--- title: "AI Agents" description: "578 posts about AI Agents, summarised, each linking to the original." --- # AI Agents > 578 posts about AI Agents, summarised, each linking to the original. ## Articles ### [Updates to Teams and Auto](https://yomu.fyi/post/updates-to-teams-and-auto.md) - Company: [Cursor](https://yomu.fyi/company/cursor.md) - Author: Michael Truell - Published: Aug 12, 2025 - Type: Announcement Two billing updates will apply at the next renewal after September 15 for monthly and yearly plans. Teams is moving agent usage from fixed request costs to variable request costs, so charges rise or fall with the amount of work performed; a simple syntax question costs less than implementing a full pull request. This change extends the credit system already used by individual plans and unifies billing across tiers. For individuals, Auto will count toward included monthly usage at competitive token rates at the next renewal after September 15, replacing its unlimited status since June 2025; the post says Auto had previously matched premium-model pricing for individuals from December 2023 through June 2025 and for teams since June 2025. ### [How to cut through AI hype and choose solutions with confidence](https://yomu.fyi/post/how-to-cut-through-ai-hype-and-choose-solutions-with-confidence.md) - Company: [Intercom](https://yomu.fyi/company/intercom.md) - Author: Nicole Rashied - Published: Aug 11, 2025 - Type: Explainer Teams evaluating AI solutions can waste time, money, and trust when they lack technical fluency, choose unsuitable vendors, underestimate internal requirements, or expect immediate perfection. The post introduces Retrieval-Augmented Generation (RAG), vector search, agentic AI, and MCP as concepts that help teams distinguish static or rules-based tools from systems that retrieve current information, understand intent, take actions, and operate within governed boundaries. It argues that building an AI Agent in-house requires substantial work across embeddings, retrieval, prompt chaining, governance, security, maintenance, and model upgrades. Vendor evaluation should consider ongoing R&D, product roadmaps, adaptability, and maintenance, while implementation depends on structured content, documentation, process visibility, and APIs. Fin is cited as typically resolving over 76% of support questions out of the box, with resolution expected to improve through ongoing optimization and measurement. ### [Cursor Agent CLI](https://yomu.fyi/post/cursor-agent-cli.md) - Company: [Cursor](https://yomu.fyi/company/cursor.md) - Author: Lukas Möller, Ian Huang & Balta Ruiz - Published: Aug 7, 2025 - Type: Announcement Cursor has made its Agent available through a command-line interface (CLI) and in headless environments, extending it beyond the editor, web, mobile, and Slack. The release targets customers using Neovim, JetBrains, and other IDEs, while allowing Agent to run alongside those tools or as multiple parallel agents in a terminal or remotely. The CLI supports any model included with a Cursor subscription, and users can start it after installing with curl https://cursor.com/install -fsSL | bash and invoking agent chat with a prompt. Agent can generate files, run terminal commands, search a codebase, and now read, modify, or delete files and execute approved shell commands. The CLI is in beta, with evolving security safeguards and use limited to trusted environments. ### [Bugbot is out of beta](https://yomu.fyi/post/bugbot-is-out-of-beta.md) - Company: [Cursor](https://yomu.fyi/company/cursor.md) - Author: Jon Kaplan, Lee Danilek & Rohan Varma - Published: Jul 24, 2025 - Type: Announcement Cursor has released Bugbot, a code review agent that automatically analyzes pull-request changes for logic bugs, edge cases, and security issues before production. It runs automatically on pull requests, gathers context from changes to understand code intent, and comments detected issues directly where they occur. Teams can steer reviews with custom BUGBOT.md rules, then send flagged issues to Cursor or launch a background agent on the web with one click. During its beta, Bugbot reviewed over 1 million PRs and found over 1.5 million issues; more than 50% of identified bugs were resolved before merge. Cursor also reports adoption by several companies, with testimonials citing review-time savings, strong bug detection, and confidence reviewing AI-generated code. ### [How To Build Agents Users Can Trust](https://yomu.fyi/post/how-to-build-agents-users-can-trust.md) - Company: [Ramp](https://yomu.fyi/company/ramp.md) - Author: CJ Enright, Will Koh, Felix Zhang - Published: Jul 10, 2025 - Type: Explainer Ramp describes how it built and shipped LLM-backed agents for expense management, focusing on the trust risks created by low-quality or unexpected finance outputs. It recommends choosing ambiguous, high-volume problems with asymmetric upside, then combining reasoning and citations grounded in product or user-provided context with an explicit “Needs review” path when the agent is unsure. The policy agent uses expense policies to approve, reject, or escalate expenses, while deterministic rules such as dollar limits, vendor blocklists, and category restrictions constrain autonomy. Ramp reports that more than 65% of approvals are fully handled by the agent, while users can progressively move from suggestions to autonomous actions through workflow-based controls and evaluation. ### [Cursor on web and mobile](https://yomu.fyi/post/cursor-on-web-and-mobile.md) - Company: [Cursor](https://yomu.fyi/company/cursor.md) - Author: Andrew Milich & Jason Ginsberg - Published: Jun 30, 2025 - Type: Announcement Cursor announces that its Agents are available on the web and mobile, extending the IDE-based agent to desktop, tablet, and mobile browsers. Agents can run bug fixes, build features, answer complex codebase questions, and scaffold work in the background, while users can include images, add follow-up instructions, and run multiple agents in parallel. Repository-access team members can review diffs and pull requests or create pull requests from the web interface; the service can also be installed as a Progressive Web App on iOS or Android. Slack integration sends completion notifications and lets users trigger agents with “@Cursor,” and work can later be reviewed, extended, or edited inline in Cursor on a laptop. ### [Phoenix.new – The Remote AI Runtime for Phoenix](https://yomu.fyi/post/phoenix-new-the-remote-ai-runtime-for-phoenix.md) - Company: [Fly.io](https://yomu.fyi/company/fly-io.md) - Published: Jun 20, 2025 - Type: Announcement Phoenix.new is presented as a batteries-included, fully online coding agent designed for Elixir and Phoenix, after an initial weekend project grew into a system that generated Phoenix applications and drove an in-browser IDE. It runs in an ephemeral Fly Machine, giving the agent and user a shared root shell while isolating local machines; applications launch in the cloud with preview URLs, GitHub integration, and Fly.io infrastructure guardrails. A headless Chrome browser lets the agent inspect page content and JavaScript state, interact with front ends, and correlate changes with server-side logs while testing applications. The system already handles full-stack applications using WebSockets, Phoenix Presence, and real databases, can work with other installed languages and frameworks, and is being used to triage GitHub issues and produce pull requests asynchronously. ### [How Ramp Fixes Merchant Matches with AI](https://yomu.fyi/post/how-ramp-fixes-merchant-matches-with-ai.md) - Company: [Ramp](https://yomu.fyi/company/ramp.md) - Author: Chami Lamelas, Anton Biryukov - Published: Jun 16, 2025 - Type: Problem & solution Ramp describes the difficulty of mapping payment-processor card acceptor data to useful merchant records when names, MCCs, and locations are vague, misleading, or affected by rebranding. Users can submit a replacement merchant name, website, and category, but deciding whether the request is legitimate and whether to update an existing record or create one requires additional context from related receipts, extracted line items, and transaction memos. Ramp addresses this with an AI agent built around an LLM, embeddings, rapid online analytical processing queries, multimodal retrieval-augmented generation, and guardrails that constrain permitted changes. The agent handles requests in under 10 seconds, and monitoring indicates it improves nearly 99% of transaction classifications, while fewer than 10% receive a second correction request. The same flow has also been extended to batch remapping and credit-card statement matching. ### [What are MCP Servers?](https://yomu.fyi/post/what-are-mcp-servers.md) - Company: [Fly.io](https://yomu.fyi/company/fly-io.md) - Published: Jun 12, 2025 - Type: Explainer This explainer clarifies what Model Context Protocol (MCP) servers do and where they differ from conventional APIs, serverless functions, and standalone appliances. It frames MCP servers as Alexa Skills that connect capabilities to an LLM, then as JSON-based APIs with multiplexing, server push, and built-in introspection for discovering tools, arguments, and usage descriptions. The post cautions that blindly generating an MCP server from an OpenAPI schema may expose low-level calls rather than agent-oriented workflows, and notes that MCP servers have a long-lived initialization, operation, and shutdown lifecycle rather than a serverless one. It also argues that MCP is not inherently secure or private, recommending remotely hosted servers protected by revocable bearer tokens, and envisions customized MCP-based agents becoming family-like assistants rather than mere appliances. ### [My AI Skeptic Friends Are All Nuts](https://yomu.fyi/post/my-ai-skeptic-friends-are-all-nuts.md) - Company: [Fly.io](https://yomu.fyi/company/fly-io.md) - Published: Jun 2, 2025 - Type: Explainer The essay argues that skepticism about LLMs for software development often reflects outdated experience with chat-based code generation rather than current agent workflows. Modern coding agents inspect repositories, edit files, invoke Unix tools and MCP calls, use Git, and run linters, compilers, tests, and model checkers, turning feedback into iterative repairs. The author says these systems can automate tedious typing, lookup, bookkeeping, test refactoring, and edit-compile-test-debug cycles while developers retain responsibility for curation, judgment, direction, and reviewing merged code. Hallucinations are presented as substantially mitigated by tool-based validation, although the author notes that agents remain unsuitable for some trusted operations and that effectiveness depends on language, tooling, prompts, indices, and guardrails. The conclusion is that AI-assisted programming is already materially changing developer productivity, despite its noise and unresolved limitations. ### [Streamlining RiskOps with the SOP agent framework](https://yomu.fyi/post/streamlining-riskops-with-the-sop-agent-framework.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Fujiao Liu - Published: May 8, 2025 - Type: Problem & solution Risk Operations teams investigating Account Takeover (ATO) cases must gather data across systems, run SQL queries, and make decisions under pressure, creating delays and error risk. The SOP-driven LLM agent framework represents the workflow as a natural-language, indentation-based tree whose steps specify queries, external functions, and decision criteria. During execution, the SOP planner navigates the tree and produces JSON-formatted instructions, while a Worker Agent invokes APIs or SQL queries and stores results in a feedback loop. The process ends by synthesising investigation data into a report and final decision. In reported results, handling time fell from 22 to 3 minutes per ticket, 87% of ATO cases were automated since launch, and the error rate was zero. ### [eBay Uses Agentic AI to Supercharge Personalized Ecommerce](https://yomu.fyi/post/ebay-uses-agentic-ai-to-supercharge-personalized-ecommerce.md) - Company: [Ebay](https://yomu.fyi/company/ebay.md) - Author: eBay News Team - Published: May 6, 2025 - Type: Announcement eBay announces a new AI shopping agent designed to provide personalized product recommendations and guidance throughout the marketplace and buying journey. It uses shopping preferences, user requests, and predictive inline messaging to support activities such as finding gifts or assembling a Spring Break wardrobe. The agent extends eBay’s shift from simple chatbots toward conversational and interactive systems intended to understand, interact with, and anticipate customer needs, combining agentic AI with eBay’s infrastructure, customer insights, and product knowledge. eBay says its Responsible AI team collaborates on AI-powered features to align them with safety, fairness, transparency, and accountability, while the new experience starts with a small percentage of U.S. customers and will expand on a rolling basis. ### [Introducing the SOP-driven LLM agent frameworks](https://yomu.fyi/post/introducing-the-sop-driven-llm-agent-frameworks.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Fujiao Liu - Published: Apr 25, 2025 - Type: Announcement Grab introduces an LLM agent framework that uses Standard Operating Procedures (SOPs) to guide enterprise automation and improve consistency in complex processes. It represents SOPs as trees of action and decision nodes, while a planner uses Depth-First Search, prompt generation, and backtracking to direct an LLM-powered worker agent; context compression and limited APIs are used to address hallucination and efficiency concerns. An SOP editor, plugins for APIs, Python, and SQL, workflows, a GRAG pipeline, playground, multilingual user agent, and state stack extend the framework’s capabilities. Reported evaluations include over 99.8% accuracy, an Account Takeover Investigations bot automating 87% of cases and reducing investigation time from 23 minutes to three, and fraud investigations cutting AHT by 45% and saving over 300 man-hours monthly. ### [Announcing a New Collaboration Between eBay and OpenAI](https://yomu.fyi/post/announcing-a-new-collaboration-between-ebay-and-openai.md) - Company: [Ebay](https://yomu.fyi/company/ebay.md) - Author: eBay News Team - Published: Jan 23, 2025 - Type: Announcement eBay announces a collaboration with OpenAI as part of its strategy to shape agentic ecommerce and redefine online discovery and shopping for enthusiasts. The company is among the first to collaborate on the research preview of Operator, an AI agent that acts as a virtual assistant and autonomously performs tasks across the internet, including online shopping. Operator directs users to eBay for unique inventory finds, a model eBay anticipates will expand sellers’ reach by giving more buyers exposure to that inventory. The announcement also places the work within eBay’s broader effort to evolve internal AI agent capabilities and deepen strategic partnerships while learning how these technologies can empower buyers and help sellers grow their businesses. ### [Building Effective AI Agents](https://yomu.fyi/post/building-effective-ai-agents.md) - Company: [Anthropic](https://yomu.fyi/company/anthropic.md) - Published: Dec 19, 2024 - Type: Explainer Effective agentic systems need not begin with complex frameworks: the post reports that successful implementations commonly use simple, composable patterns and recommends choosing the least complex solution that meets the task's needs. It distinguishes workflows, which follow predefined code paths, from agents, where LLMs dynamically direct their processes and tool use. The proposed foundation is an augmented LLM with retrieval, tools, and memory, potentially integrated through the Model Context Protocol; larger designs include prompt chaining, routing, parallelization, and orchestrator-workers patterns. Frameworks can simplify implementation but may obscure prompts and responses, so direct API use and understanding underlying code are recommended. Tool definitions are treated as an interface requiring testing, clear examples, low formatting overhead, and mistake-resistant parameters; absolute filepaths eliminated observed errors in a coding agent. ### [Intercom vs Zendesk: Two AI agents put to the test](https://yomu.fyi/post/intercom-vs-zendesk-two-ai-agents-put-to-the-test.md) - Company: [Intercom](https://yomu.fyi/company/intercom.md) - Author: Sagar Joglekar - Published: Nov 14, 2024 - Type: Benchmark The post compares Intercom’s Fin AI Agent with Zendesk’s AI agent, focusing on answer quality, complexity, and resolution performance. In a test using 48 fictional bed-and-breakfast help articles and 200 questions, both systems answered the same dataset, while four AI models judged responses and an Elo rating system selected winners. It reports that Fin produced better answers in 80% of cases, answered 96% of multi-source questions versus Zendesk’s 78%, and delivered responses averaging 120 words versus 50. The analysis also found no statistical difference in hallucination levels and estimated that Fin was 66% more likely to provide a complete resolution when both agents answered. The post notes limitations including simulated data, AI judging, and differing product features, then describes Fin’s integrations and 99¢-per-resolution pricing. ### [How Yoko Li makes towns, tamagoes, and tools for local AI](https://yomu.fyi/post/how-yoko-li-makes-towns-tamagoes-and-tools-for-local-ai.md) - Company: [Fly.io](https://yomu.fyi/company/fly-io.md) - Published: Jan 8, 2024 - Type: Explainer Yoko Li’s local AI work spans playful agent experiments and practical tools for running language-model workflows on personal hardware. AI Town uses conversations among AI agents to produce changing relationships and emergent behavior, while AI Tamago feeds game state and user inputs to an unmodified LLaMA 2 7B model and uses Ollama’s JSON mode to generate pet actions. The Local AI Starter Kit packages document ingestion, vector storage, and language-model responses into a private, local “chat with a knowledge base” pipeline, avoiding setup across at least four API providers. The post also describes unreliable JSON generation, hallucinations, and grammar-guided workarounds, concluding that randomness is a liability for factual outputs but useful for game behavior and creative experimentation. ### [Our problems](https://yomu.fyi/post/our-problems.md) - Company: [Cursor](https://yomu.fyi/company/cursor.md) - Author: Sualeh Asif - Published: Oct 12, 2023 - Type: Explainer The post lays out problems Cursor is working on in AI-assisted coding, from selecting relevant context in large codebases to making edits and finding bugs. It proposes collecting 500k tokens from editor sources and using a custom, fast reranker to reduce that context to 8k tokens, while reporting 1.4 billion vectors and 150,000 codebases indexed as of October 12, 2023. Other planned capabilities include a constrained few-step agent that searches, writes, and runs code with feedback, passive and active bug finding, and larger edits presented in real time. Future ideas include time-warp predictions, code understanding in model weights, reader and pseudo-code modes, and automatic stack-trace fixes. The list is explicitly not a final roadmap because new ideas and priorities change, and the post also invites interested people to join the company. [Newer posts](https://yomu.fyi/topic/ai-agents/page/28.md)