it produced a one-shot family PDF more beautiful than anything I've ever made with Claude or Codex ## Build a WhatsApp AI agent with Appwrite Functions and TablesDB DevFeed: [Build a WhatsApp AI agent with Appwrite Functions and TablesDB]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [AI Agent](), [Messaging](), [Serverless](), [API](), [Database]() Tags: [ai](), [ai-agent](), [api](), [app](), [build](), [data](), [database](), [messages](), [messaging](), [meta](), [serverless](), [support](), [tutorial](), [tutorials](), [whatsapp]() ### AI overview This tutorial shows how to build a WhatsApp AI support agent with Appwrite Functions and TablesDB. One function receives and stores incoming messages, while a second function retrieves conversation history, uses an order-lookup tool, sends replies through Meta's WhatsApp Cloud API, and stores the responses. Conversation summaries help keep long threads within the model's token budget. ### Source excerpt Turn a WhatsApp number into an AI support agent. Two Appwrite Functions receive messages and reply, TablesDB keeps the conversation history, and a compaction step keeps the context small. ## How to test an AI agent policy before enforcing it DevFeed: [How to test an AI agent policy before enforcing it]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T22:03:24Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [AI Agent](), [Authorization](), [Testing](), [Access Control](), [Linear]() Tags: [access-control](), [ai-agent](), [authorization](), [how-to](), [testing]() ### AI overview This guide explains how to test authorization policies for AI agents before enforcing them. It presents reusable tests for role-based access control, task intent, organizational rules, and approval requirements, with checks against the connected service. ### Source excerpt Design and test AI agent policies that block unsafe actions and keep useful work moving. Learn to test intent, RBAC, and approvals, or try WorkOS Airlock. ## Who gets to define an AI agent's intent? DevFeed: [Who gets to define an AI agent's intent?]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T20:25:55Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [AI Agent](), [Artificial Intelligence]() Tags: [ai](), [ai-agent](), [identity](), [policy](), [scopes](), [tool](), [workflow]() ### AI overview The article explains how to define an AI agent's intent while preserving identity, resource-access controls, and company policy. It argues that the requester should specify the agent's purpose and intended effects, while the organization determines which actions are permitted. Using Airlock examples, it distinguishes task intent from policy decisions such as approval or denial of specific operations. ### Source excerpt An agent's task describes the work to do. Learn how to define useful intent while keeping identity, resource access, and company policy in force. ## Beyond the model: Engineering AI infra with scientific judgement DevFeed: [Beyond the model: Engineering AI infra with scientific judgement]() Original publisher: [Read original article]() Author: AirbnbEng Published: 2026-09-15T17:06:18Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Agent Harness](), [AI Agent](), [Data Science](), [data](), [datasets](), [Large Language Model]() Tags: [agent-harness](), [ai](), [ai-agent](), [data](), [data-science](), [datasets](), [engineering](), [llms](), [quality](), [science](), [trust]() ### AI overview Airbnb describes an agent harness for data science that embeds scientific methodology around an AI model. The system guides agents through framing questions, selecting evidence, and recording decisions so unstructured-data investigations can be reproduced, audited, challenged, and extended across languages, geographies, and LLM-based products. ### Source excerpt How Airbnb's agent harness transforms unstructured data exploration by encoding scientific methodology into scalable, reproducible, and audit-ready infrastructure. By: Wren Dougherty Ask a coding agent to analyze 100,000 customer support conversations and within minutes you'll have a polished taxonomy, precise prevalence numbers, and an executive-ready summary. What you can't see is the investigation that produced them: the methods it chose, the evidence it weighed, how much to trust it, or whether a second request would agree. All that reaches you is the polish. The model is undeniably intelligent, but intelligence without methodology is not science. LLMs certainly make for confident scientists, but we need them to be responsible ones. Smarter models help, but intelligence has never been the whole of science, in people or in machines. The method is as much the product as the answer. That is the idea behind the agent harness we built for data science: the methodology itself, built as infrastructure around the model. It governs how an AI agent operates, from framing a question to selecting evidence to recording decisions, so results can be reproduced, audited, and challenged, and the method shared, inspected, and built on. The challenge of unstructured data exploration In 2025, Airbnb was preparing to launch an AI customer service assistant. Before it could ship, we needed to understand exactly what kinds of situations it would face in the real world. That included rare events that could be risky for AI to interact with, and involved examining their taxonomy and prevalence to create the datasets that would help us build a more responsible product. The investigative work to do this was rigorous, but the process was deeply artisanal. Months of high-touch iteration went into each investigation, from finding the right data, reviewing samples with experts, and generating representative datasets, and the method was manually curated across notebooks, tables, docs, and indiv ## AI agent approval workflows: handling edits and retries DevFeed: [AI agent approval workflows: handling edits and retries]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T15:02:39Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [AI Agent](), [Authorization](), [Tool]() Tags: [agent](), [authorization](), [execution](), [how-to](), [retry](), [review](), [server](), [tool](), [workflows]() ### AI overview This guide explains how to add human approval to AI agent tool calls with WorkOS Airlock. It covers binding approval to an exact request, persisting the proposed action, resuming execution after approval, and recovering when a provider response is uncertain. ### Source excerpt Add human approval to AI agent tool calls. Learn how to review exact requests, resume execution, and handle edits and retries with Airlock. ## DigiCert's AI Trust framework for governing enterprise AI agents and models DevFeed: [DigiCert's AI Trust framework for governing enterprise AI agents and models]() Original publisher: [Read original article]() Author: Robin Birtstone Published: 2026-09-15T15:00:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [ai-governance](), [AI Agent](), [Authorization](), [Security](), [data]() Tags: [agents](), [ai](), [ai-governance](), [authorization](), [data](), [security](), [sponsored]() ### AI overview This sponsored feature presents DigiCert's AI Trust framework for governing enterprise AI agents. The framework uses public key infrastructure, DNS, and attestation to help organizations identify agents, track data and credentials, stop compromised agents, and reconstruct incidents. ### Source excerpt SPONSORED FEATURE: DigiCert wants to hand every agent a passport, complete with an expiry date and a named human owner ## Announcing Meta Business Messaging MCP: Set up and manage WhatsApp Business from your AI agent DevFeed: [Announcing Meta Business Messaging MCP: Set up and manage WhatsApp Business from your AI agent]() Original publisher: [Read original article]() Published: 2026-09-15T14:00:49Z Content type: release Language: en Sources: [Facebook Developers]() Topics: [Model Context Protocol](), [MCP Server](), [AI Agent](), [Messaging](), [Meta](), [API]() Tags: [agent](), [ai-agent](), [api](), [mcp](), [mcp-server](), [messaging](), [meta](), [testing](), [whatsapp](), [workflow]() ### AI overview Meta announces Business Messaging MCP, an MCP server that connects AI coding agents to the WhatsApp Business Platform. It supports account and phone-number setup, message templates, webhooks, and test messages within the developer workflow. ### Source excerpt Business Messaging MCP connects your AI coding agent to the WhatsApp Business Platform, so you can create accounts, add numbers, build templates, and send test messages without leaving your workflow. ## Grab's LLM-Kit Framework Standardizes More Than 500 Internal Agent Services DevFeed: [Grab's LLM-Kit Framework Standardizes More Than 500 Internal Agent Services]() Original publisher: [Read original article]() Author: Hien Luu Published: 2026-09-15T09:00:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Framework](), [AI Agent](), [Deployment](), [Model Context Protocol](), [agent observability](), [FastAPI](), [OpenTelemetry](), [GitLab]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-ml-data-engineering](), [deployment](), [fastapi](), [framework](), [gitlab](), [grab-agent-platform](), [ml-data-engineering](), [model-context-protocol](), [news](), [opentelemetry](), [vault]() ### AI overview Grab's internal LLM-Kit framework standardizes more than 500 agent services by providing shared scaffolding for evaluation, tracing, secret handling, service discovery, and tool-server connections. The article reports that deploying a new agent service now takes about one hour instead of two weeks or more. ### Source excerpt Grab has implemented LLM-Kit, a framework that standardizes over 500 internal agent services. This system enhances service integration, evaluation, and secret handling, reducing the time to deploy new AI agents from two weeks to one hour. It centralizes infrastructure management, allowing runtime tool discovery and flexible model integration, while maintaining operational control. By Hien Luu ## How Everpure proposes reducing GPU idle time by improving AI data access DevFeed: [How Everpure proposes reducing GPU idle time by improving AI data access]() Original publisher: [Read original article]() Author: Chris Mellor Published: 2026-09-15T08:00:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [AI Infrastructure](), [GPU](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [AI Agent](), [data-processing](), [data]() Tags: [agents](), [ai](), [ai-infrastructure](), [ai-ml](), [data-processing](), [gpu](), [hardware](), [rag](), [sponsored]() ### AI overview This sponsored feature describes Everpure's approach to reducing GPU idle time in AI systems by improving access to large-scale insurance data. It discusses central metadata indexing, storage performance, self-describing data, and integration with Nvidia GPU infrastructure for AI agents and retrieval-augmented generation. ### Source excerpt SPONSORED FEATURE: The vendor's AI solutions are dedicated to increasing GPU utilization and avoiding costly GPUs doing nothing while waiting for data ## AI DevFeed: [AI]() Original publisher: [Read original article]() Published: 2026-09-15T02:59:12.316196Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Artificial Intelligence](), [AI Agent](), [Security](), [Google](), [Protocol (disambiguation)](), [Databases](), [CockroachDB]() Tags: [2026](), [agents](), [ai](), [cockroachdb](), [databases](), [google](), [security](), [vector-search]() ### AI overview An AI-focused Cockroach Labs blog category page listing articles about agent-to-agent communication, AI agent security, database isolation, compliance, resilience, identity, vector search, operations automation, and durable execution with CockroachDB. ### Source excerpt Blog ai A2A Is Now an Open Standard. The Data Layer Underneath It Isn't. In April 2025, Google released a protocol for agent-to-agent communication. Within three months, Google had donated it to the Linux Foundation... Quentin Packard August 19, 2026 Multi-Tenant AI Agents: Why Data Isolation Starts at the Database Most SaaS teams shipping agentic features focus on prompt safety and API-layer filtering. But effective AI agent security also depends on... ## How to Connect Your Twilio Agent to External APIs DevFeed: [How to Connect Your Twilio Agent to External APIs]() Original publisher: [Read original article]() Author: Amanda Lange Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [AI Agent](), [API](), [C#](), [.NET 9](), [REST API](), [SDKs](), [OpenAI]() Tags: [agent](), [api](), [apis](), [c-sharp](), [developer-insights](), [environment-variables](), [how-to](), [net](), [net-9](), [ngrok](), [openai](), [rest-api](), [sdk]() ### AI overview This tutorial explains how to build a voice AI agent with C# and .NET 9 using Twilio Conversation Relay. It demonstrates LLM tool calling to fetch live data from an external REST API and covers project setup, required packages, environment variables, and API key storage. ### Source excerpt How to Connect Your Twilio Agent to External APIs ## How to operate shared platforms safely at agent scale DevFeed: [How to operate shared platforms safely at agent scale]() Original publisher: [Read original article]() Author: Candace Shamieh; T Zhang; Gabriele Baldoni Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Platform Engineering](), [Loop Engineering](), [AI Agent](), [sdlc](), [Latency](), [Low Latency](), [Concurrency](), [Software]() Tags: [agent](), [agent-observability](), [agentic-workflows](), [agents](), [ai](), [ai-agent](), [ci](), [concurrency](), [latency](), [low-latency](), [operational](), [performance](), [platform-engineering](), [platforms](), [queue](), [recovery](), [timeout]() ### AI overview This Datadog article explains how platform teams can operate shared platforms safely as AI agent workloads scale across teams. It discusses modeling demand across agent trajectories, planning capacity across dependencies such as CI queues and sandbox pools, handling contention and recovery behavior, and preserving control across system boundaries. ### Source excerpt Learn how Datadog models agent demand, allocates capacity under contention, and preserves control as AI agent workloads scale across shared platforms. ## Inside the LLM Call: GenAI Observability with OpenTelemetry DevFeed: [Inside the LLM Call: GenAI Observability with OpenTelemetry]() Original publisher: [Read original article]() Author: OpenTelemetry Authors; Docs CC BY Published: 2026-09-14T16:56:42Z Content type: tutorial Language: en Sources: [Blog on OpenTelemetry]() Topics: [OpenTelemetry](), [agent observability](), [telemetry](), [AI Agent](), [Large Language Model]() Tags: [agent](), [ai-agent](), [claude-code](), [codex](), [observability](), [opentelemetry](), [token](), [tool](), [visibility]() ### AI overview This tutorial explains how OpenTelemetry Semantic Conventions for Generative AI record LLM calls, tool invocations, token counts, and related events. It demonstrates exporting telemetry from an LLM-powered application, viewing it with Aspire Dashboard, and considering sensitive-data implications of optional content capture. ### Source excerpt Your AI agent just took 45 seconds to answer a simple question. Was it the model? A slow tool call? A retry loop? Every time an application calls an LLM, a chain of model calls, tool invocations, and token exchanges happens behind the scenes -- and without observability, you are guessing. The OpenTelemetry Semantic Conventions for Generative AI give you that visibility. They standardize how GenAI operations are recorded -- the model being called, input and output token counts, and when opted in, the full content of prompts, completions, tool calls, and tool results. ## Agentic Engineering in Python: From Vibes to Evidence DevFeed: [Agentic Engineering in Python: From Vibes to Evidence]() Original publisher: [Read original article]() Author: Ben Batman Published: 2026-09-14T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Loop Engineering](), [AI Agent](), [Python](), [Transformers](), [Code review](), [Test-driven development]() Tags: [agentic](), [agentic-engineering](), [ai-coding-agents](), [cheat-sheet](), [code-review](), [learning](), [python](), [tests]() ### AI overview This tutorial presents agentic engineering in Python as a disciplined workflow for delegating bounded tasks to AI coding agents while retaining human control over intent, constraints, review, and release decisions. It explains execution and acceptance loops, the Engineering Evidence Ladder, and the RECAP review method for evaluating agent-generated changes with tests, type hints, refactoring, and code review. ### Source excerpt Move from vibe coding to agentic engineering in Python, using tests, types, and code review to prove an AI agent's changes are safe to keep. ## How to Connect Your Twilio Agent to External APIs in Node.js DevFeed: [How to Connect Your Twilio Agent to External APIs in Node.js]() Original publisher: [Read original article]() Author: Dhruv Patel, Amanda Lange Published: 2026-09-14T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [AI Agent](), [Node.js](), [voice ai](), [REST API](), [OpenAI](), [Express]() Tags: [agent](), [ai](), [ai-agent](), [developer-insights](), [environment-variables](), [node-js](), [openai](), [rest-apis](), [visual-studio-code](), [voice]() ### AI overview This tutorial explains how to build a voice-enabled AI agent in Node.js with Twilio Conversation Relay and OpenAI. The agent uses LLM-driven tool calling to retrieve live data and take actions through an external REST API. ### Source excerpt Build a voice-enabled AI agent in Node.js with Twilio Conversation Relay and OpenAI to dynamically fetch live data from external REST APIs. ## {unscripted} SF recap: do you trust your AI agents? DevFeed: [{unscripted} SF recap: do you trust your AI agents?]() Original publisher: [Read original article]() Author: Nick Durkin Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [AI Agent](), [Security](), [Code review]() Tags: [ai](), [ai-agents](), [blog](), [review](), [security]() ### AI overview A recap of {unscripted} 2026 in San Francisco examines trust, security, and adoption of AI agents in software delivery. It highlights guardrails, quality standards, regulatory visibility, and code review as important constraints on safely using AI. ### Source excerpt Key takeaways from {unscripted} SF: trust over speed, security teams falling behind attackers, and code review as the new AI bottleneck. | Blog ## It passed CI. It passed your evals. The customer still got the wrong answer. DevFeed: [It passed CI. It passed your evals. The customer still got the wrong answer.]() Original publisher: [Read original article]() Author: Sean O'Dell Published: 2026-09-13T14:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [AI Agent](), [agent observability](), [observability](), [tracing](), [dynatrace](), [ci](), [coding]() Tags: [ai](), [ai-agent](), [ai-agents](), [ai-engineering](), [ci](), [coding](), [dynatrace](), [observability](), [post-contributed](), [sponsor-dynatrace](), [sponsored](), [sponsored-post-contributed](), [traces]() ### AI overview The article explains how AI-agent failures can pass CI and evaluations while still producing slow or incorrect customer-facing results. It presents distributed traces and agent trajectories--model calls, tool calls, arguments, and results--as evidence for debugging retrieval behavior, release context, and feature-flag state. ### Source excerpt A diff is not evidence. It's a statement of intent. The tests passed. The review's done. The change is live. The post It passed CI. It passed your evals. The customer still got the wrong answer. appeared first on The New Stack. ## Deploy, Discover, Inspect, Observe: A Summer Spent Making a Public Vespa MCP Server DevFeed: [Deploy, Discover, Inspect, Observe: A Summer Spent Making a Public Vespa MCP Server]() Original publisher: [Read original article]() Author: eivinbingen oystein viktor mfstort Published: 2026-09-13T00:00:00Z Content type: article Language: en Sources: [Vespa Blog]() Topics: [Model Context Protocol (MCP)](), [MCP Server](), [Cloud](), [AI Agent](), [Large Language Model](), [Command-line interface]() Tags: [ai-agent](), [ai-assistants](), [claude](), [cli](), [cloud](), [codex](), [internships](), [language-models](), [mcp](), [mcp-server]() ### AI overview This article describes the construction of a standalone, publicly hosted Vespa Cloud MCP server. It explains how MCP connects AI assistants and language models to external systems through resources, tools, and prompts, and discusses evaluating MCP usage against terminal access and Vespa CLI access. ### Source excerpt We built a standalone Vespa Cloud MCP server as a summer interns project [Next page]()