---
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

### [Conversation design: How to make your AI Agent communicate like your team](https://yomu.fyi/post/conversation-design-how-to-make-your-ai-agent-communicate-like-your-te.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Fred Walton
- Published: Jun 18, 2026
- Type: Tutorial

AI agents need deliberate conversation design to communicate effectively with customers, covering tone, response structure, handoffs, interaction flow, and response quality. The post argues that without an explicit owner, an agent may over-explain, sound flat during frustration, or escalate too late, undermining trust and prompting customers to seek human help. It recommends defining a concise voice reference, adapting register to the situation, matching detail to the question, supporting customer-led topic changes, designing informative handoffs and follow-ups, and keeping behavioral instructions separate from knowledge-base content. Teams can begin without a dedicated hire by naming an owner, selecting a poorly performing conversation type, auditing instructions, and fixing the worst handoff first. At Fin, an A/B test raised CSAT from 72.8% to 78.4%.


### [How A2A is Building a World of Collaborative Agents](https://yomu.fyi/post/how-a2a-is-building-a-world-of-collaborative-agents.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Alan Blount, Frank Guan, Nick Losier
- Published: Jun 18, 2026
- Type: Announcement

A2A, launched one year before this June 18, 2026 update, is presented as a common language for conversational, dynamic agents that need to collaborate and hand off tasks securely rather than behave like stateless tools. The protocol uses black-box handoffs to keep sensitive data and proprietary processes encapsulated, prevent context pollution, support clarifying exchanges, and distribute specialized workloads across teams or services. Foldrun demonstrates this model by exposing protein-structure prediction as a standalone agentic interface that manages long-running tasks, adjusts parameters based on prediction confidence, and selects among AlphaFold 2, OpenFold 3, and Boltz-2 depending on the molecule. In an A2A-compliant environment, users can register Foldrun and delegate work in natural language without custom glue code, while a primary agent handles other research tasks. The post also cites applications in commerce, streaming, IT operations, and regulated telecom, while Python and Go SDKs are 1.0 GA.


### [What is an AI Agent Harness?](https://yomu.fyi/post/what-is-an-ai-agent-harness.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 17, 2026
- Type: Explainer

An AI agent harness is the software infrastructure around a large language model (LLM) that lets it act on tasks rather than only answer prompts. The model reasons and chooses actions, while the harness connects tools, execution environments, memory, external systems and safety controls. Many agents follow a reason–act–observe loop: the model proposes an action, the harness executes it, captures the result and returns that result as context for the next decision. Production harnesses typically include system prompts, tool execution, sandboxes, filesystems and durable storage, context management, feedback and self-verification, guardrails, human-in-the-loop approvals, and observability; their design increasingly shapes reliability as model capabilities converge.


### [Databricks and NVIDIA: Building for the Agentic Era](https://yomu.fyi/post/databricks-and-nvidia-building-for-the-agentic-era.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Hanlin Tang, Tejas Sundaresan
- Published: Jun 17, 2026
- Type: Announcement

Databricks and NVIDIA describe a full-stack partnership for enterprise AI, spanning governed model training, inference, agent infrastructure, and industry-specific AI. Databricks AI Runtime brings NVIDIA Hopper GPUs and Quantum InfiniBand to governed data for distributed training, with Blackwell preparation, Free Edition GPU support, and planned NGC container support. Model Serving uses NVIDIA hardware and Triton Inference Server for low-latency, high-throughput production inference, while NVIDIA Vera targets agent harnesses, tool calls, analytics, and multi-step orchestration with up to 3x faster SQL queries and 80% faster agentic performance. The collaboration also places NVIDIA Agent Toolkit on Databricks Apps, integrates GPU debugging through Genie Code, and brings frameworks such as BioNeMo, Parabricks, cuOpt, and Isaac Sim to Databricks workflows. The stated vision is an end-to-end stack in which GPUs handle models and purpose-built CPUs handle surrounding agentic workloads, with governance supplied by the Databricks platform.


### [The Partner Well-Architected Framework: What's New and What's Next](https://yomu.fyi/post/the-partner-well-architected-framework-what-s-new-and-what-s-next.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Porter
- Published: Jun 17, 2026
- Type: Announcement

The Partner Well-Architected Framework (PWAF) has moved partner guidance from static PDFs to AI-ready guidance spanning Built-On, Connected, and Data Collaboration architectures. It combines an architecture center with technical standards, patterns, and instrumentation guidance intended to align integrations with partner-engineering validation while making adoption and DBU impact measurable. Since February, Databricks has added a Dev Kit containing 15+ tested AI-developed skills, new or refreshed guidance for Clean Rooms, software-defined storage, Marketplace apps, Genie, Lakebase, and MCP server onboarding, and an open-source Firefly Analytics reference implementation. The framework is positioned as an evolving, agent-compatible way to shift routine integration work to AI tools, freeing partner and Databricks engineers to focus on complex architecture; more patterns, skills, reference implementations, and demos are planned.


### [What’s coming next to Free Edition](https://yomu.fyi/post/what-s-coming-next-to-free-edition.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Will Valori, Trang Le
- Published: Jun 17, 2026
- Type: Announcement

Databricks is expanding Free Edition, launched twelve months ago to help people learn modern data and AI, after providing more than $10M in credits to over 500,000 learners. The five additions are Genie Code, serverless GPUs subject to availability, Lakebase, Agent Bricks, and Lakeflow Designer. Genie Code can generate, run, interpret, and refine code autonomously for tasks such as dataset analysis, pipeline cleaning, and visualization, while GPUs support deep-learning workloads including training, fine-tuning, inference, and large-dataset processing. Lakebase adds a fully managed Postgres-compatible database for data apps and AI agents; Agent Bricks supplies composable tools, memory, orchestration, and evaluation; and Lakeflow Designer enables visual pipeline construction. Together, these additions are presented as a complete free toolkit covering data engineering, analytics, machine learning, app development, and AI agents, allowing users to build end-to-end data and AI projects in Free Edition.


### [Becoming the most comprehensive data & AI ecosystem on earth](https://yomu.fyi/post/becoming-the-most-comprehensive-data-ai-ecosystem-on-earth.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Stephen Orban
- Published: Jun 17, 2026
- Type: Announcement

Databricks describes a year of expanding its partner ecosystem for independent software vendors and data providers, responding to requests for stronger go-to-market support, technical guidance, and Marketplace transactions. It says it introduced partner tiering, the Partner Well-Architected Framework, a Marketplace Commit Drawdown pilot, and the ability to list Databricks Apps and Genie Agents. Under the pilot, customers with a Universal Commit can submit invoices for eligible solutions that run on or share data to Databricks; validated purchases reduce their commitment obligation, while Databricks compensates its sales team. OpenSharing is presented as an open-source, vendor-agnostic protocol for cross-cloud, cross-platform sharing of structured and unstructured data, volumes, agent skills, AI models, and semantics. The post says Apps adoption has grown fivefold since last year's summit, with more than 5,000 accounts running Apps in production weekly, and that more than 20,000 customers rely on Databricks.


### [What’s new in Genie Code at Data + AI Summit 2026](https://yomu.fyi/post/what-s-new-in-genie-code-at-data-ai-summit-2026.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Julia Powell, Gal Oshri, Weston Hutchins
- Published: Jun 17, 2026
- Type: Announcement

At Data + AI Summit 2026, Databricks announced expansions to Genie Code for complex, agentic data and ML work. The changes include a full-page command center for managing concurrent threads and assets, upgrades across production ML engineering, and scheduled tasks that run prompts while users are away. For ML workflows, Genie Code uses Databricks production expertise and Genie Ontology, integrates with MLflow and Model Serving, and can move GPU jobs to AI Runtime while using workspace environment features. It can write features, coordinate edits, run and debug code, compare candidates, inspect endpoint health, and diagnose issues, with users deciding what to keep. Scheduled tasks are described as coming soon, creating reviewable threads from prompts and optional Databricks assets.


### [Building an open ecosystem for AI governance with Unity AI Gateway](https://yomu.fyi/post/building-an-open-ecosystem-for-ai-governance-with-unity-ai-gateway.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Nasi, Kelly Albano, Ashish Kathapurkar
- Published: Jun 17, 2026
- Type: Announcement

Databricks announced the Unity AI Gateway partner ecosystem, extending enterprise AI governance beyond models to runtime interactions among models, agents, MCP servers, skills, and AI tools. Built on Unity Catalog, the gateway lets organizations apply policies, monitor activity, manage spend, and govern AI across providers and frameworks, while integrating security, identity, and governance products they already use. The announcement groups the integrations into runtime AI security, observability and guardrails; agent identity and access governance; and AI observability and risk monitoring. Named integrations include Alice, CrowdStrike Falcon AI Detection and Response, Cyera, HiddenLayer, Netskope, Noma Security, Obsidian Security, Openlayer, Okta, Ping Identity, SailPoint, and Saviynt, with described capabilities including prompt-injection detection, data-loss prevention, agent discovery, authorization, and lifecycle governance.


### [What’s New in the AI Platform: Agents for ML Engineering, Our Deep Learning Platform, and New Capabilities for Real-Time ML](https://yomu.fyi/post/what-s-new-in-the-ai-platform-agents-for-ml-engineering-our-deep-learn.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Tejas Sundaresan, Mike Del Balso
- Published: Jun 17, 2026
- Type: Announcement

The announcement presents three additions to the Databricks AI Platform: Genie Code support for ML engineering, AI Runtime’s serverless GPU environment, and expanded real-time ML capabilities. Genie Code integrates with Unity Catalog, Feature Store, training, serving, monitoring, and MLflow, assisting with feature engineering, model training, deployment, evaluation, and production operations. AI Runtime provides on-demand serverless NVIDIA A10 and H100 GPUs, supports high-performance multinode training with RDMA and high-performance data loading, and adds Lakeflow Jobs, DABs, MLflow, and Unity Catalog integration. For real-time ML, the platform adds declarative feature engineering, streaming features, online feature serving on Lakebase, and enhanced Model Serving targeting 300K+ QPS with under 10ms p99 latency overhead. Reported customer examples include faster workflows, lower infrastructure costs, and production scaling beyond 100K QPS.


### [Introducing the Agentic CDP: A New Species of CDP for a New Era of Agents](https://yomu.fyi/post/introducing-the-agentic-cdp-a-new-species-of-cdp-for-a-new-era-of-agen.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Tasso Argyros, Ali Ghodsi, Reynold Xin
- Published: Jun 17, 2026
- Type: Announcement

Traditional customer data platforms (CDPs) were built for human-managed, batch-based campaigns, but the post argues that agentic buying requires millisecond speed, hyper-personalization, and richer context. It contrasts the familiar Golden Record with Golden Context, which combines customer data with current business goals and the history and outcomes of prior decisions. The proposed Agentic CDP uses “Infinity Campaigns,” always-on engagement loops that use LLMs and agents to adapt messaging, timing, and channels for individuals. It is also embedded in the data foundation, bringing customer, business, and decision context together under existing governance, and is designed for agents and humans from the outset. Databricks presents CustomerLake as an implementation of these principles for its platform.


### [Cloud Environment Setup and Cloud Subagents in Agents Window](https://yomu.fyi/post/cloud-environment-setup-and-cloud-subagents-in-agents-window.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Published: Jun 17, 2026
- Type: Announcement

This release updates cloud agents in the Agents Window of the Cursor desktop app, adding cloud environment setup, cloud subagents, and more reliable movement between local and cloud sessions. Cursor can help configure a development environment in under 10 minutes while showing progress in a shared terminal, then capture the result as a reusable snapshot for faster startup and software testing by future agents. The /in-cloud command launches a subagent in its own virtual machine and branch, isolating long-running or parallel tasks while the local workspace remains responsive; /babysit lets a cloud agent iterate on a pull request remotely. Cloud subagents can run in the background, and sessions can be pulled back to a local computer for testing, with multiple cloud agents running in parallel.


### [Announcing the Agentic Resource Discovery specification](https://yomu.fyi/post/announcing-the-agentic-resource-discovery-specification.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Junjie Bu, Srinivas Krishnan
- Published: Jun 17, 2026
- Type: Announcement

Agentic Resource Discovery (ARD) is an open specification for publishing, discovering, and verifying AI capabilities across the web, addressing the lack of a standard that works across organizations and ecosystems. It uses organization-hosted catalogs, including an ai-catalog.json file at a well-known domain path, plus federated registries that crawl, index, and return capabilities with trust metadata. Clients can query a registry with plain-language intent or fetch a known catalog directly, verify the publisher’s cryptographic identity, then connect at runtime through the capability’s native protocol or API. Google Cloud is bringing ARD to Gemini Enterprise Agent Platform through Agent Registry, which supports hosted discovery and governance features including namespaced URNs, egress policies, specification pinning, and Agent Identity trust-manifest verification. The specification is available under Apache 2.0, with native Agent Platform support planned in the coming months.


### [Introducing OpenSharing: the Next Evolution of Delta Sharing for the Agentic Era](https://yomu.fyi/post/introducing-opensharing-the-next-evolution-of-delta-sharing-for-the-ag.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Huey Han, Harish Gaur, Akram Chetibi, Mengxi Chen
- Published: Jun 16, 2026
- Type: Announcement

OpenSharing is presented as the next evolution of Delta Sharing, extending an open zero-copy data-sharing protocol from tables and files to models, agents, semantic context, unstructured data, and reusable AI logic. The protocol is now an independent open-source project hosted by the Linux Foundation, while Databricks OpenSharing adds Unity Catalog governance and audit logging, Marketplace discoverability, and enterprise features. Genie Agent Sharing supports governed AI experiences across organizational boundaries, with controls for proprietary instructions, data access, daily prompt quotas, and row exports. SecureConnect removes per-recipient firewall changes through a Databricks-managed proxy, while Global Distribution uses local replicas to reduce egress fees and latency. The launch also supports Apache Iceberg REST Catalog API, external catalogs, and on-premises storage partners; providers define shares in Unity Catalog, recipients query live data through existing tools, and governance enforces access controls.


### [Agent Bricks: Data + AI Summit 2026](https://yomu.fyi/post/agent-bricks-data-ai-summit-2026.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Hanlin Tang, Kasey Uhlenhuth, Akhil Gupta, Patrick Wendell
- Published: Jun 16, 2026
- Type: Announcement

At Data + AI Summit 2026, Databricks announced Agent Bricks as a comprehensive developer platform for building and operating agents, extending a product launched the previous year. The announcement frames the core agent loop as only 1% of the work, with token capacity, deployment, security, evaluation, monitoring, context, and sharing forming the remaining infrastructure burden. Agent Bricks addresses choice, context, and control through support for multiple proprietary, open-source, and custom models, any agent harness, MCP-connected data, Genie Ontology, managed memory, document intelligence, sandboxes, and governed tools. Unity AI Gateway adds catalogs, fine-grained access controls, budgets, traffic routing, contextual policies, monitoring, and registry support for agents, tools, and models. Databricks says more than 100,000 agents have been built and customers including AstraZeneca, 7-Eleven, Fox Corporation, and Block have shipped agents on the platform.


### [AI governance at Data + AI Summit 2026: What’s new with Unity AI Gateway](https://yomu.fyi/post/ai-governance-at-data-ai-summit-2026-what-s-new-with-unity-ai-gateway.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Nasi, Stefania Leone, Ahmed Bilal, Kevin Stumpf, Martin Grund, Vladimir Kolovski, Kelly Albano
- Published: Jun 16, 2026
- Type: Announcement

Databricks announces new Unity AI Gateway capabilities for governing enterprise AI as organizations operate multi-model, multi-agent, and multi-vendor estates connected to models, MCP services, APIs, and tools. The update adds unified spend visibility, granular attribution, hard spend caps, and smart routing, alongside Unity Catalog support for registering and governing models, MCP services, agents, and skills. Contextual Service Policies, in Beta, can allow, deny, or require approval for actions based on users, agents, models, tools, services, or request and response contents, with guardrails for risks such as PII exposure and prompt injection. The announcement also covers end-to-end tracing, coding-agent analysis with Genie, incident investigation with Lakewatch, ecosystem integrations, and Managed Omnigent on Databricks in Beta.


### [Lakeflow: A new era of agentic data engineering](https://yomu.fyi/post/lakeflow-a-new-era-of-agentic-data-engineering.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Bilal Aslam, Ray Zhu, Manish Dalwadi, Saad Ansari, Giselle Goicochea
- Published: Jun 16, 2026
- Type: Announcement

Databricks announces a major evolution of Lakeflow, its unified platform for data engineering across ingestion, transformation, and orchestration, with capabilities centrally governed by Unity Catalog. Genie Code and generally available Lakeflow Designer support agentic and no-code pipeline development, while Genie ZeroOps monitors production assets, analyzes failures, proposes fixes, and validates them in a governed sandbox before human approval. Lakeflow Connect expands to more than 100 managed connectors, and Zerobus Ingest adds Kafka-compatible, gRPC, REST, SDK, and OpenTelemetry interfaces for high-volume event ingestion. Real-Time Mode for Spark Declarative Pipelines reaches Public Preview with end-to-end latency as low as 5 milliseconds, alongside declarative APIs and expanded Lakeflow Jobs integrations. The release also adds data-readiness triggers and external orchestration for systems including Snowflake, REST APIs, Slack, and PagerDuty.


### [Introducing Genie ZeroOps: Put your data and AI operations on autopilot](https://yomu.fyi/post/introducing-genie-zeroops-put-your-data-and-ai-operations-on-autopilot.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Bilal Aslam, Lennart Kats, Ray Zhu, Mike Del Balso, Ori Zohar
- Published: Jun 16, 2026
- Type: Announcement

Genie ZeroOps is an autonomous background agent for monitoring and operating data and AI assets, including jobs, pipelines, tables, and ML models. It continuously detects visible and silent failures, uses Unity Catalog lineage and platform observability to assess root causes, generates remediation through development workflows, and verifies fixes in isolated sandboxes. These environments use shallow, zero-copy table clones, scoped permissions, and network isolation, so proposed changes run against real data without touching production or applying anything before approval. For ML workloads, the agent can diagnose degraded predictions, train a candidate on corrected features, evaluate it against the production model’s existing eval suite and criteria, and support live-traffic ramping when it is measurably better. Genie ZeroOps is entering private preview in the coming weeks, initially supporting jobs, pipelines, tables, and ML workloads; Apps and Lakebase databases are on the roadmap.


### [Announcing Lakebase Search: agent-native retrieval built into Lakebase Postgres](https://yomu.fyi/post/announcing-lakebase-search-agent-native-retrieval-built-into-lakebase.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Pranav Aurora, Zhou Sun, Jinjing Zhou
- Published: Jun 16, 2026
- Type: Announcement

Lakebase Search is a beta offering on AWS and Azure that adds hybrid vector and full-text retrieval to Lakebase Postgres. It uses the lakebase\_vector and lakebase\_text extensions to keep retrieval, memory, operational data, and hybrid search in one backend. lakebase\_vector retains pgvector types and operators, applies RaBitQ clustering and compression for 32x smaller indexes, and targets more than 1B vectors, while lakebase\_text replaces GIN with object-storage-optimized BM25 ranking. A tiered cache keeps hot data on NVMe and places colder data in object storage; the source reports lower memory needs, faster index builds, and cold-cache startup than standard pgvector HNSW in its LAION-100M benchmark. The extensions also combine vector similarity and keyword relevance with reciprocal rank fusion in a single SQL query, enabling joins and tenant filtering alongside transactional workflows.


### [Introducing Genie One, Genie Agents, and Genie Ontology](https://yomu.fyi/post/introducing-genie-one-genie-agents-and-genie-ontology.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Sydney Sundell, Ken Wong, Elise Georis
- Published: Jun 16, 2026
- Type: Announcement

Databricks announces Genie One, Genie Agents, and Genie Ontology to help enterprises answer business questions and act on data whose context is scattered across dashboards, queries, documents, tickets, and chats. Genie One connects data and business tools through Lakehouse federation, Lakeflow Connect, native integrations, Slack, Teams, mobile apps, schedules, alerts, document creation, custom skills, and MCP support. Genie Agents evolve Genie Spaces into domain-specific agents that can reason over structured and unstructured data, execute multi-step workflows, and be created from a prompt. Genie Ontology builds a permission-aware living graph from enterprise assets, weighting sources by authority, usage, certification, and freshness. In an internal 28-question benchmark, Genie answered 84.5% correctly on the first attempt and delivered twice the speed of the strongest coding agent.


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