---
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

### [Choosing a low-latency infrastructure layer for conversational AI](https://yomu.fyi/post/choosing-a-low-latency-infrastructure-layer-for-conversational-ai.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Luke Morgan
- Published: Sep 8, 2026
- Type: Problem & solution

Conversational AI call-center latency can feel like dead air, and the delay may come less from LLM inference than from the infrastructure moving audio among telephony, media servers, ASR, the agent, and TTS. The described approach uses a unified voice path: ConversationRelay provides a WebSocket API to stream live call audio, handle speech-to-text and text-to-speech in one connected pipeline, connect with a backend agent, and support barge-in. It is model-agnostic, allowing teams to retain their LLM, knowledge bases, AI tools, and third-party voice providers, while Conversation Orchestrator, Conversation Memory, and Agent Connect address context and integration. Twilio’s internal benchmarks report median latency below 0.5 second and below 0.725 second at the 95th percentile, though relevance and context also matter alongside speed.


### [Audit Your Auth0 Tenant with Auth0 Agent Skills](https://yomu.fyi/post/audit-your-auth0-tenant-with-auth0-agent-skills.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: Carlos Aguilar
- Published: Sep 8, 2026
- Type: Announcement

Auth0 Agent Skills addresses the gap between generic AI coding agents and a tenant’s live Auth0 configuration, plan entitlements, and deployed application settings. Its Auth0 HealthCheck skill uses Auth0 CheckMate to scan security and configuration hygiene, assess capability and plan fit, and produce confidence-weighted scores, prioritized findings, reports, and upgrade guidance. Using the Auth0 CLI, the skill authenticates locally, creates or reuses a narrowly scoped CheckMate machine-to-machine application, and queries the Management API from the developer’s environment. Remediation follows a developer-in-control flow: the skill fetches current settings, previews the proposed tenant-specific change and CLI command, requests explicit approval, then applies and verifies approved updates. The collection also provides framework quickstarts, B2B and B2C architecture guidance, debugging help, and configuration automation through Auth0 CLI, Terraform providers, and Vercel-native integrations.


### [From incident to remediation: Building an AI-driven AIOps workflow with Red Hat Ansible Automation Platform](https://yomu.fyi/post/from-incident-to-remediation-building-an-ai-driven-aiops-workflow-with.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Neha Chugh
- Published: Sep 5, 2026
- Type: Tutorial

This tutorial builds an AIOps workflow for diagnosing and remediating a deliberately induced CPU incident in a product catalog application running on an AWS EC2 instance with RHEL. An Ansible investigation playbook collects CPU utilization, top processes, application status, and HTTP health evidence, which Claude Code analyzes through MCP to identify the Python process as the likely contributor and recommend the approved “AIOps - Remediate High CPU” Job Template. AI does not access the host directly: Ansible Automation Platform retains responsibility for credentials, inventory, execution, and remediation. The remediation changes the application from HTTP 503 and degraded to HTTP 200 and healthy, while a final health check confirms recovery and completes the detect-investigate-analyze-recommend-execute-verify sequence.


### [Five ways marketers can use Genie One](https://yomu.fyi/post/five-ways-marketers-can-use-genie-one.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Cynthya Peranandam, Sydney Sundell
- Published: Sep 4, 2026
- Type: Explainer

Marketing teams often have data across campaign, customer, attribution, and pipeline systems but still depend on analysts to answer questions quickly and consistently. Databricks Genie One is presented as a data-smart AI coworker that uses governed business data, organizational context, metric definitions, campaign structures, reporting rules, and access permissions to support marketing analysis. The five described uses cover channel and campaign performance, funnel attribution, customer insights, omnichannel planning, and mobile business reporting, with natural-language follow-ups, reusable skills or Genie Agents, scheduled analyses, dashboards, and shareable outputs. Examples include tracing sourced pipeline changes, identifying declining enterprise-account usage, and finding territories with weakening repeat purchases. The source recommends starting with repeatable, cross-source workflows and preparing data sources, attribution models, fiscal-calendar logic, and team terminology in Databricks; marketers retain responsibility for decisions and campaign plans.


### [Building a Memory-Driven Agent with NVIDIA NemoClaw](https://yomu.fyi/post/building-a-memory-driven-agent-with-nvidia-nemoclaw.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Sep 4, 2026
- Type: Tutorial

Enterprise agents need more than conversation history to connect decisions, obligations, people, and projects that change over time. Using NVIDIA NemoClaw, the example builds a memory-driven Chief of Staff around a human-readable self model in structured Markdown, a SQLite ledger for obligations and judgments, scheduled maintenance, bounded retrieval, and user correction with append-only auditing. Its architecture separates evidence, knowledge, and governed execution, while NVIDIA OpenShell sandboxes runtime access and keeps managed inference and MCP credentials outside the sandbox; memory can inform an action but cannot authorize it. In the supplied evaluation, the self model reached 90.9% overall accuracy versus 82.8% for an agentic RAG baseline, with larger gains on hard questions, changing facts, point-in-time reasoning, entity disambiguation, and multisource synthesis, but lower scores on corpus-faithful answers and single-hop lookup.


### [Driving Developer Excellence: Inside the Program Sprints](https://yomu.fyi/post/driving-developer-excellence-inside-the-program-sprints.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Anant Nawalgaria, Eric Schmidt, Sokratis Kartakis, Aman Khan
- Published: Sep 4, 2026
- Type: Announcement

Google Cloud’s Gemini Enterprise developer experience (DevEx) Program pressure-tests enterprise AI tooling by walking fixed developer workflows without internal credentials or shortcuts, logging friction, and working with engineering on fixes. This sprint focused on governance across five dependency-ordered workflows: provisioning an agent identity, registering it, binding it to an Agent Gateway, applying policies and content safety, and verifying an authorized request with auditable decisions. Changes included documenting the Identity-Aware Proxy API prerequisite, fail-closed Model Armor extension examples, automatic allowance for essential Google-managed APIs, Private Service Connect and private Cloud DNS guidance, corrected IAM CEL syntax, clearer bind-time versus runtime evaluation, and ready-made Logs Explorer queries. The program says fixes are rigorously re-verified through continued user-journey testing to improve reliability, security, and end-to-end usability.


### [What is bring your own LLM (BYO LLM)?](https://yomu.fyi/post/what-is-bring-your-own-llm-byo-llm.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Jesse Sumrak
- Published: Sep 4, 2026
- Type: Explainer

Bring your own LLM (BYO LLM) is an architecture in which teams choose the model powering their AI agents while a platform supplies communication infrastructure. The division leaves model selection, prompts, reasoning logic, tool definitions, and called systems with the team, while channels, conversation context, audio streaming, identity resolution, and human escalation remain with the platform. The post explains why bundled models create quality, migration, compliance, and cost-control constraints, including the inability to route simple interactions to smaller models and complex work to frontier models. It describes adding a third-party model to an existing contact center through middleware, a shared message handler, tools and knowledge, channel registration, and context-preserving handoff. Twilio Agent Connect is presented as a self-hosted Python and TypeScript SDK under the MIT License, with voice streaming and integrations for existing contact-center workflows.


### [Governance beyond security: knowledge, context & ontology on the lakehouse](https://yomu.fyi/post/governance-beyond-security-knowledge-context-ontology-on-the-lakehouse.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Srikanth Mandalapu, Travis Paulson, Bernie Kuan
- Published: Sep 3, 2026
- Type: Explainer

The post argues that healthcare AI governance must extend beyond security controls to include knowledge, context, and ontology: security governs access, while semantics determine what data means and whether models can trust it. DEP frames classification tags, model cards, data contracts, and lineage as machine-readable governance artifacts, with Unity Catalog serving as the metadata runtime for agents. Build agents can generate ETL, testing, de-identification, and deployment, while analytic agents answer questions over individual data products; human stewards approve proposals and own accountability. A shared lifecycle certifies the data product and AI track through gates using scores for governance, quality, semantics, and ownership, with certification expiring after relevant changes or failures. The approach applies data-layer ABAC to SQL and vector retrieval, logs agent prompts with lineage, and connects readiness metrics to model economics by enabling smaller models when cataloged data supplies sufficient context.


### [How Agentforce Achieves 100% Deterministic Rendering for AI Agent UX](https://yomu.fyi/post/how-agentforce-achieves-100-deterministic-rendering-for-ai-agent-ux.md)
- Company: [Salesforce](https://yomu.fyi/company/salesforce.md)
- Author: Scott Nyberg
- Published: Sep 3, 2026
- Type: Explainer

Agentforce faced a production problem: LLM-driven rendering could recognize a required form or regulated disclosure yet choose a different output because its reasoning was stochastic. The Agentforce Connections layer addresses this by separating probabilistic reasoning and orchestration from deterministic presentation: builders associate an action with a connection-specific response format through the render: directive, and the platform applies that format instead of asking the LLM to select a component. Multi-action, long-running, and supervised flows required the system to identify the configured action and render only after approval and execution. The same abstraction supports Lightning Web Components, third-party interfaces, and headless structured JSON outputs. Per-action, per-session event logs record the action, format, connection, and conversation point, providing auditable evidence that required rendering occurred.


### [Portal by Spotify cut my Claude Code token usage by 90%](https://yomu.fyi/post/portal-by-spotify-cut-my-claude-code-token-usage-by-90.md)
- Company: [Spotify](https://yomu.fyi/company/spotify.md)
- Author: Spotify Engineering
- Published: Sep 3, 2026
- Type: Problem & solution

Claude Code often spends frontier-model tokens on file I/O and predictable code generation rather than reasoning, motivating a routing setup built with Portal by Spotify’s AiKA Modes. The implementation defines two public modes—bulk-reader for multi-file analysis and code-writer for pattern-based generation—both shown with Gemini 2.5 Flash, while a Claude Code plugin named shunt enforces delegation through hooks, scripts, and skills. PreToolUse hooks block large Read and shell-based file reads above a configurable threshold, while scripts send files or references to ephemeral worker invocations without exposing the full file corpus or generated code to Claude’s context. In a Java monorepo benchmark, mean bulk-read savings were around 90%, although code-write savings were harder to measure. The approach does not suit editing or reasoning tasks: worker summaries lacked reliable line numbers, missed a subtle thread-safety bug, and added 10–30 seconds of latency, so targeted reads and Claude’s reasoning remain necessary.


### [Spotify’s Backstage Portal cut my Claude Code token usage by 90%](https://yomu.fyi/post/spotify-s-backstage-portal-cut-my-claude-code-token-usage-by-90.md)
- Company: [Spotify](https://yomu.fyi/company/spotify.md)
- Author: Spotify Engineering
- Published: Sep 3, 2026
- Type: Tutorial

Claude Code often spends frontier-model tokens on file I/O rather than reasoning, prompting a routing approach that sends predictable work to cheaper models. The post implements this with two public AiKA Modes in Spotify’s Backstage Portal: bulk-reader summarizes large file sets, and code-writer generates patterned files from a specification and reference file, using Gemini 2.5 Flash in the examples. A Claude Code plugin called shunt enforces delegation through PreToolUse hooks, scripts, and skills; reads above a configurable threshold, defaulting to 350 lines, are blocked and redirected. In tests on a Java monorepo, bulk-read saved around 90% of Claude’s tokens, while generated code can go directly to disk. The design does not cover editing or reasoning, and 10–30-second delegation latency makes small tasks counterproductive.


### [NVIDIA PAIR Virtual Inference Router Expands Available Compute on Your Local Network](https://yomu.fyi/post/nvidia-pair-virtual-inference-router-expands-available-compute-on-your.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Sep 3, 2026
- Type: Explainer

NVIDIA Personal AI Router (PAIR) addresses the GPU bottleneck created when multi-agent workflows and concurrent local sessions generate many independent inference requests. It acts as a virtual inference router rather than an inference engine, proxying compatible Ollama and LM Studio interfaces, discovering and securely pairing local systems, and scheduling each request onto one eligible node based on readiness, engine state, model availability, workload, and GPU utilization. The agent keeps one familiar connection, while PAIR streams responses back and exposes placement through Jobs and metrics views; it does not merge GPUs, pool VRAM, or split a single request across machines. In an unofficial Hermes five-subagent demonstration, a three-device cluster averaged 8 minutes 48 seconds versus 18 minutes on one RTX Spark laptop, with results explicitly dependent on configuration and workload.


### [The economics of agent scale: tokens, ROI, and building platforms for AI-first teams (Part 2)](https://yomu.fyi/post/the-economics-of-agent-scale-tokens-roi-and-building-platforms-for-ai.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Eira May
- Published: Sep 3, 2026
- Type: Explainer

The conversation examines the economics and platform implications of operating dozens or hundreds of AI agents rather than a small number of human-driven workflows. Andi Gutmans argues that many current models are already sufficient for automation, shifting the optimization problem to the complete system: model, harness, context, personalization, and evaluation. The goal is the least sophisticated model and smallest viable context that produce a reliable outcome at the lowest cost, with better trajectories reducing reasoning loops and token processing; Google’s stated approach is to hill climb models and data together. The discussion also addresses cost governance, search and observability for agents as a distinct persona, human-in-the-loop handling, and the continuing importance of computer-science fundamentals, agentic fluency, and business judgment.


### [Evaluate AI agents with IBM CLEAR & EvalHub on OpenShift AI](https://yomu.fyi/post/evaluate-ai-agents-with-ibm-clear-evalhub-on-openshift-ai.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Hema Veeradhi, Surya Pathak
- Published: Sep 3, 2026
- Type: Tutorial

Reliable AI agents can fail in production through incorrect tool use, off-topic reasoning, or plausible but incorrect answers that unit tests, static benchmarks, and logs may not expose. This tutorial builds an eval-driven trace-evaluation pipeline on Red Hat OpenShift AI 3.4 or later, using MLflow to store structured agent traces and evaluation artifacts, IBM CLEAR to score reasoning quality, relevance, and tool usage with an LLM judge, and EvalHub to orchestrate OpenShift jobs. It uploads eight prerecorded multistep research-agent traces, each containing at least 30 spans, then verifies them in MLflow before deploying EvalHub, registering the ibm-clear provider, and configuring judge-model credentials. Standard mode evaluates steps holistically, while SPARC mode gives tool calls separate scoring rows; the resulting workflow produces critiques and ranked failure patterns with frequency, severity, and step or agent attribution.


### [MongoDB VFS for LangChain Deep Agents: A Searchable Filesystem for Agents](https://yomu.fyi/post/mongodb-vfs-for-langchain-deep-agents-a-searchable-filesystem-for-agen.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 3, 2026
- Type: Announcement

Modern AI agents are expected to complete real work, not just answer questions, which requires reliable filesystem navigation and updates. The MongoDB virtual filesystem (VFS) for LangChain Deep Agents connects this filesystem experience to MongoDB Atlas and an underlying object store like S3. It provides agents with a searchable workspace that persists across sessions, deployments, and sub-agents. Developers can use MongoDB Atlas as the data platform, including MongoDB Search, Vector Search, and hybrid search, without changing agent code. This architecture allows agents to plan, inspect source material, create artifacts, and resume work over hours or days without loading the entire corpus into context.


### [MongoDB Enterprise Advanced for AI-Ready Operational Insights in Manufacturing](https://yomu.fyi/post/mongodb-enterprise-advanced-for-ai-ready-operational-insights-in-manuf.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 3, 2026
- Type: Explainer

Unplanned manufacturing downtime costs $2.3 million per hour in the automotive sector, and a workforce shortage means 26% of existing workers will retire by 2030. Existing predictive maintenance systems operate in isolation, increasing mean equipment recovery times from 49 to 81 minutes per incident. MongoDB Enterprise Advanced provides a unified data platform for multi-agent AI systems that combine sensor telemetry, vector embeddings, agent memory, and operational records in a single database. Its document model, hybrid search, time series collections, and change streams enable agents to perform semantic retrieval, keyword search, and real-time stream processing. A multi-agent workflow includes a monitoring agent that detects anomalies, a diagnostics agent that retrieves historical patterns, a planning agent that drafts work orders with human-in-the-loop review, and a scheduling agent that checks production calendars and inventory, all on a single on-premise deployment with enterprise security and local embedding generation.


### [4 must-ask questions before you delegate to your AI agent](https://yomu.fyi/post/4-must-ask-questions-before-you-delegate-to-your-ai-agent.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Jesse Sumrak
- Published: Sep 3, 2026
- Type: Problem & solution

Automation roadmaps that prioritize ticket volume and low complexity can improve containment while lowering customer satisfaction when customers did not want a task automated. The post proposes four screening questions: how reversible a mistake is, whether the task touches money, health, or identity, whether the customer needs a transaction or to be heard, and whether the AI can access the necessary context. It cites a Twilio survey of 7,652 consumers, including 80% comfortable with appointment or reservation scheduling and 60% unwilling to have AI handle a medical inquiry. Tasks that fail the test can be split between AI and people: AI gathers information, verifies identity, retrieves history, and drafts a recommendation before a human decides, with Twilio Conversation Orchestrator handing conversation history and profile to Twilio Flex. The recommendation is to start with tasks where errors are relatively cheap and the data is available.


### [Your Agent Speaks MCP. Give It a Computer.](https://yomu.fyi/post/your-agent-speaks-mcp-give-it-a-computer.md)
- Company: [Fly.io](https://yomu.fyi/company/fly-io.md)
- Published: Sep 3, 2026
- Type: Announcement

Sprites are presented as disposable cloud computers for running coding agents and as tools agents can create for testing, debugging, benchmarking, and multi-service work. The post describes MCP as the transport and authorization layer, while progressive disclosure comes from plugins and skills that reveal capabilities only when needed; CLI, REST, and machine-readable /.sprite/llm.txt remain available. The hosted MCP server returns file reads as MCP resources and attaches safety annotations, marking read-only and destructive operations and flagging exec and service\_start as crossing the Sprite boundary. Agents connect through sprites.dev/mcp and authenticate to a specific Fly.io organization, with configurable scoping, a default five-Sprite cap, and an mcp- name prefix. The design emphasizes durable filesystems, real networks, and near-zero idle cost instead of stateless sandboxes, while the post argues these properties make disposable computers practical for agent workflows.


### [Give Your Coding Agents a Memory You Own](https://yomu.fyi/post/give-your-coding-agents-a-memory-you-own.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: David Corvoysier
- Published: Sep 3, 2026
- Type: Announcement

funes is presented as a durable memory layer for coding agents that turns existing session traces into searchable, provenance-backed working memory across machines and agent types. A single binary adds recall and get tools to Claude Code, Codex, pi, or Hermes, builds an incremental local index, and automatically indexes completed turns. Its deterministic pipeline normalizes traces, chunks them, embeds them with a pinned local model, stores them in Lance, and combines vector and BM25 search with rank fusion, cross-encoder reranking, recency weighting, and neighboring chunks. Memories can remain local or be bound to a private Hugging Face dataset owned by the user, with credential redaction, secret scanning, local caching, and Hub-provided access control and versioning. In the handoff-vs-recall benchmark, recall was the cheapest option on both tasks, costing 8x less than a written handoff on one and 4x less on the other, while compaction succeeded on only one task.


### [Southern Company’s SCOUT: Completing the Storm Intelligence Story](https://yomu.fyi/post/southern-company-s-scout-completing-the-storm-intelligence-story.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Boyd, Blake Moore, Salome Abekhe
- Published: Sep 2, 2026
- Type: Explainer

Southern Company’s SCOUT is a real-time storm operations application designed to make outage intelligence accessible beyond control-center desks during active events. Built on the Databricks lakehouse, it combines Delta Lake storage, Databricks Lakehouse warehouses, Unity Catalog governance, and collaborative notebooks, querying a dedicated warehouse every minute for current operational data. The mobile-friendly application unifies outage, customer, GIS, weather, terrain, crew, and reliability information, helping teams assess restoration times, assign appropriately equipped crews, coordinate mutual assistance, and communicate with customers. SCOUT is used across Southern Company’s operating businesses and had 1,139 adopted employees, including more than 250 users on a peak storm day; the source also describes exploration of AI crew recommendations and autonomous-drone integration, not a completed deployment.


[Newer posts](https://yomu.fyi/topic/ai-agents/page/10.md) · [Older posts](https://yomu.fyi/topic/ai-agents/page/12.md)