---
title: "AI Agents"
description: "571 posts about AI Agents, summarised, each linking to the original."
---

# AI Agents
> 571 posts about AI Agents, summarised, each linking to the original.

## Articles

### [Why 78% of customers try to bypass your AI agent](https://yomu.fyi/post/why-78-of-customers-try-to-bypass-your-ai-agent.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Jesse Sumrak
- Published: Aug 28, 2026
- Type: Explainer

Twilio research cited in the post finds that 78% of consumers have tried to bypass an AI agent to reach a human, while 63% want easy escalation and only 47% of brands provide it. The reported causes are unclear AI disclosure, discomfort with high-stakes tasks, and prior failures: 90% of tested consumers misidentify text-based AI, 80% report a negative AI experience, and failed attempts add 81 seconds before escalation. Consumers are most comfortable with scheduling and reservations, returns and exchanges, and concert seat selection, but pull back on medical inquiries, payments, and loan applications. The proposed operating model combines routine automation with clear disclosure, obvious escalation, persistent customer memory, and real-time detection of sentiment and escalation risk. It argues that trust, rather than wholesale replacement, should determine how humans and AI divide customer-service work.


### [How scientists are using Claude to accelerate research and discovery](https://yomu.fyi/post/how-scientists-are-using-claude-to-accelerate-research-and-discovery.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Aug 27, 2026
- Type: Explainer

Claude is being used by scientific research groups as a collaborator across experiment planning, data analysis, and discovery, rather than only for literature reviews or coding. The post describes Biomni, a Stanford platform that gives a Claude-powered agent access to hundreds of biological tools and databases; in an early trial, it completed a genome-wide association study analysis in 20 minutes, while reported validation cases included wearable data and 336,000-cell gene-activity data. Other systems encode expert workflows, including MozzareLLM for interpreting gene-knockout clusters, while a Lundberg lab project uses molecular relationship maps to propose genes for under-studied biological processes. These systems remain imperfect and require guardrails, expert-defined skills, or prospective testing, but the reported examples suggest AI can reduce research bottlenecks and support new approaches to scientific investigation.


### [Anthropic partners with Allen Institute and Howard Hughes Medical Institute to accelerate scientific discovery](https://yomu.fyi/post/anthropic-partners-with-allen-institute-and-howard-hughes-medical-inst.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Aug 27, 2026
- Type: Announcement

Modern biological research generates data at unprecedented scale, but transforming it into validated biological insights remains a fundamental bottleneck. Knowledge synthesis, hypothesis generation, and experimental interpretation still depend on manual processes, while scientific AI systems must provide reasoning researchers can evaluate, trace, and build upon. Anthropic announces partnerships with the Allen Institute and Howard Hughes Medical Institute (HHMI) to extend Claude’s capabilities into frontier research and help scientists plan and execute experiments. HHMI will develop specialized lab agents integrated with scientific instruments and analysis pipelines, while the Allen Institute will build coordinated multi-agent systems for multimodal analysis, including multi-omic integration, knowledge graphs, temporal dynamics, and experimental design. Together, the collaborations aim to compress manual analysis, surface overlooked patterns, and create transparent systems that augment rather than replace scientific judgment while keeping researchers in control.


### [Introducing Claude for Small Business](https://yomu.fyi/post/introducing-claude-for-small-business.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Aug 27, 2026
- Type: Announcement

Anthropic is launching Claude for Small Business, a package of connectors and ready-to-run workflows intended to put Claude inside small businesses’ existing tools. Through Claude Cowork, owners can connect Intuit QuickBooks, PayPal, HubSpot, Canva, Docusign, Google Workspace, and Microsoft 365, choose a job, and approve actions before anything sends, posts, or pays. The package includes 15 agentic workflows and 15 skills covering finance, operations, sales, marketing, HR, and customer service, including payroll planning, month-end close, invoice chasing, campaign preparation, and contract review. Trust controls retain existing permissions, keep users in the approval loop, and state that Team and Enterprise Plans do not train on customer data by default. The launch also includes a free AI Fluency for Small Business course, a multi-city Claude SMB Tour beginning May 14 in Chicago, and partnerships supporting solopreneurs and community development financial institutions.


### [Claude Science, an AI workbench for scientists](https://yomu.fyi/post/claude-science-an-ai-workbench-for-scientists.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Aug 27, 2026
- Type: Announcement

Claude Science is a beta AI workbench for scientists that brings databases, research tools, computing resources, and domain-specific workflows into one environment. Researchers can analyze literature, run multi-step studies, create figures and manuscripts, and revise them through conversation. Every generated artifact includes its code, environment, plain-language creation description, and message history, while a reviewer agent checks citations, calculations, and consistency between figures and code. A coordinating agent uses more than 60 curated skills and connectors across fields including genomics, single-cell, proteomics, structural biology, and cheminformatics; it can manage jobs on local systems, HPC clusters over SSH, or Modal, scaling from one GPU to hundreds. Beta access is available on macOS and Linux for Pro, Max, Team, and Enterprise users, with reported examples including faster glioma analyses and large literature reviews.


### [The Cheapest Token You Never Send: Agentic AI Search on MongoDB + Voyage AI](https://yomu.fyi/post/the-cheapest-token-you-never-send-agentic-ai-search-on-mongodb-voyage.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Aug 27, 2026
- Type: Problem & solution

The post reframes cost, latency, and answer quality in agentic AI search as a single retrieval precision problem. Its pipeline embeds with flexible Voyage AI dimensionality and automatic quantization in MongoDB index, with binary reducing RAM by roughly 24x. Hybrid search via $rankFusion combines vector and full-text search with metadata filters in one server-side query, eliminating separate systems. A Voyage reranker then reorders the broad candidate set by true relevance, allowing the LLM to receive only the top few high-signal chunks and drastically shrinking input context. The agentic loop over consolidated Atlas where data, vectors, embeddings, and reranking coexist minimizes network hops per iteration, and an illustrative example shows an 80% reduction in retrieved-context input tokens.


### [8 top conversational AI platforms in 2026](https://yomu.fyi/post/8-top-conversational-ai-platforms-in-2026.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Jesse Sumrak
- Published: Aug 26, 2026
- Type: Explainer

Published in August 2026, this guide compares eight conversational AI platforms amid rapid market consolidation and acquisitions including NiCE’s roughly $955 million purchase of Cognigy. It defines the category as software for building, deploying, and operating agents across voice and messaging, then distinguishes vendor-managed services that own reasoning from infrastructure providers that carry conversations while teams own the reasoning. The evaluation criteria include model flexibility, voice and messaging parity, deployment control, cross-channel context, human handoff, pricing, and integration depth. Twilio is presented as an infrastructure layer for teams choosing their own models, with ConversationRelay, Agent Connect, Conversation Orchestrator, Conversation Memory, and Flex supporting channels, continuity, and escalation; other entries include Google Conversational Agents, Kore.ai, and NiCE Cognigy. The guide argues that portable models and conversation data will matter as the conversation layer separates from agent reasoning.


### [Agent Observability: Monitoring Decisions, Not Requests](https://yomu.fyi/post/agent-observability-monitoring-decisions-not-requests.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Aug 25, 2026
- Type: Explainer

Agent observability captures the sequence of decisions an agent makes between model calls, not just request-level metrics, and is distinct from monitoring (health) and evaluation (quality). Unlike deterministic database systems, an agent can succeed at every step yet fail overall—for example, looping forever on a failed fix while spans report success. The primary artifact is a trace, a parent-child tree of spans covering agent invocations, model calls, tool executions, and memory reads and writes. OpenTelemetry GenAI conventions define span types like create\_agent and execute\_tool, but the standards are still in development and change frequently; vendor support includes Datadog, Honeycomb, and frameworks like LangChain and CrewAI. A reasoning trace layer on top of operational telemetry explains why an agent chose that path; in multi-agent systems the trace must span the full coordination chain, not one agent’s output.


### [How to Evaluate Live & Voice Agents in ADK](https://yomu.fyi/post/how-to-evaluate-live-voice-agents-in-adk.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Stephen Allen
- Published: Aug 24, 2026
- Type: Tutorial

ADK now supports native evaluation of live, voice-based agents, addressing regressions in tool use, context continuity, turn-taking, timing, and recovery that may not appear in demos. The walkthrough builds a graph-based workflow of three single-purpose agents running gemini-live-2.5-flash-native-audio, with a mocked date-of-birth tool between stages and session state carried across the open audio stream. It shows how to author scenario-based or fixed conversations, synthesize simulated user turns with Gemini TTS, configure live mode and rubric-based multi-turn trajectory judges, and run evaluations from the CLI or AgentEvaluator. ADK Web then reconstructs the audio as transcripts with playable clips, while configurable voices, languages, invocation limits, thresholds, and per-turn metrics support repeatable debugging and CI/CD regression checks.


### [Top user authentication trends for 2026](https://yomu.fyi/post/top-user-authentication-trends-for-2026.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Sebastian Del Aguila Fiocco, Ravleen Kaur
- Published: Aug 21, 2026
- Type: Explainer

The article outlines authentication trends expected to shape identity verification, fraud prevention, payments, and access in 2026, against a backdrop of deepfakes, synthetic identities, regulatory change, and increasingly online services. It describes identity orchestration that combines document checks, biometrics, device intelligence, and behavioral signals, while fraud systems assess behavior and may allow, challenge, delay, or block access. For AI agents, Know Your Agent links a bot to its enterprise and official communication channel, with document verification and Twilio Lookup presented as a combined onboarding approach. The recommended strategies add continuous anomaly monitoring, risk-based MFA, human-in-the-loop approvals, multichannel authorization, passkeys, digital IDs, and layered controls, while concluding that businesses and regulators should remain agile and use services such as Twilio Verify, Lookup API, and Verify Fraud Guard.


### [The fraud-friction paradox: Why stronger security should feel invisible](https://yomu.fyi/post/the-fraud-friction-paradox-why-stronger-security-should-feel-invisible.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Anurag Dodeja, Reed Mcginley-Stempel, Catie Kolander
- Published: Aug 21, 2026
- Type: Explainer

The post argues that the long-standing trade-off between security and seamless user experience is a false choice, especially as autonomous AI agents act across systems. High-friction measures such as passwords, CAPTCHAs, and one-time login checks can create insecure credential reuse and fail to govern over-permissioned agents or risks that emerge during a session. It proposes invisible, continuous risk evaluation using device, network, phone, email, communication, and behavioral signals, with event-triggered verification when an agent requests a consequential action. The approach also separates human authentication from agent identity and Know Your Agent (KYA), which define delegated authority, contextual rules, and time-bounded permissions while linking an agent to its deploying entity. Twilio is presented as a unified trust layer combining customer data, security intelligence, AI governance, and communication channels; the conclusion is that adaptive controls can protect users while making security largely invisible.


### [Building E-commerce AI Agents on MongoDB with CrewAI](https://yomu.fyi/post/building-e-commerce-ai-agents-on-mongodb-with-crewai.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Aug 19, 2026
- Type: Tutorial

A 34% year-over-year increase in consumers using generative AI for online shopping, especially among Gen Z and Millennials, is motivating e-commerce teams to adopt AI shopping agents. This tutorial demonstrates building a chat-based shopping assistant on MongoDB Atlas with CrewAI that supports zero-click buying from product discovery to post-purchase follow-up. The agent interprets natural-language queries, retrieves semantically relevant products via MongoDB Vector Search with Voyage AI embeddings using price and inventory pre-filters before similarity ranking, and uses Change Streams for atomic inventory checkout. Atlas Triggers enable proactive order notifications, while Atlas Stream Processing detects delivery patterns by monitoring updates in two-minute tumbling windows, writing confirmed signals to agent\_triggers when at least 10 orders per carrier-region are delayed by 45 minutes. MongoDB consolidates operational data, semantic search, and event-driven capabilities into a single platform, reducing latency and complexity.


### [Cloud Agents and Cursor Harness Improvements](https://yomu.fyi/post/cloud-agents-and-cursor-harness-improvements.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Published: Aug 19, 2026
- Type: Announcement

Cursor’s release expands cloud agents and the Cursor harness for longer-running, event-driven software work. Cloud agents can subscribe to pull requests, Slack threads, or scheduled tasks, wake on events, keep goals until completion, and automatically address CI failures and bot comments on pull requests they create. Custom modes pin a selected skill in chat, while /goal supplies a long-lived objective that can be paired with a custom mode or /loop. Subagents can run on separate virtual machines with isolated project copies and clean context, supporting fresh testing or parallel fixes without collisions. Steering improvements let users send follow-ups that wait for the next tool call instead of interrupting an action.


### [Building Shared Memory for AI Agents in Notion](https://yomu.fyi/post/building-shared-memory-for-ai-agents-in-notion.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Hesham Salman
- Published: Aug 18, 2026
- Type: Explainer

Lore is an open-source, MIT-licensed tool from Notion for giving AI agents shared, persistent memory backed by Notion, addressing experiential knowledge, follow-up tasks, decision records, and procedures that can disappear between sessions. A Lore vault uses five related Notion databases—Projects, Topics, Memories, Entities, and Facts—with MCP tools for agents, Notion or a CLI for humans, and hooks intended to retrieve context and save information in the background. The design emphasizes memory maintenance: facts expire by default, relationships such as supersedes and conflicts\_with clarify updates, and the CLI can identify orphaned or duplicated information. In a 500-query SkillRet retrieval evaluation, Lore achieved 84% success, while a paired model evaluation found seeded memory recovered about 46% of failures on tasks never solved without memory. The results indicate that relevant, well-maintained memory can improve agent performance, while stale or irrelevant content can harm it.


### [Leading Indian Organizations Are Driving India’s AI Future With MongoDB](https://yomu.fyi/post/leading-indian-organizations-are-driving-india-s-ai-future-with-mongod.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Aug 18, 2026
- Type: Announcement

Six Indian organizations are using MongoDB Atlas to move AI projects from prototype to production at scale. Emergent Labs built a platform for non-programmers to create applications via natural language prompts, powering nearly 12 million apps. Zomato’s AI customer support platform Nugget improved human-agent efficiency by 40% within four months. Leena AI uses MongoDB Atlas to handle high-volume employee queries in real-time across 500 enterprises. Brahma AI relies on MongoDB as a unified data foundation for its enterprise content management and AI creation platform. These organizations cite MongoDB’s flexible document model, built-in search and vector capabilities, and scalability as key enablers for their AI workflows.


### [Agent Memory Inside the Harness](https://yomu.fyi/post/agent-memory-inside-the-harness.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Aug 18, 2026
- Type: Explainer

The post distinguishes agent state, the run-bound working data of a single execution, from agent memory, knowledge that outlives the run and is read across sessions. Context compaction, which decides what to keep when the window fills, is lossy by construction and sets both token cost and reliability; Chroma’s study found models become unreliable well before their advertised 200k-token window, and teams compact at 95% or proactively at 60%. System prompts and skills must be versioned, governed infrastructure, not text files, with prompt registries and the SKILL.md standard for procedural memory, and skills are executable so they require sandboxing. Enterprise data enters memory through pipelines like change data capture, not migrations, with chunking, embedding, and provenance stamps. The database choice caps filtered recall under selective scoping, the consistency window, and temporal modeling, setting the ceiling for later decisions.


### [Build zero-trust AI agents with Google's Agent Development Kit](https://yomu.fyi/post/build-zero-trust-ai-agents-with-google-s-agent-development-kit.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Shubham Saboo, Eric Dong
- Published: Aug 17, 2026
- Type: Explainer

Connecting an autonomous Agent Development Kit (ADK) agent to databases, internal APIs, and code execution turns model decisions into production-state changes that system prompts cannot securely constrain. The post presents an open-source Customer Support & Returns Agent built with ADK and Gemini, using an example where prompt injection could produce an unauthorized refund or leak environment secrets. Its zero-trust design combines hardware-backed signing for database mutations, gVisor isolation with no network egress and resource limits for generated Python, and deterministic semantic gateways for prompts, tool calls, and database updates. Local equivalents use HMAC keys, a database ingress verifier, a constrained Docker runner, and Python validation tests, while production mapping uses Cloud KMS, HSM, and VPC Service Controls. The conclusion is that infrastructure-enforced boundaries let the model reason dynamically within strict limits.


### [Integrations That Write Themselves](https://yomu.fyi/post/integrations-that-write-themselves.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Shubhayan Srivastava
- Published: Aug 14, 2026
- Type: Problem & solution

Ramp describes an approach to scaling integrations beyond what engineers can build and maintain by hand. Its agentic system lets customers describe a missing workflow integration, researches the provider’s API, requests credentials through a secure widget, writes and tests ordered API-call “recipes,” and generates a deterministic script that runs the integration after build time. An internal Integration Factory applies the same pattern to first-party connectors, producing evidence for review and opening pull requests; the post says 75 integrations have shipped this way, with work reduced from weeks or months to hours. Security controls include HTTPS and hostname allowlists, private-address rejection, isolated egress, bounded responses, business-scoped immutable recipes, and test credentials rather than customer data. The central design lesson is to use models during research and construction, keep them out of execution, and bound autonomy with verification artifacts.


### [Firetiger joins Cursor](https://yomu.fyi/post/firetiger-joins-cursor.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Maxime Prades
- Published: Aug 13, 2026
- Type: Announcement

Cursor announced that the Firetiger team is joining the company, bringing together coding agents with agents focused on software after it reaches production. Founded in 2024 by Rustam Lalkaka and Achille Roussel, Firetiger builds agents that monitor rollouts, catch regressions, investigate incidents, and pass findings back to coding agents. The stated motivation is to reduce operational toil and connect code generation with production feedback, so an agent can ship a change, observe its behavior, and respond when something goes wrong. Firetiger’s work is expected to appear across Cursor as the companies develop long-running, autonomous, context-aware agents for teams, alongside Cursor Origin and the forthcoming Change Monitors, which will watch deployed changes and flag problems as they appear.


### [Announcing Evals and Releases: Evaluate Fin before, during, and after you go live](https://yomu.fyi/post/announcing-evals-and-releases-evaluate-fin-before-during-and-after-you.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Brian Donohue
- Published: Aug 13, 2026
- Type: Announcement

Fin’s new Evals and Releases provide an evaluation and rollout system for testing changes before launch, controlling deployment, and assessing live conversations, alongside Monitors. Evals group multi-turn Simulations built from real conversations around a theme, then score them pass/fail using deterministic checks and an LLM judge against criteria such as required responses, Procedures, or data connector calls. Releases let teams edit content, Procedures, and Guidance away from live Fin, run Evals on proposed changes, collaborate, and publish to everyone, ramp traffic, A/B test, or roll back. Monitors evaluates every live conversation and can turn failures into new Simulations for subsequent testing. The combined workflow addresses probabilistic behavior, regression risk, and drift as support content and policies change, while Operator can draft changes, tests, and Releases for review and approval.


[Newer posts](https://yomu.fyi/topic/ai-agents/page/12.md) · [Older posts](https://yomu.fyi/topic/ai-agents/page/14.md)