---
title: "AI Agents"
description: "574 posts about AI Agents, summarised, each linking to the original."
---

# AI Agents
> 574 posts about AI Agents, summarised, each linking to the original.

## Articles

### [AI Agent Orchestration: A Guide for Enterprise Systems](https://yomu.fyi/post/ai-agent-orchestration-a-guide-for-enterprise-systems.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jul 20, 2026
- Type: Explainer

AI agent orchestration coordinates multiple specialized agents by managing task assignment, shared state, communication, and execution sequencing across complex enterprise workflows. The guide contrasts autonomous agents, which reason about subtasks and adapt to intermediate results, with fixed workflows, and recommends decomposing processes into single-responsibility agents with documented input/output contracts and least-privilege access. It describes centralized, decentralized, hierarchical, hybrid, federated, and emergent patterns, noting that pattern selection trades control, resilience, scalability, and auditability according to risk. Implementation guidance includes assigning human owners, defining accuracy, latency, and escalation targets, establishing failure paths, instrumenting a minimal prototype, and measuring a pilot against documented baseline processes. Organizations using multi-agent systems report 35% faster task completion and a 30% efficiency increase with specialized agents, while high-risk or irreversible actions still require human approval gates and audit trails.


### [Tech builds on AI. Finance protects the margin.](https://yomu.fyi/post/tech-builds-on-ai-finance-protects-the-margin.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Madelyn Mullen
- Published: Jul 17, 2026
- Type: Explainer

AI-native tech companies must protect unit economics as agents accelerate changes in compute consumption, pricing, and revenue recognition, while gross margins remain below classic software levels. Finance teams built around extracts, spreadsheets, and monthly reconciliation can miss repricing changes, metering errors, and compute-commitment risk. The proposed foundation is an evolving ontology that keeps product, plan, usage, and cost meanings current, with Stripe data entering Unity Catalog through OpenSharing and Lakebase providing transactional Postgres on the lakehouse. Genie One uses that ontology to answer governed, sourced questions about gross margin, consumption revenue at risk, and compute spend, while people retain decision authority. The post describes organizations using Databricks to consolidate reporting, forecasting, workflows, and finance applications, positioning a shared data-and-AI platform as the path from an initial answer to an ongoing finance platform.


### [Building a soccer coaching app on Databricks](https://yomu.fyi/post/building-a-soccer-coaching-app-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Samwel Emmanuel, Sheridan Harris, Andrew Helmreich, Kush Patel, Nick Ragonese
- Published: Jul 17, 2026
- Type: Problem & solution

Coach’s Corner, also called La Pizarra, turns high-frequency soccer tracking data into a bench-side application for replay, tactical analysis, scouting, standings, and agent-generated dossiers. Built as a Databricks App, it ingests NDJSON feeds at 25 frames per second through Auto Loader and Spark Declarative Pipelines, enforcing 46 data-quality expectations across bronze, silver, and gold layers. Liquid clustering supports 1–3-second DBSQL queries, while Lakebase synchronizes gold data to Postgres for millisecond replay reads and separates sequential playback from exploratory analytics. The scouting layer grounds Genie, Vector Search, a Unity Catalog-registered xG model, and an Agent Bricks supervisor in governed data, with Claude calls routed through the Unity AI Gateway, MLflow tracing, and a deterministic fallback. Together, these components are presented as a way to deliver traceable insights within seconds without forcing coaches to interpret raw tables or analysts to relay every result.


### [How We Made All Swiggy Apps Operations AI-Native](https://yomu.fyi/post/how-we-made-all-swiggy-apps-operations-ai-native.md)
- Company: [Swiggy](https://yomu.fyi/company/swiggy.md)
- Author: Raj Gohil
- Published: Jul 17, 2026
- Type: Explainer

Swiggy’s mobile engineering teams faced operational overload as releases, production signals, and reviews required switching among Jira, Confluence, Bitrise, Play Console, Crashlytics, New Relic, Google Sheets, ImageKit, and Slack. The response was an operating layer built around Slack-native agent workflows, reusable AI skills, MCPs, and scheduled jobs connected to release, observability, QA, cost, and internal mobile systems. It can generate readiness updates, group Jira work, identify missing owners and blockers, summarize vitals, analyze alerts, follow up TRM actions, and recommend RAMP, HOLD, or ROLLBACK for staged rollouts by correlating Crashlytics, Play Vitals, and adoption. Humans still approve rollout actions, while recurring channel reports make observability and accountability more consistent and reduce clerical integration work. The stated model lets agents handle mechanical load while engineers retain judgement, ownership, product quality, architecture, and execution.


### [The skills gap behind agentic AI — and how Databricks is closing it with a new context engineer certification and agent trainings](https://yomu.fyi/post/the-skills-gap-behind-agentic-ai-and-how-databricks-is-closing-it-with.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Rachel Canetta, Trang Le
- Published: Jul 16, 2026
- Type: Announcement

Databricks introduced the Databricks Certified Context Engineer Associate beta exam to validate skills for building reliable, production-grade AI agent systems as organizations scale agentic AI. Context engineering is presented as the practice of curating, maintaining, and filtering tokens, memory banks, and tool parameters so an LLM can solve a task; the certification is intended to benchmark that technical skill set. Databricks also added AI Agent Fundamentals, Building Retrieval Agents on Databricks, and Agent Evaluation, covering agent reasoning, retrieval-augmented architectures, and systematic performance testing and improvement. Its AI-first certification-prep guide is available on every certification page, works with free tiers of major LLMs, includes guardrails and disclaimers about hallucinations, and shows candidates how to use Free Edition for hands-on practice; registration for the Context Engineer Associate is open, with the first exam scheduled for July 29, 2026.


### [Unified context: The missing layer for enterprise AI coworkers](https://yomu.fyi/post/unified-context-the-missing-layer-for-enterprise-ai-coworkers.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Cynthya Peranandam, Christy Maver
- Published: Jul 16, 2026
- Type: Explainer

Enterprise AI assistants often produce fluent answers yet fail to improve forecast calls, deal reviews, and operational standups because decision context is scattered across systems, teams, and competing definitions. Genie One addresses this by using a shared context layer spanning Databricks data, documents, SaaS applications, and operational systems, allowing questions and follow-up work to retain business meaning. Genie Ontology organizes terms, metrics, entities, and relationships into a living knowledge graph, learning from data, dashboards, queries, documents, and connected applications while ranking definitions and signals using usage and certified-asset links. Together with Unity Catalog, it applies permissions, certified data, shared definitions, and governance controls to answers, actions, and agents. The stated outcome is faster movement from decision preparation to action, with less manual reconciliation while preserving accuracy and control.


### [Building scalable AI agents with modular prompt transpilation](https://yomu.fyi/post/building-scalable-ai-agents-with-modular-prompt-transpilation.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Simerus Mahesh
- Published: Jul 16, 2026
- Type: Problem & solution

Monolithic system prompts become difficult to reason about, test, and change as teams add safety policies, domain rules, formatting requirements, and escalation behaviors. The proposed approach treats prompts as software artifacts: modular skill files are composed with includes, variables, and macros, then transpiled into deterministic artifacts. Build-time validation checks missing imports, undefined variables, and circular dependencies, while dependency graphs and CI/CD golden-file comparisons catch drift before deployment. At runtime, progressive disclosure keeps a stable control plane loaded while agents retrieve task-specific skills, reducing context exhaustion and noise. Agents can propose new skill modules or import changes through pull requests, but human review and evaluation remain part of the merge process.


### [Expanding Choice in Gemini Enterprise Agent Platform: Introducing Grounding with Parallel Web Search](https://yomu.fyi/post/expanding-choice-in-gemini-enterprise-agent-platform-introducing-groun.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Guangsha Shi
- Published: Jul 16, 2026
- Type: Announcement

Gemini Enterprise Agent Platform now natively integrates Parallel Web Systems as a web-grounding provider, expanding developers’ choice of sources for real-time, verifiable information. Grounding with Parallel Web Search connects Gemini models to Parallel’s structured, LLM-optimized search results, combines the provider’s web index with Gemini’s prompt decomposition and synthesis, and adds precise citation annotations. The capability is available through the Gemini API and Agent Studio, and can be subscribed to through Google Cloud Marketplace with usage charged on an existing Google Cloud invoice; zero data retention is available for sensitive workloads. Developers can make programmatic calls at scale, extract and cache web data, post-process results with other LLMs, and build catalog enrichment, autonomous-agent, and multi-agent workflows. Setup requires subscribing, accepting the terms, reviewing pricing, and selecting Parallel Web Search under Agent Studio’s partner-grounding settings.


### [Agents for financial services](https://yomu.fyi/post/agents-for-financial-services.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 15, 2026
- Type: Announcement

Anthropic is releasing ten ready-to-run agent templates for financial services, targeting work such as pitchbook creation, KYC screening, and month-end close operations. Each template packages task-specific skills, governed data connectors, and subagents, and is available as a plugin for Claude Cowork or Claude Code, or as a cookbook for Claude Managed Agents. Claude add-ins for Excel, PowerPoint, Word, and, soon, Outlook carry context between applications, while new connectors and a Moody’s MCP app provide access to market, research, credit, and company data. The announcement says the updates pair best with Claude Opus 4.7, which leads Vals AI’s Finance Agent benchmark at 64.37%, and states that templates are available today through the financial services marketplace.


### [Inkling model from Thinking Machines Lab now on Databricks](https://yomu.fyi/post/inkling-model-from-thinking-machines-lab-now-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Mike Eastham, Yuchen Jin, Preslav Le
- Published: Jul 15, 2026
- Type: Announcement

Databricks announces that Inkling, Thinking Machines Lab’s first open-weights model, is available to enterprise customers through the Unity AI Gateway. The model is positioned for coding and agentic reasoning workflows, supports multi-modal inputs, and can be applied to enterprise data, including proprietary codebases, internal documentation, and domain-specific data. Unity AI Gateway provides centralized security, permissions, audit logging, policy enforcement, cost controls, budgets, and observability, while data remains within the governed environment; Inkling is invoked through a REST API, with SQL query support planned. Teams can try it in AI Playground, deploy a governed endpoint, connect coding agents such as Cursor, OpenCode, or Pi, and build agents with Agent Bricks, with open weights enabling customization and inference-cost optimization without per-token API pricing.


### [AI-Enabled Advisory Services for Higher Education](https://yomu.fyi/post/ai-enabled-advisory-services-for-higher-education.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Chad Ammirati, Zach Langford, Nicole Wong
- Published: Jul 15, 2026
- Type: Problem & solution

Higher-education call centers face costly, limited-coverage monitoring of advisor conversations and brittle, slow methods for identifying student concerns from transcripts. The proposed workflow deploys OpenAI Whisper on Databricks Model Serving, applies AI Functions for sentiment, topics, intent, and rubric scoring, and uses Unity Catalog to govern the resulting data. For advisor quality, an LLM-as-a-judge evaluates every transcript against a reference-table rubric, returns a weighted 1–5 overall score and per-criterion scores, and routes flagged calls for targeted QA review instead of random sampling. For student insights, quarterly transcript enrichment feeds an Agent Bricks Knowledge Assistant for cited reasoning over raw calls and a Genie Space for structured trend queries, while LangGraph orchestrates UC SQL functions as tools. Together, these components let non-technical advisors, mentors, and QA managers query student interactions without reaching out to a data SME.


### [Data-Native AI Agents: Why Agents Must Move to Your Data](https://yomu.fyi/post/data-native-ai-agents-why-agents-must-move-to-your-data.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Kaan Kuguoglu, John Karlsson
- Published: Jul 15, 2026
- Type: Explainer

Enterprise AI pilots often move data into separate vector databases, SaaS LLMs, or serving layers, creating governance gaps, compounded latency, fragmented costs and observability, and duplicated lifecycle work. The post advocates data-native agents: models, agents, tools, retrieval, and memory run inside the governed data platform, with policy enforced during query planning and computation rather than after responses are produced. It argues that post-hoc controls cannot undo sensitive information encoded in aggregations and can trigger token-burning retry loops. For state and memory, it presents Lakebase, managed PostgreSQL within Databricks, as transactional storage and a shared source of truth for multi-agent swarms. The described platform pattern combines Unity Catalog, Unity AI Gateway, Model Serving, MLflow 3, AI Search, Lakebase, and business-context services, and recommends inventorying workloads already outside the perimeter before closing seams incrementally.


### [Take insights anywhere with Genie One on mobile](https://yomu.fyi/post/take-insights-anywhere-with-genie-one-on-mobile.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Mohit Hingorani, Christine Li, Rhetta Nadas, Sydney Sundell
- Published: Jul 14, 2026
- Type: Announcement

Genie One mobile apps for iOS and Android let business users ask questions of company data, view dashboards, access Databricks Apps, and use conversational agent capabilities away from a desk. Answers draw on Genie Ontology, Genie Agents, enterprise governance, and connectors to Google Drive, Microsoft 365, and Atlassian, while respecting source permissions. AI/BI Dashboards reflow widgets into a single column in portrait mode and preserve their designed layout in landscape. Authentication uses the existing OAuth flow and identity provider, with MFA, conditional access, device posture, network controls, regional data handling, and workspace permissions carried over from the browser; there is no separate mobile backend or mobile-only endpoint. The app is available in Public Preview, with dark mode, push notifications, voice mode, and account-level access listed as planned capabilities.


### [How Retail Finance teams are using Agentic AI to protect omni-channel margins](https://yomu.fyi/post/how-retail-finance-teams-are-using-agentic-ai-to-protect-omni-channel.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Sarah Duffy
- Published: Jul 14, 2026
- Type: Explainer

Omni-channel retail has spread margin, cash, and markdown decisions across more channels, fulfillment paths, and return routes, while agentic systems increase the speed and complexity of change. The post presents ontology as a way to preserve the meaning and business context behind finance figures, keeping definitions, channels, and cost drivers current. Databricks Genie is described as a data-smart AI coworker that answers finance questions in plain language, grounding responses in an evolving ontology, source traces, permissions, and governed AI costs. It focuses on margin after fulfillment and returns, inventory cash tied up in the wrong place, and full-price revenue at risk from markdowns and returns, then prepares actions for a person to approve. Unilever deployed Genie to more than 1,200 finance and business users; analysis that took days now takes minutes, with expected multi-million-euro annual cost avoidance.


### [Foundational context: Cross-industry & function-specific accelerators for Lakebase](https://yomu.fyi/post/foundational-context-cross-industry-function-specific-accelerators-for.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Amit Singh
- Published: Jul 14, 2026
- Type: Announcement

Databricks presents Lakebase as a fully managed, serverless, standard Postgres database for combining operational and analytical workloads on its Data + AI Platform. The platform separates compute from storage, integrates with the lakehouse through Synced Tables and Lakebase CDF, and uses Unity Catalog for governance; copy-on-write branching and autoscaling to zero are described as core infrastructure primitives. The post showcases partner-built, ready-to-deploy accelerators spanning technology, finance, marketing, sales, supply chain, human resources, customer service, and operations. Examples include PostgreSQL migration assessment, multi-agent Genie orchestration, stateful enterprise agents, autonomous data reliability, governed contact-center intelligence, and project operations management. These offerings package Lakebase patterns into migration controls, domain-specific solutions, and agent frameworks intended to accelerate modernization and reduce transformation complexity.


### [Blocking Slow-Burn Attacks: Contextual Policies in Omnigent](https://yomu.fyi/post/blocking-slow-burn-attacks-contextual-policies-in-omnigent.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Nishith Sinha, Matei Zaharia
- Published: Jul 14, 2026
- Type: Problem & solution

Omnigent’s post examines how a vendor-review assistant can leak confidential pricing terms when an attacker hides an indirect prompt injection in a shared runbook. Because the malicious workflow is divided into ordinary actions, stateless checks approve each step even though the session as a whole is unsafe. The demonstration compares an unprotected run, which sends the summary externally, with a contextual policy that stores a running risk score, adds 30 for each document read, and denies email after the score exceeds 50. It also shows that agents cannot remove or disable policies, new policies require human approval, and any denial prevails when policies are combined. Runtime enforcement therefore preserves the block even when the agent has been misled.


### [The agentic marketing stack starts with the data layer](https://yomu.fyi/post/the-agentic-marketing-stack-starts-with-the-data-layer.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aly McGue
- Published: Jul 10, 2026
- Type: Explainer

The piece argues that agentic marketing depends on modernizing the data and infrastructure foundation first, rather than starting with isolated AI use cases. At Acxiom, moving core products and client solutions from on-premises Hadoop to Databricks reportedly improved workload runtimes by 80 to 90 percent, reducing jobs that took 50+ hours or as long as 90+ hours to 2–3 hours. The migration also reduced manual pipeline and infrastructure work, freeing engineering capacity for products and client outcomes. Those capabilities now support agentic audience planning, media buying, campaign activation, performance analytics, code generation, testing, and ad variation, with a goal of connecting the full marketing value chain. Because workflows handle PII, generated content passes through legal approval, defined controls, and human oversight; Acxiom is also embedding its data in client platforms and privacy-safe clean rooms to make decisions more transparent and native to customer environments.


### [UST is bringing Claude to physical AI](https://yomu.fyi/post/ust-is-bringing-claude-to-physical-ai.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 10, 2026
- Type: Announcement

UST is partnering with Anthropic to bring Claude into engineering and production systems used by semiconductor, automotive, manufacturing, telecom, embedded, and IoT companies. Claude Code reads chip pinouts and hardware schematics, then writes and runs regression tests while carrying design context across hours-long tasks. UST is integrating Claude as the reasoning layer in its iDEC validation pipeline, which generates tests, compares live equipment data with a digital twin, and already cuts validation cycles from four days to 48 hours, 50 to 70 percent. The partnership also applies Claude to healthcare, telecom, and banking platforms, with human approval retained for recommended actions and response workflows. UST will train 20,000 associates worldwide and build specialized deployment teams, with governance and audit controls supporting production use in high-stakes industries.


### [Side Chats and Conversation Search](https://yomu.fyi/post/side-chats-and-conversation-search.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Published: Jul 10, 2026
- Type: Announcement

An update adds side chats for exploring tangents alongside a main agent conversation without interrupting it. Each side chat is a durable, full agent conversation with main-chat context, can be revisited, followed up on, and at-mentioned back into the main thread. In the Agents Window, users can search agent transcripts through Cmd+K using a local index designed to scale to thousands of conversations, or search within a conversation with Cmd+F. Redesigned project and repo pickers support creating projects and connecting GitHub, GitLab, or Azure DevOps without leaving the picker, with search scoped to the current location. New cloud agent hooks expose prompts, responses, thinking, subagents, compaction, and turn completion for observation and control.


### [Ask, build, compose: What our 5th Genie Hackathon taught us about Databricks Genie](https://yomu.fyi/post/ask-build-compose-what-our-5th-genie-hackathon-taught-us-about-databri.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Shruti Prasanna, Rob Bajra
- Published: Jul 9, 2026
- Type: Explainer

The fifth Databricks hackathon used Databricks Genie to test three ways of working with governed data: asking, building, and composing. Genie Agents give business users domain-specific natural-language access to curated data, while Genie Code helps analysts create metric views, functions, pipelines, and dashboards inside Databricks. The composition track uses Conversation APIs and a managed MCP server to make Genie an agent tool alongside custom logic, Model Serving, and other MCP servers, with Unity Catalog governing access. Projects included supervisory routing across 190 tables, governance and analytics products built in days, and multi-agent systems such as ShipBob’s overnight operations brief. Across the tracks, the stated lesson is that shared governance and semantic context let business users, builders, and engineers use Genie at different levels without abandoning grounded, permissioned data access.


[Newer posts](https://yomu.fyi/topic/ai-agents/page/15.md) · [Older posts](https://yomu.fyi/topic/ai-agents/page/17.md)