---
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

### [Transformation in action: Raising the bar for customer experience](https://yomu.fyi/post/transformation-in-action-raising-the-bar-for-customer-experience.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Mar 20, 2026
- Type: Explainer

AI is shifting support from reactive backlog management toward proactive experience design, as teams gain capacity through faster, more efficient service across languages and time zones. The report says 58% rank experience and satisfaction as their top 2026 priority, up from 28%, while mature deployment triples the share able to always meet expectations from 9%. Intercom’s support team expanded access, improved first-response rates, and added onboarding sessions while absorbing a 300% demand increase without expanding headcount, enabled by deep integration of Fin. Clay uses Fin across Slack, email, and in-app chat, where it handles half of roughly 7,000 monthly queries and participates in 90% of all queries. The recommended model treats support as a product, combines AI with human handoffs, anticipates needs, builds trust, and personalizes interactions.


### [Transformation in action: Why ROI becomes clearer with deeper integration](https://yomu.fyi/post/transformation-in-action-why-roi-becomes-clearer-with-deeper-integrati.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Mar 20, 2026
- Type: Explainer

Customer service teams often see early AI gains in response speed and efficiency, but deeper operational integration produces larger returns and makes value easier to measure. Research cited in the 2026 Customer Service Transformation Report says 62% of support teams have improved metrics after implementing AI, rising to 87% among teams at mature deployment; the share able to measure ROI rises from 35% to 70%. As deployment matures, teams shift from measuring time and cost savings to tracking how freed capacity supports higher-value customer work and revenue-generating activity. The proposed AI-first economic model treats AI as a workforce capability that improves human productivity, system improvement, revenue influence, and organizational agility. Intercom’s Fin illustrates the model: it is involved in 97% of customer conversations, resolves 83.5% end-to-end, and helped absorb more than 300% demand growth without scaling headcount.


### [Transformation in action: Why AI deployment depth makes the difference](https://yomu.fyi/post/transformation-in-action-why-ai-deployment-depth-makes-the-difference.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Mar 20, 2026
- Type: Explainer

AI adoption is widespread in customer service, but the report identifies a deployment gap between organizations experimenting with the technology and those integrating it deeply into operations. Based on a survey of 2,470 global support professionals, it finds that 82% of senior leaders invested in AI in the past year and 87% plan to invest in 2026, while only 10% report mature deployment. Mature teams put AI at the core of workflows, give it meaningful responsibility, and use continuous improvement to achieve higher-quality, more consistent support; 43% report that outcome, nearly double the rate for teams at initial deployment. Lightspeed illustrates the approach by combining tailored training, executive alignment, content investment, and AI operations discipline, reaching 72% Fin resolution without human intervention across 43,000-plus monthly requests and 12-plus languages.


### [Kaizen for the AI era: How small improvements build smarter support](https://yomu.fyi/post/kaizen-for-the-ai-era-how-small-improvements-build-smarter-support.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Caroline Glackin
- Published: Mar 19, 2026
- Type: Explainer

The post presents Kaizen as a structured approach to continuous improvement, centered on small changes, immediate iteration, and participation from people closest to the work. At Fin, this philosophy is applied to Agent Fin through the Fin Flywheel: teams train it on complex queries, test simulated conversations, deploy it across channels, and analyze performance with AI-powered insights. Each interaction feeds a repeatable loop rather than a one-time launch, while Human Support representatives examine escalations, ask why Fin could not resolve them, and identify content, process, or tooling fixes. A lightweight AI-powered logging process captures suggestions in the moment, enabling subject matter experts or frontline teams to implement changes quickly; together, the two systems make continuous learning an ingrained capability and are presented as a source of competitive advantage.


### [From firefighting to building: How AI agents restored our team’s core productivity](https://yomu.fyi/post/from-firefighting-to-building-how-ai-agents-restored-our-team-s-core-p.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Sneh Agrawal
- Published: Mar 19, 2026
- Type: Problem & solution

Grab’s Analytics Data Warehouse team supports more than 1,000 monthly users and 15,000 tables, but repetitive questions about definitions, lineage, quality, and basic enhancements consumed about 40% of engineering time. It deployed a multi-agent AI system that routes Slack requests into enhancement or investigation pathways, using FastAPI and LangGraph for stateful, cyclical collaboration, Redis for caching and sessions, and PostgreSQL for persistent memory. Specialized agents query Trino, Hive, or Delta Lake, inspect GitLab code, check incidents and pipeline health, or prepare semi-automated code changes, while human reviewers approve production modifications. The system autonomously resolves most standard inquiries and some common enhancements, reducing resolution time by an order of magnitude and bringing simple answers to users within minutes. The resulting capacity shift reclaimed several FTEs worth of engineering bandwidth and moved hundreds of hours from reactive support to roadmap work.


### [Money Forward brings Cursor’s coding agents to product, design, and QA](https://yomu.fyi/post/money-forward-brings-cursor-s-coding-agents-to-product-design-and-qa.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Mar 18, 2026
- Type: Explainer

Money Forward expanded Cursor coding agents beyond engineering to product, design, and quality assurance after developers began saving an estimated 15–20 hours per week. Adoption among engineers rose 30% within a week, and the company now reports more than 1,000 daily users; engineering examples include Rails optimization, Terraform deployments, and Vue-to-React migrations. For the company-wide rollout, its MEPAR department chose Cursor for minimal setup, visual browser capabilities, a unified workspace, model-agnostic asynchronous cloud agents, and context retrieval across complex codebases. QA uses MCP-connected Jira and Notion materials to generate test cases and Playwright scripts, reducing test-generation time by 70%, while product managers inspect repositories for requirements and designers prototype against live frontends and product analytics.


### [Training Composer for longer horizons](https://yomu.fyi/post/training-composer-for-longer-horizons.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Federico Cassano & Sasha Rush
- Published: Mar 17, 2026
- Type: Explainer

Composer is trained for long-horizon coding tasks using self-summarization, allowing reinforcement-learning trajectories to exceed the model’s context window. When a fixed token trigger is reached, Composer receives a synthetic summarization query, uses scratch space to produce a condensed context, and resumes with the summary plus conversation state; training rewards every token in the chained rollout with the final outcome. Against a tuned prompt-based compaction baseline, self-summary reduced compaction-related error by 50% on CursorBench at both 80k and 40k triggers, while summaries averaged about 1,000 tokens instead of more than 5,000 and reused the KV cache. An early Composer checkpoint solved Terminal-Bench 2.0’s make-doom-for-mips after 170 turns, summarizing more than 100,000 tokens into roughly 1,000 tokens of retained information.


### [Making AI Write Android Code Our Way: A Practical Guide to Agent Skills](https://yomu.fyi/post/making-ai-write-android-code-our-way-a-practical-guide-to-agent-skills.md)
- Company: [Medium](https://yomu.fyi/company/medium.md)
- Author: Pierrick CAEN
- Published: Mar 17, 2026
- Type: Tutorial

An Android team at Medium describes how it encoded its project conventions so AI assistants generate code aligned with Clean Architecture, Kotlin, Jetpack Compose, Hilt, Apollo GraphQL, and feature modules. An AGENTS.md file provides persistent context about module structure, dependency injection, state management, navigation, testing, and commands, while Markdown skills in .agents/skills/ provide ordered runbooks for specific tasks. Examples cover scaffolding screens and feature modules, migrating Material 2 to Material 3, enforcing ViewModel feature-flag handling, and adding deeplinks, analytics, translations, and tests. Detailed patterns include ViewModel-injecting and @VisibleForTesting composables, assisted injection, StateFlow, Result, MockK, Turbine, and Robolectric. The team iterates skills from review feedback, keeps them version-controlled, and plans file-aware rules plus commands for release diffs and reporting.


### [Securing our codebase with autonomous agents](https://yomu.fyi/post/securing-our-codebase-with-autonomous-agents.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Travis McPeak
- Published: Mar 16, 2026
- Type: Announcement

After a fivefold increase in pull-request velocity over nine months, the team found static analysis and rigid code ownership insufficient for security at scale. It built a fleet of Cursor Automations powered by cloud agents, webhook and GitHub integrations, and a security MCP deployed as a just-in-time serverless Lambda function. The MCP provides persistent data, semantic deduplication using Gemini Flash 2.5, and consistently formatted reporting and follow-up actions, while Terraform standardizes review and deployment. Four templates cover PR review, segmented vulnerability hunting, dependency patching with reachability analysis and tests, and daily invariant-drift monitoring. Agentic Security Review has run on thousands of PRs in two months and prevented hundreds of issues from reaching production; the team says more automations are planned.


### [From resolutions to outcomes: Evolving how Fin delivers value](https://yomu.fyi/post/from-resolutions-to-outcomes-evolving-how-fin-delivers-value.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Darragh Curran
- Published: Mar 12, 2026
- Type: Announcement

Fin is changing its pricing metric from “resolutions” to “outcomes” as its capabilities extend beyond fully autonomous support conversations. Historically, a resolution meant Fin solved an issue end to end without human involvement; the model gave teams a clear ROI comparison, and Fin’s average customer resolution rate now stands at 76% across more than 7,000 teams. Customers increasingly deploy Fin on complex, multi-step work involving external systems, policy constraints, and human oversight, where Fin may gather context and act before handing off for confirmation. An outcome counts when Fin successfully completes the action it was configured to perform, including a full resolution or a Procedure. The change keeps pricing simple while recognizing collaborative workflows, supporting compliance, and providing a metric that can scale as Fin takes on more roles beyond service.


### [How we compare model quality in Cursor](https://yomu.fyi/post/how-we-compare-model-quality-in-cursor.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Naman Jain
- Published: Mar 11, 2026
- Type: Benchmark

Cursor describes a hybrid online-offline process for comparing coding-agent quality as developer tasks become longer, more complex, and multi-file. Its offline CursorBench suite uses tasks derived from real Cursor sessions, paired with ground-truth solutions through Cursor Blame, and evaluates correctness alongside code quality, efficiency, and interaction behavior; the post focuses on correctness. Tasks are intentionally short and underspecified, span larger codebases and multi-workspace environments, and are scored with agentic graders, while correctness is plotted against median completion tokens to show the compute/latency trade-off. Controlled live-traffic experiments supplement the suite, catching regressions that offline grading misses, and the post reports that CursorBench separates frontier models more clearly and its rankings track developer experience more closely than public benchmarks.


### [Over 30 new plugins join the Cursor Marketplace](https://yomu.fyi/post/over-30-new-plugins-join-the-cursor-marketplace.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Kevin Niparko
- Published: Mar 11, 2026
- Type: Announcement

Cursor is adding more than 30 plugins to its Marketplace, including integrations from Atlassian, Datadog, GitLab, Glean, Hugging Face, monday.com, and PlanetScale. The plugins extend Cursor across infrastructure and productivity workflows: users can query observability data, manage GitLab repositories and pipelines, work with PlanetScale schemas and queries, handle Atlassian issues and backlogs, search company knowledge in Glean, and manage monday.com boards and tasks. Each plugin bundles capabilities such as MCPs with skills that instruct an agent how to use them, and those tools can support manually started or automatically invoked cloud agents. Cursor also supports community-built plugins, while Teams and Enterprise admins can create private team marketplaces. Automations can run agents on schedules or events with custom instructions.


### [Fin Product Updates: February recap](https://yomu.fyi/post/fin-product-updates-february-recap.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Brian Donohue
- Published: Mar 10, 2026
- Type: Announcement

February updates for Fin address constraints in complex automation, voice quality, Shopify deployment, and helpdesk operations. Procedures can be drafted from existing source material, edited in natural language with hooks for live data, business logic, and deterministic code, then tested step by step in simulations using conversation data and defined criteria. Voice changes add pronunciation rules, 11 new voices, and automatic background-noise reduction, while Shopify setup connects a store, installs Messenger without code, and deploys Fin in under two minutes. Helpdesk additions include 11 call metrics and automatic public-holiday office hours; the post says 19 improvements shipped in 2026, and concludes that all featured updates are live and available now.


### [Transformation in action: When support becomes the blueprint for company-wide AI](https://yomu.fyi/post/transformation-in-action-when-support-becomes-the-blueprint-for-compan.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Mar 5, 2026
- Type: Explainer

The post describes how businesses are extending AI beyond customer support across the customer lifecycle, with support teams increasingly positioned to lead that work. Research cited in the 2026 Customer Service Transformation Report says 52% of respondents plan to scale AI to other departments in 2026, while 32% say customer service teams lead their AI transformation strategy. WHOOP deployed Fin on its Join page after prospective buyers faced waits of more than 10 hours; Fin resolved 84% of inbound questions and helped drive a 130% increase in attributable sales. The post warns that isolated departmental agents can fragment experiences, and presents a unified Customer Agent with shared memory and business knowledge as the intended direction. It argues support can provide shared standards, guardrails, operating practices, and implementation expertise as AI expands into sales and other functions.


### [Build agents that run automatically](https://yomu.fyi/post/build-agents-that-run-automatically.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Jack Pertschuk, Jon Kaplan & Josh Ma
- Published: Mar 5, 2026
- Type: Announcement

Cursor Automations introduces always-on agents that run on schedules or react to events from Slack, Linear, GitHub, PagerDuty, or custom webhooks. Each invocation spins up a cloud sandbox, uses configured models and MCPs, verifies its output, and can draw on memory from past runs. The product supports review and monitoring workflows, including security audits on pushes to main, risk-based codeowner assignment, and PagerDuty-driven incident investigation that proposes fixes. Other automations generate weekly repository digests, add tests after merged code, triage Slack bug reports through Linear, and assemble cross-tool dashboards. Examples from Cursor and Rippling report faster incident response and reduced repetitive work, while Runlayer describes a software factory built with Runlayer MCP and plugins.


### [Cursor is now available in JetBrains IDEs](https://yomu.fyi/post/cursor-is-now-available-in-jetbrains-ides.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Mar 4, 2026
- Type: Announcement

Cursor is now available in IntelliJ IDEA, PyCharm, WebStorm, and other JetBrains IDEs through the Agent Client Protocol (ACP), extending its agent-driven development experience to developers using those environments. The integration lets users choose frontier models from OpenAI, Anthropic, Google, and Cursor, while Cursor provides a custom-built agent harness for each model. Cursor's secure codebase indexing and semantic search are combined with JetBrains IDE code intelligence and tooling to support work on large enterprise codebases. Users install Cursor ACP from the ACP Registry and authenticate with an existing Cursor account; it is free for users on paid plans, and the release is described as a foundation for deeper JetBrains integrations.


### [How technical support at Cursor uses Cursor](https://yomu.fyi/post/how-technical-support-at-cursor-uses-cursor.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Kody Fisher & Zach Hudson
- Published: Mar 3, 2026
- Type: Explainer

Cursor’s technical support team treats customer investigations as research problems, using a single session to combine code, logs, team knowledge, and past conversations. Engineers typically start in Ask Mode, use semantic search across a locally indexed codebase and documentation, and group related repositories with multi-root workspaces. MCP servers bring customer data, Datadog logs and traces, Slack and support history, runbooks, account details, and ticketing context into those investigations; Linear MCP turns evidence into escalations, while a Slack-mentioned cloud agent can open documentation pull requests. Slash commands, Rules, Skills, and parallel subagents automate repeated work, including failure analysis, known-issue searches, ticket writing, and customer-response drafting. The team reports that more than 75% of support interactions run through Cursor, with stated throughput gains of 5–10x and estimated productivity improvements of up to an order of magnitude.


### [PlanetScale protects production reliability with Bugbot](https://yomu.fyi/post/planetscale-protects-production-reliability-with-bugbot.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Mar 2, 2026
- Type: Problem & solution

PlanetScale describes code review as the bottleneck created by rapidly increasing agent-generated code, while human review capacity remained fixed. To preserve production reliability, the company adopted Bugbot as a dedicated agentic review layer alongside its existing process. Bugbot surfaces semantic and logical issues that static analyzers, linters, and straightforward frontier-model reviews may miss, including premature completion states, non-converging asynchronous controllers, and database restart edge cases. Across more than 2,000 pull requests reviewed monthly, roughly 80% of Bugbot's comments are addressed before merge. PlanetScale says this prevents potential production downtime, saves the equivalent of two full-time engineers of review effort, and lets engineers focus on complex infrastructure work without sacrificing quality.


### [How we built security into Custom Agents](https://yomu.fyi/post/how-we-built-security-into-custom-agents.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Sean Keenan, Jacob Brackett
- Published: Feb 27, 2026
- Type: Explainer

Custom Agents were designed for collaborative workspaces where useful access must be balanced against dangerous actions. They use a build-from-nothing model with no default read, write, or interaction permissions, then add resource-level controls such as page-level access, Slack “read and reply,” email confirmation, and selected MCP tools. Runtime safeguards combine permission controls with prompt-injection mitigations, monitoring of external data, warnings, owner confirmation for risky actions, and remediation such as deleting an incorrect Slack message. Alpha testing across Notion and customers produced more than 3,000 internal and 25,000 customer agents, and exposed overly broad Slack write access that could reach #general. That finding led to the custom permission and reinforced the case for first-party integrations, while planned work includes admin-approved broader scopes, Workers integrations, and additional permission modes.


### [Transformation in action: How AI is evolving support careers](https://yomu.fyi/post/transformation-in-action-how-ai-is-evolving-support-careers.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Feb 27, 2026
- Type: Explainer

AI is changing customer service operations by shifting human agents away from queue-level work such as triage and FAQs toward training systems, complex escalations, and strategic improvement. In research from The 2026 Customer Service Transformation Report, 45% of teams updated job descriptions with AI-related responsibilities, 40% said agents focus more on training AI systems, and 27% primarily handle complex escalations and edge cases. Interviews with 166 customers found nearly 95% reporting meaningful workflow changes, while 83% saw roles become more strategic and supervisory; mature teams also reported less time handling support volume. New positions including AI knowledge managers and conversation designers are emerging, while existing staff can move into AI-focused work such as optimizing knowledge, monitoring outputs, and improving customer journeys. The source recommends transparent communication, agent participation, dedicated time outside inbox, and metrics such as resolution rate as teams build operating models with AI at their core.


[Newer posts](https://yomu.fyi/topic/ai-agents/page/24.md) · [Older posts](https://yomu.fyi/topic/ai-agents/page/26.md)