---
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

### [Unity Catalog Pages: a governed home for your business knowledge in Genie Ontology](https://yomu.fyi/post/unity-catalog-pages-a-governed-home-for-your-business-knowledge-in-gen.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Tim Feng, Ashish Singh
- Published: Sep 23, 2026
- Type: Announcement

Business concepts like active customer or completed trip are often defined differently across teams, scattered in Slack threads and spreadsheets, causing AI agents to guess confidently when answering questions. Databricks introduces Unity Catalog Pages as a governed home within Genie Ontology where data stewards with Genie Code define the authoritative meaning of those terms and entities. Pages complement existing semantics — metric views how to calculate, domains where to look, and certification what to trust — by capturing what each concept means. Genie Code can bootstrap Pages by extracting definitions from Confluence, Slack, Google Docs, or GitHub via MCP-connected tools, turning weeks of work into minutes. When Genie One uses a Page to ground an answer, the Page is cited as a clickable source so users can inspect the definition and verify the reasoning.


### [Small Talk With Prasun Kumar, CEO and Founder of Oppex AI](https://yomu.fyi/post/small-talk-with-prasun-kumar-ceo-and-founder-of-oppex-ai.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Mitsal Athaya
- Published: Sep 23, 2026
- Type: Explainer

Oppex AI builds AI agents that help developers resolve production incidents by gathering context before the engineer is paged. The agents collect logs, cloud metrics, database health, affected customers, recent code changes, and past incident history, then present a recommendation. Because developer logs are not plain English, the team constructs a knowledge graph of how the entire system connects. They harden the agent by pitting it against a chaos monkey that breaks test systems without revealing the method, forcing the agent to learn. The 15-person team has launched the product with early enterprise customers and aims to bring mean time to resolve under 10 minutes, eventually moving toward autonomous system healing.


### [How to serve trillions of tokens for trillion-parameter coding agents](https://yomu.fyi/post/how-to-serve-trillions-of-tokens-for-trillion-parameter-coding-agents.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Published: Sep 23, 2026
- Type: Explainer

Serving inference for coding agents requires trillion-parameter sequence models like Moonshot AI’s Kimi K2.6 to process trillions of input and output tokens at extremely high relative and absolute performance. To be economically viable, these services must achieve large fractions of hardware peak performance, using contemporary matrix math accelerators like Tensor Cores at petaFLOP per second scale. Modal optimized by batching multiple sequences together, caching KV state, and patching SGLang with NVFP4 micro-scaling on Blackwell GPUs like the B200. Per-replica performance improved 2.8x per user and 5.6x across users, transforming ruinously expensive into a price-competitive service. One deployment processed hundreds of billions of tokens daily and trillions total, showing that careful engineering and benchmarking are key to scaling inference.


### [Collected, stored, and useless: The lifecycle of most customer data](https://yomu.fyi/post/collected-stored-and-useless-the-lifecycle-of-most-customer-data.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Jesse Sumrak
- Published: Sep 23, 2026
- Type: Problem & solution

The post examines the typical lifecycle of customer data in companies, revealing that most data is captured but never effectively used. A survey of 660 business leaders found that 48% struggle to connect customer data across channels, making it the top obstacle over budget or headcount. Data is often captured in volume but siloed across systems, duplicated into separate records for the same customer, and reliant on stale overnight batch updates. This leads to AI agents working with outdated profiles, causing 71% of consumers to abandon conversations when the AI fails to recognize them. The post proposes shortening the path between capture and use through real-time data synchronization, exemplified by Twilio’s Conversations and Conversation Memory products.


### [Introducing Support for Local AI Models in the Antigravity SDK](https://yomu.fyi/post/introducing-support-for-local-ai-models-in-the-antigravity-sdk.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Sachin Kotwani, Taylor Mullen
- Published: Sep 23, 2026
- Type: Announcement

The Antigravity SDK now supports local AI models, starting with Gemma 4 26B A4B via Google AI Edge’s LiteRT, enabling offline agentic assistance on local GPU and RAM with a recommended machine of 24GB VRAM or unified memory. Local execution eliminates API costs and rate limits while keeping code and requests on-device, offering privacy and offline resiliency. The SDK supports hybrid workflows where a cloud model like Gemini 3.8 Flash plans while local Gemma 4 instances execute, as in a code auditing task where 97.2% of tokens (3,322 of 3,417) ran locally. A built-in example generates a live-updating CLI resource monitor using psutil and rich from a single prompt, with the agent autonomously writing the script, creating requirements.txt, and testing the code entirely offline. The SDK also supports any OpenAI-compatible server like Ollama, LM Studio, or vLLM via LocalOpenAIAgentConfig without changing agent orchestration.


### [Database Branching: A Developer's Guide to Git-Style Workflows](https://yomu.fyi/post/database-branching-a-developer-s-guide-to-git-style-workflows.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 22, 2026
- Type: Explainer

Database branching creates isolated database environments from a known parent state using copy-on-write, where branches share unchanged data and only store modifications. For a large parent database like one that is 40 GB, two branches may require just 5.6 MB of additional storage instead of 80 GB with traditional full copies. Developers can test schema migrations like adding a NOT NULL column to a table with millions of rows against realistic data and constraints, CI pipelines provision per-pull-request branches for isolated tests, and AI agents experiment across short-lived environments without risking the parent database. Safe operation relies on protecting parent branches from writes and deletions, restricting sensitive data in disposable branches, automating creation and cleanup, and keeping environments reproducible through version-controlled configuration. Databricks Lakebase provides database branching, enabling teams to adopt Git-style workflows for development, CI/CD, and AI agent use cases.


### [AI Agent Observability: Making Silent Production Failures Visible and Actionable](https://yomu.fyi/post/ai-agent-observability-making-silent-production-failures-visible-and-a.md)
- Company: [Salesforce](https://yomu.fyi/company/salesforce.md)
- Author: Scott Nyberg
- Published: Sep 22, 2026
- Type: Announcement

Agentforce Health Monitoring (AHM) addresses silent availability failures in production AI agents that traditional monitoring misses because agents can appear healthy even when unreachable. The team unified telemetry from approximately five or six locations across sessions, reasoning steps, tool calls, errors, and escalations into a coherent representation. End-to-end alerting latency was reduced from roughly 20 minutes to several minutes by converting Data 360 ingestion to streaming and simplifying query complexity. AHM provides 16+ built-in metrics including agent availability, error rate, response/escalation rate, engagement, and latency, with a drill-down interface connecting alerts to session context. The roadmap includes cost, TTFT/TTLT, toxicity, feedback metrics, RAG Quality Monitoring integration, and eventually a runtime insight agent for root-cause analysis and automated remediation.


### [Genie One MCP: Give any AI Agent the Right Business Context](https://yomu.fyi/post/genie-one-mcp-give-any-ai-agent-the-right-business-context.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Krishna Rupanagunta, Cynthya Peranandam, Siddesh Pore
- Published: Sep 22, 2026
- Type: Explainer

Business leaders often have access to plenty of data but still cannot get a reliable answer to a simple question like why net revenue declined 8% at their largest account last week. The answer may span sales, finance, promotions, inventory, and account data, with each source potentially correct in isolation yet different in definitions, detail, relationships, and authority. Without the right business context, general-purpose agents cannot reliably determine what metrics mean, which sources are authoritative, how data should connect, or what each user is allowed to see. Genie One MCP gives governed context to any MCP-compatible AI agent, connecting assistants like ChatGPT, Claude, Microsoft Copilot, and coding agents to Genie One. This lets them answer business questions using approved definitions, trusted data relationships, and permission-aware access controls, with traceable, permission-aware answers grounded in governed business context.


### [Accelerating a ROS 2 Node with an AI Agent and NVIDIA Isaac ROS](https://yomu.fyi/post/accelerating-a-ros-2-node-with-an-ai-agent-and-nvidia-isaac-ros.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Sep 22, 2026
- Type: Tutorial

GPU acceleration alone does not guarantee fast ROS 2 graphs because messages may still be serialized or copied through CPU memory. NVIDIA contributed the rosidl::Buffer abstraction and CUDA buffer backend to ROS 2 Lyrical, enabling zero-copy transport of GPU-resident payloads between co-located nodes when they share the same host, CUDA device, Linux user, and a supported RMW implementation, with automatic CPU fallback otherwise. An AI coding agent using the migrate-node-to-rosidl-buffer skill can audit an existing CUDA-accelerated node by tracing data movement, running a copy-boundary audit, and implementing a minimal interface-preserving patch. The tutorial demonstrates this on the Depth Anything 3 TensorRT node, updating subscription options and message storage to use CUDA buffer backend without changing the sensor\_msgs/msg/Image message definition. The resulting accelerated workload can be deployed on NVIDIA Jetson AGX Thor, preserving standard ROS 2 messages while avoiding unnecessary serialization and host copies.


### [JetBrains Air: Building a System of Products for Agentic Software Development](https://yomu.fyi/post/jetbrains-air-building-a-system-of-products-for-agentic-software-devel.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Kirill Skrygan
- Published: Sep 22, 2026
- Type: Announcement

JetBrains Air is a system of products for agentic software development, built on the premise that no single model, agent, or service fits every developer, team, or task. It connects products for individual work, team coordination, organizational control, context, and process automation, while remaining open to tools and agents developers choose, including those JetBrains does not build. This openness lets developers select the agent, model, or service suited to a task without forcing the organization to give up context, visibility, or control. The Post notes that every new model, agent, or service an organization adds reduces visibility into its own development work. Because intelligence does not belong to one model, the benefit can extend across supported agents and services.


### [Data Ontology defined: The context layer your AI agents are missing](https://yomu.fyi/post/data-ontology-defined-the-context-layer-your-ai-agents-are-missing.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Richard Tomlinson
- Published: Sep 22, 2026
- Type: Explainer

The Post argues that AI agents are breaking a long-standing assumption in enterprise data: that a human analyst can always resolve ambiguity. Richard Tomlinson explains that while analysts rely on tribal knowledge—like which revenue table is trusted or what ‘active customer’ means—agents lack this context. This gap causes inconsistent answers and erodes trust in self-service tools. The conversation examines why previous ontology efforts stalled and what must be true for an ontology to support agents. The ontology is positioned as the missing context layer that companies have not yet built.


### [The 2026 CDP Shift: Why AI Agents Are Only as Smart as Your Data Infrastructure](https://yomu.fyi/post/the-2026-cdp-shift-why-ai-agents-are-only-as-smart-as-your-data-infras.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Cynthia Goldsworthy
- Published: Sep 22, 2026
- Type: Problem & solution

The 2026 Customer Data Platform Report identifies a critical enterprise challenge: while AI tools grow individually smarter, fragmented customer data causes a worsening, not better, experience. The shift is from computational capacity to contextual continuity, as disconnected pipelines force AI agents to start every interaction from scratch. Three failures arise from fragmented systems: agent amnesia, escalating operational costs from wasted tokens, and trapped intelligence that never reaches active workflows. Modern CDPs have evolved from passive audience builders into API-first context engines that support three pillars: pre-execution context with working memory, unified behavioral and conversational data streams, and infrastructure-level governance enforcing compliance before data reaches AI models. Organizations like Twilio, Trustpilot, and Quartz demonstrate that embedding real-time customer context into the conversation layer outperforms deploying the smartest standalone models.


### [How to design an AI-to-human handoff that preserves context](https://yomu.fyi/post/how-to-design-an-ai-to-human-handoff-that-preserves-context.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Jesse Sumrak
- Published: Sep 22, 2026
- Type: Problem & solution

AI-to-human handoffs often fail because context is lost during transfer, forcing customers to repeat themselves. 76% of consumers report that human agents have little to no context about their issue, and 91% of brands have seen customer satisfaction drop due to AI missteps, with 40% attributed to context loss between channels. A cold transfer drops the customer into a new queue with nothing carried over, while a warm transfer briefs the receiving agent beforehand. Five steps to preserve context include defining escalation triggers early, specifying a payload with conversation history and customer profile, generating a three-line summary, maintaining context across channel changes, and writing back learned information to customer records. Measuring repeat-explanation rate and post-handoff resolution rate helps identify whether handoffs are working or if the AI is over-holding conversations.


### [How Stripe is designing Checkout for AI agents](https://yomu.fyi/post/how-stripe-is-designing-checkout-for-ai-agents.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Cara Mecozzi
- Published: Sep 22, 2026
- Type: Problem & solution

Stripe implemented WebMCP on its Checkout UIs, which processes around 0.45% of global GDP, to optimize the buying experience for AI agents. Previously, agents relied on slow browser automation, consuming an average of 1.8 million tokens and 38.83 tool calls per transaction, taking over 2.5 minutes. WebMCP exposes MCP tools from the client side, allowing agents to execute actions like get\_order\_summary or fill\_payment\_form instead of interpreting the DOM. The implementation uses progressive tool disclosure, revealing only relevant tools; in evaluations across six models, agents using WebMCP consumed 42% fewer tokens, made 38% fewer tool calls, and completed checkout 39% faster. Stripe reused existing UI state and forms via imperative and declarative APIs, avoiding a separate agent-only code path and keeping human and agent experiences aligned.


### [Confused Deputy: The Old Bug That AI Agents Keep Reintroducing](https://yomu.fyi/post/confused-deputy-the-old-bug-that-ai-agents-keep-reintroducing.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: Andrea Chiarelli
- Published: Sep 22, 2026
- Type: Explainer

The Confused Deputy problem, first documented in 1988, describes a program with legitimate authority being tricked into misusing that authority on behalf of a less-privileged actor. AI agents are particularly vulnerable because they process instructions and untrusted content through the same natural-language channel, making prompt injection straightforward. The post traces this pattern through CSRF, SSRF, and recent attacks like Clinejection, where an issue-triage bot executed an attacker-supplied command from a crafted issue title. That attack led to a poisoned GitHub Actions cache and a compromised npm package downloaded 4,000 times. The fix involves task-scoped short-lived credentials, capability-scoped permissions, credential isolation from the model, and human-in-the-loop approval for high-risk actions.


### [How to Evaluate AI Agents From Tool Calls to Task Completion](https://yomu.fyi/post/how-to-evaluate-ai-agents-from-tool-calls-to-task-completion.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Sep 21, 2026
- Type: Explainer

Standard LLM benchmarks are insufficient for evaluating AI agents because they score static outputs rather than multi-step tool-call sequences against a live environment. The post explains that agent evaluation requires two scoring layers: step-level (process scoring) checks each call’s validity and usefulness, while end-to-end (E2E) scoring checks only the final environment state. A trace logs every step and environment state, and metrics roll up through a hierarchy of benchmark, trial, task, turn, and step. Key metrics include task success rate, consistency, tool-call precision, argument accuracy, steps per success, and cost per success. The post argues that benchmarks are converging on tool use because deployed agents always use tools, and enterprise benchmarks should measure task completion against real APIs and policies.


### [AI threat modeling shouldn't be a bottleneck, so we open sourced ours](https://yomu.fyi/post/ai-threat-modeling-shouldn-t-be-a-bottleneck-so-we-open-sourced-ours.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Katie Mulliken, Jon Weiser, Judy Kelly
- Published: Sep 21, 2026
- Type: Announcement

The post announces the open-sourcing of agentic-threat-modeling, an AI-assisted threat-modeling module for Claude Code, Cursor, and Gemini CLI, built to address the bottleneck of security teams reviewing AI-generated code. It runs on Lola, an open source package manager for AI contexts, and profiles likely attackers, generates narrative attack scenarios grounded in actual code, and checks them against six structured threat modeling frameworks (STRIDE, PASTA, LINDDUN, attack trees, and more). The module ties every finding to a specific file and line of code, as shown in a real-world example against a file upload endpoint. It’s designed to produce structured output for AI-assisted triage, though that pipeline isn’t built yet. The team open sourced it because the problem isn’t unique to them, and it’s available in RedHatProductSecurity/agentic-threat-modeling and LobsterTrap/lola.


### [Building Sentry's Laravel AI Integration](https://yomu.fyi/post/building-sentry-s-laravel-ai-integration.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Martin Linzmayer
- Published: Sep 21, 2026
- Type: Announcement

Sentry’s Laravel AI integration, built during an internal Agent Hackweek, provides Agent Tracing for Laravel AI agents. The author used Claude for coding, which initially produced poor generic helper code. Laravel AI emits lifecycle events for agents and tool calls, but not for individual LLM invocations. To capture these, the integration matches outgoing HTTP requests to active invocations using provider URL prefixes, associating with the most recently started invocation if multiple match. Enabling the integration requires only updating to sentry-laravel 4.27, with no Sentry-specific code. Traces appear in Sentry’s Explore > Agents section, showing conversations, tool calls, and their inputs and outputs.


### [Sending SMS with an Agentic AI using Twilio and Hermes Agent](https://yomu.fyi/post/sending-sms-with-an-agentic-ai-using-twilio-and-hermes-agent.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Amanda Lange
- Published: Sep 21, 2026
- Type: Tutorial

The author, a Twilio employee, built a personal agentic AI using Hermes Agent and connected it to Twilio SMS to enable phone-based interaction. They chose SMS over a custom Android app because it works out of the box on any phone and globally, making the agent accessible to anyone with the phone number, including non-technical users. The author wired up a toll-free 800 number and tested the setup by sending a text message from a grocery store parking lot, receiving a reply from the agent. The experiment demonstrated that agentic AI can be integrated with legacy protocols like SMS to provide realtime chat capabilities. The author also noted the importance of reading email provider terms of service if creating a new account for the agent.


### [More data won't make your AI smarter (here's what will)](https://yomu.fyi/post/more-data-won-t-make-your-ai-smarter-here-s-what-will.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Jesse Sumrak
- Published: Sep 21, 2026
- Type: Problem & solution

Twilio research found that 99% of companies report something blocks them from understanding customers, with 48% citing connecting data across channels as the top obstacle. The problem isn’t data collection, which has succeeded, but the missing data activation layer that makes context usable in the eight seconds an AI agent has before a customer gives up. Twilio’s solution involves three elements: continuity across channels, real-time profile freshness, and retrieval of relevant history mid-conversation. Twilio Conversations treats touchpoints as a single lifecycle, Conversation Memory syncs new details in real time, and Conversation Intelligence surfaces approved documents alongside history. Twilio recommends starting small by picking one handoff where customers repeat themselves and making context survive it, rather than feeding the AI more data.


[Newer posts](https://yomu.fyi/topic/ai-agents/page/6.md) · [Older posts](https://yomu.fyi/topic/ai-agents/page/8.md)