---
title: "AI Agents"
description: "118 posts about AI Agents, summarised, each linking to the original."
---

# AI Agents
> 118 posts about AI Agents, summarised, each linking to the original.

## Articles

### [ClickHouse welcomes RunReveal](https://yomu.fyi/post/clickhouse-welcomes-runreveal.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 1, 2026

ClickHouse has acquired RunReveal, a company that built a security data platform on ClickHouse, and says the team’s expertise will become part of its work in security. The announcement frames security as a demanding data workload involving cloud audit logs, identity events, endpoint telemetry, and network flows, with continuous high-throughput ingest, years-long retention, and interactive query requirements. ClickHouse says the acquisition will bring sharper reference architectures, guidance on schema and retention design, and a clearer connection between customer requirements and its database roadmap, while RunReveal remains available through a bring-your-own-database model. Existing RunReveal customers will continue to receive platform support under unchanged contract terms, and the team’s work on agentic investigation is expected to inform support for analytics involving agents that hunt across sources, tune detections, and advance investigations.


### [Building an Adaptive Agentic Cybersecurity System with NVIDIA Nemotron](https://yomu.fyi/post/building-an-adaptive-agentic-cybersecurity-system-with-nvidia-nemotron.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Sep 1, 2026

NVIDIA and CrowdStrike describe an agentic cybersecurity system that links red-agent attack execution with blue-agent detection engineering in a continuous offense-defense testing loop. In an isolated environment modeled on NVIDIA accelerated computing infrastructure, Falcon sensors captured telemetry while Nemotron 3 Ultra orchestrated the defensive workflow and a customized Nemotron 3 Super generated or repaired detections. The harness grounded agents in sensor schemas and attack traces, then applied linting, replay, structured correction, and independent review before deployment. Backtesting raised mean detection of the recorded attack from 16.5% to 41.9% with the optimized open pipeline across independently seeded sessions. In live-fire tests, five of 11 open detections detected an unseen attack, three qualified as gold, and those three covered all eight attacks; the authors call the result a directional case study because it used one scenario family and limited benign traffic.


### [Becoming an AI Team](https://yomu.fyi/post/becoming-an-ai-team.md)
- Company: [Pinterest](https://yomu.fyi/company/pinterest.md)
- Author: Pinterest Engineering
- Published: Sep 1, 2026

Becoming an AI team requires more than adding assistants to existing workflows: it changes ownership, planning, roles, and execution. At Pinterest, infrastructure teams face the scale of serving billions of Pins, boards, ads, and real-time signals, making AI adoption an operational necessity for reliability, cost efficiency, and developer productivity. AI code-generation and transformation models can turn a widespread legacy-codebase refactor, such as replacing “foo” with “bar,” from a months-long effort into work completed and verified in a week or less. As routine execution is increasingly augmented or automated, engineers, product managers, and designers are expected to spend more time on strategy, problem definition, and user validation, while managers emphasize vision, trade-offs, mentorship, and collective impact. The conclusion is that teams must continuously optimize an AI-driven operating system rather than treat AI as a side project.


### [Securing Agentic Commerce](https://yomu.fyi/post/securing-agentic-commerce.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: Bradford Peirce
- Published: Aug 31, 2026

Agentic commerce is moving from AI-assisted product discovery toward purchases completed by agents, but retail systems often cannot reliably establish the shopper’s identity or spending authority. The post describes four phases—product discovery, checkout functions, delegate-to-buy, and agent-to-agent commerce—and notes standards including UCP, ACP, and AP2. Auth0’s approach combines user authentication, asynchronous authorization, and authentication for MCP, using CIMD-based agent registration, OAuth, and on-behalf-of token exchange to issue temporary, scoped transaction access. It presents third-party shopping agents and retailer-owned agents as two deployment models, with actions tied to an authenticated shopper and recorded for auditability. The proposed identity layer can sit alongside an existing identity provider while supporting emerging protocols and future autonomous purchasing.


### [Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science](https://yomu.fyi/post/run-nvidia-bionemo-nim-microservices-for-protein-structure-prediction.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Aug 31, 2026

Claude Science is used with the NVIDIA BioNeMo Agent Toolkit and NVIDIA NIM microservices to investigate how the predicted Seh1 structure changes alone versus with proposed partner C1HCX1. The workflow retrieves UniProt sequences, generates unpaired and species-paired multiple-sequence alignments with MSA Search, and sends those inputs to OpenFold3 and Boltz-2 for independent monomer and heteromer predictions. Using MSA input, heteromer iPTM reached 0.85 for OpenFold3 and 0.82 for Boltz-2, versus 0.14 and 0.19 without it; five-sample runs had standard deviations no greater than 0.006. Both models placed partner β-strands at Seh1’s WD40 propeller closure, while core Cα-RMSD remained 0.68 Å and 0.65 Å, respectively, but the predicted interaction remains experimentally unverified.


### [From Leaderboards to Model Profiles: A Deep Dive Evaluation of LLMs for Agentic Coding](https://yomu.fyi/post/from-leaderboards-to-model-profiles-a-deep-dive-evaluation-of-llms-for.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Dmitry Savelev
- Published: Aug 31, 2026

Resolve rate alone can hide how coding agents reach the same outcome. This evaluation compares Claude Opus 4.7, Gemini 3.5 Flash, GPT-5.5, and Qwen 3.6 27B FP8 through a Junie pipeline across four benchmark datasets, combining test results with trajectory, cost, efficiency, patch quality, and process measures. In one private benchmark, Opus and Gemini solved the same number of tasks, but Opus averaged 184 steps at USD 2.79 per run versus Gemini's 271 steps at USD 1.24. Across 523 tasks, Opus resolved 267 (51.1%) and Gemini 254 (48.6%); shared outcomes masked differences in navigation, validation, redundancy, hallucination, and patch scope. The wider comparison found GPT-5.5 had the highest resolve rate and always ran an executable check, Opus led patch-quality metrics, and Qwen delivered lower cost with lower resolution, while the authors caution that profiles depend on the Junie scaffold and LLM judges are diagnostic rather than ground truth.


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

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


### [AI in supply chain: from demand forecasting to AI agents](https://yomu.fyi/post/ai-in-supply-chain-from-demand-forecasting-to-ai-agents.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jul 24, 2026

AI in supply chain management applies machine learning, generative AI and AI agents to demand forecasting, inventory optimization, supplier-risk assessment and logistics orchestration. It connects ERP records, point-of-sale feeds, supplier communications and external signals to support continuous decision-making rather than periodic planning. The guide maps use cases to required data foundations, covering demand-sensing pilots, warehouse task prioritization, agent guardrails, cross-system APIs and generative-AI grounding. It recommends starting with a product category or region, comparing model output with a baseline, and expanding only when accuracy and bias improve consistently. Reported figures include up to 85% forecast-accuracy improvement, up to 15% lower inventory carrying costs, and 23% lower fulfillment costs among organizations that deployed AI at scale.


### [AI Applications in Finance: A Practical Use Case Guide](https://yomu.fyi/post/ai-applications-in-finance-a-practical-use-case-guide.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jul 24, 2026

AI in finance spans machine learning, natural language processing, and generative AI for credit scoring, fraud detection, algorithmic trading, finance automation, and decision support across banking, capital markets, and insurance. Finance teams rank use cases by revenue impact, risk reduction, and implementation effort, while data scientists clean and validate the underlying data. Credit scoring can combine traditional and alternative data, with confidence thresholds routing uncertain cases to human underwriters; trading strategies use historical backtests, monitoring, and versioned audit trails. Fraud systems monitor transactions in real time and prioritize alerts, while finance automation uses machine learning, rules-based logic, exception queues, and ERP integration. The guide recommends 90-to-120-day pilots with predefined metrics and ROI measurement before scaling, alongside explainable AI, model governance, and logged decisions for responsible deployment.


### [How the FDA Built an AI Platform That 85% of Its Staff Now Use Daily](https://yomu.fyi/post/how-the-fda-built-an-ai-platform-that-85-of-its-staff-now-use-daily.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Molly Just-Behr
- Published: Jul 23, 2026

The FDA built ELSA, a generative AI platform for its 16,000 staff, on Halo, a governed Databricks data foundation created to address fragmented systems across eight centers. Those centers had separate chatbots and data stores; consolidating 50 to 60 sources enabled faster sharing, real-time streaming, and centralized access controls through Unity Catalog. Within roughly two months, ELSA adoption rose from less than 1% to 85%, while staff began building hundreds of agents weekly from standard operating procedures, regulatory guidance, and center-specific documents. MCP servers layered over Unity Catalog make governed data and tooling accessible beyond data scientists, and Databricks ML and NLP capabilities through MLflow extracted starting materials and product-supplier-manufacturer relationships from millions of submission pages. A reviewer can now request grounded starting-material information for a drug application in about three minutes instead of days, while the FDA adapts center-specific MCP tools and extends the model across its organization.


### [Permission isn't purpose: Intent-based authorization in Omnigent](https://yomu.fyi/post/permission-isn-t-purpose-intent-based-authorization-in-omnigent.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Nishith Sinha, Matei Zaharia
- Published: Jul 23, 2026

Omnigent’s intent-based authorization addresses a gap in identity-based access control: an agent with valid credentials may follow indirect prompt injections embedded in data and perform authorized actions unrelated to the user’s task. It binds each session to a human-declared purpose and evaluates that intent before every tool call, producing permitted, consent-required, or denied verdicts. In a data-quality example, reading a customers table is allowed, dashboard publication requires approval, and an injected request to grant external access is denied despite the agent’s identity permitting that tool. The intent is fixed at design time for autonomous agents or approved by a human at session start for interactive agents, and cannot be broadened by the agent. Omnigent combines this policy with session-risk scoring and other contextual policies in a single engine where any denial wins.


### [Why A Frontier Data Agent Outperforms General Coding Agents in Quality and Cost](https://yomu.fyi/post/why-a-frontier-data-agent-outperforms-general-coding-agents-in-quality.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: The Databricks AI Research Team
- Published: Jul 23, 2026

An evaluation compared Genie Code with three widely used coding agents from major AI labs on 401 real internal data tasks, using a shared 20-minute wall-clock budget and independent grading. Genie Code achieved 76.6% accuracy at an estimated $0.55 per task, outperforming rivals at 55.9–72.1% accuracy and $0.91–$1.16 per task. Its advantage is attributed to semantic search across catalog and workspace assets, persistent memory of tables and business logic, and enterprise-context understanding, which reduce exploratory tool use. On discovery-heavy tasks, general-purpose agents often wandered through large workspaces or timed out on inefficient scans, while Genie Code averaged 8.3 tool calls per task. The benchmark found no accuracy-cost trade-off, although the authors note that Genie Ontology was disabled and expect it to strengthen the advantage.


### [Simplify AI agent orchestration with Lakebase Postgres](https://yomu.fyi/post/simplify-ai-agent-orchestration-with-lakebase-postgres.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Li Yu, Michelle JanneyCoyle, Jon Cormack, Yarri Bryn, Alec Sorensen, Darshana Nair
- Published: Jul 22, 2026

CLA and Databricks built a production document-processing application for auditing that reduces extraction time from hours to minutes without compromising quality, using Databricks-native services including Lakebase Postgres, Databricks Apps, Lakeflow Jobs, MLflow, and Unity Catalog Volumes. Lakebase serves as the orchestration layer’s single source of truth for tasks and execution attempts, coordinating long-running work, retries, leases, priorities, rate limits, costs, and status visibility. The queue uses Postgres patterns including FOR UPDATE SKIP LOCKED, priority and FIFO ordering, expiring leases for crash recovery, and database-backed concurrency controls. Databricks Jobs process PDFs through intelligent document processing and vision/LLM calls, while MLflow Tracing records execution and cost details and dashboard updates combine fast Postgres data with slower billing queries. In production, this architecture avoids external brokers and schedulers while providing durable task management, real-time visibility, and per-task cost attribution.


### [The last mile: why great first-party data still doesn't make great marketing](https://yomu.fyi/post/the-last-mile-why-great-first-party-data-still-doesn-t-make-great-mark.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Michael Burton, Katy Yuan
- Published: Jul 21, 2026

Modern data platforms and sophisticated marketing systems can still fail to produce timely customer experiences when no bridge connects first-party data to campaign activation. The post explains Scott Brinker’s “composable canvas,” a five-ring architecture centered on a unified data core, with semantic layer, CaaS, decisioning, and apps and agents operating on shared data without repeated movement. In contrast, batch files, dashboards, and disconnected teams can leave autonomous agents unable to trigger campaigns or propensity models unused. It recommends closing this last mile through activation-ready data architecture, self-service marketing analytics, and one narrowly scoped AI agent tied to a measurable campaign outcome. Examples in the post include a 4X model conversion rate, a four-week account scoring launch, and grocery offer flows that changed weekly preparation from hours to minutes.


### [Announcing the Public Preview of Discover and Domains, powered by Unity Catalog](https://yomu.fyi/post/announcing-the-public-preview-of-discover-and-domains-powered-by-unity.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Stef Bran Melendez, Kelly Albano
- Published: Jul 21, 2026

Databricks announces the Public Preview of Domains and the Discover page, powered by Unity Catalog, to help people and agents identify relevant, high-quality, and safe data and AI assets. Domains organize tables, dashboards, notebooks, queries, metric views, Genie Agents, and apps by business structure, while Discover provides an internal marketplace with search, certification signals, popularity and trending indicators, and AI-powered recommendations. Data stewards can create domains and subdomains, certify assets, add descriptions and contacts, and curate page sections and pinned content. Domains extend Unity Catalog Semantics and feed Genie Ontology, giving agents business context for narrowing retrieval, prioritizing trusted assets, and interpreting metrics within each user’s existing permissions. The features are available in Public Preview through a Databricks workspace, where organizations can create domains and curate assets.


### [The three ways AI unlocks transformation in Retail, Travel, and Consumer Goods](https://yomu.fyi/post/the-three-ways-ai-unlocks-transformation-in-retail-travel-and-consumer.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Rob Saker
- Published: Jul 20, 2026

The piece argues that retail, travel, and consumer-goods companies face one problem in three forms: signals are not trusted, arrive too late, or cost too much to process at scale. It contrasts business intelligence, which depends on structured schemas, predefined questions, and dashboards, with AI systems that read unstructured data, reason probabilistically across signals, and connect decisions to action. Examples include reported improvements from Harmons’ shelf scanning, faster consumer-insight work at a health and hygiene company, and travel applications spanning maintenance, pricing, and concierge services. Its proposed architecture combines broad ingestion and open storage with governance, evaluation, model and agent controls, and applications that operate on a substrate, presenting current, coherent data as the foundation for organizations that can act.


### [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

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

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

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.


### [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

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.


[Older posts](https://yomu.fyi/topic/ai-agents/page/2.md)