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82 posts about Automation. Every summary links to the original.
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Apache Camel is shown as the integration path that brings application actions into one request workflow. The diagram clarifies real-time versus scheduled ingestion, mapping, merging, authorization, and audit.
Adoption moves through experimentation, measurement, and reinforcement loops. Learn how leading companies build momentum from pilot programs into enterprise-wide habits and measurable business impact.
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Salesforce Marketing Cloud’s AI Delivery Lead Sohini Arya and team built Agent Designer, a system that automates the creation and testing of multi-agent AI teams. The orchestrator cannot write agent files itself; instead, seven specialist agents analyze architecture, failure modes, and compliance before the orchestrator synthesizes findings for human review. The system transforms an engineer’s request into a tested multi-agent team in 15-30 minutes, down from 2-4 hours manually, and helped approximately 200 engineers adopt the manager-of-agents model. Governance challenges included enforcing write-scope boundaries via prompts, reconciling independently produced design conflicts, and applying topology-specific budget formulas that account for flat, swarm, or team structures. Verifier agents return structured PASS/WARN/FAIL blocks, and self-healing is limited to two fix attempts and a $3 ceiling to prevent runaway repairs, with the orchestrator’s 500-turn budget divided across analysis, approval, generation, testing, and retries.
Human-in-the-loop (HITL) frameworks integrate human judgment into automated AI workflows to improve reliability and safety, but without careful design they can create bottlenecks. The post reveals key trade-offs: more automation increases speed and reduces cost but raises risk, while more human oversight boosts accuracy and safety but adds latency and cost. Common failure modes include over-reviewing trivial cases, lacking clear trigger logic, and expecting human reviewers to scale linearly. The proposed solution is a tiered HITL system applying the Pareto principle: automation handles 85% of high-confidence decisions using confidence calibration, with three tiers addressing the remaining cases under 3 ms latency, asynchronous review within hours, and real-time oversight with 30-60 second windows. Success requires careful routing, reviewer tooling, and closing feedback loops across seconds, days, weeks, and quarterly cycles to continuously improve model performance and reduce human involvement over time.
JetBrains introduced Air Teams, a shared workspace for agentic development that extends beyond individual developers to teams and organizations. The system comprises four components: Automations for recurring tasks triggered by events or schedules, shared cloud environments with preconfigured tools and credentials, cloud tasks that run in parallel without tying up local machines, and team projects that centralize members, environments, and credits. Automations include templates for code review, bug fixes, and dependency updates, and they run under a project’s service account so they persist after a creator leaves. Shared environments are defined in a repository’s .air/cloud/startup.sh script and can be snapshotted for reuse, with secrets shared without exposing values. Air Teams is available to business customers, with plans to expand to individual customers later.
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.
Hardcoding Twilio Auth Tokens in source code or exposing them in plaintext leaves communication systems vulnerable to account takeover, toll fraud, and unauthorized exploitation. This tutorial presents an automated detection and rotation pipeline using TruffleHog for secret scanning, Tines for security workflow automation, and Slack for user interaction. TruffleHog scans local directories or remote Git repositories for Twilio secrets, performs live verification via API requests, and outputs results in JSON. Tines ingests detection events through a webhook, parses the results, and triggers a Slack notification with interactive buttons for acknowledgment, scheduling remediation, or immediate rotation. Upon rotation, Tines calls the Twilio Account API to generate a new secondary auth token, displays it in a Slack modal for secure copy, and eventually promotes it to primary, while the old token is revoked.
Agentic analytics is an approach in which autonomous AI agents continuously monitor data, detect patterns and anomalies, generate contextual insights, and take actions with minimal human intervention. Unlike traditional business intelligence, which relies on people to query dashboards and interpret reports, these systems operate as goal-directed decision loops that can recommend or trigger alerts, forecasts, workflows, and system changes within defined boundaries. Their architecture combines data access, agent orchestration, LLM-based reasoning, a semantic layer, action integrations, and feedback signals. The source identifies faster insight, lower reporting overhead, reduced analytics backlogs, and potentially better forecasting as benefits, while stressing governance, auditability, security, monitoring, and human oversight to control errors and unintended outcomes.
AIOps applies AI and machine learning to IT operations to analyze logs, metrics, traces, events, alerts, and related system data. It detects anomalies, correlates events, uses topology and dependencies to identify likely root causes, and supports guided or automated responses. The guide positions AIOps between observability and action, reducing noise and adding context without replacing observability, DevOps, or human judgment. Its workflow includes data ingestion, normalization, anomaly detection, event correlation, root-cause analysis, automation and orchestration, and collaboration workflows, with human-in-the-loop review recommended for higher-risk production changes. It contrasts domain-centric and domain-agnostic approaches and recommends evaluating coverage, depth, integrations, setup effort, and organizational fit before expanding automation.
Cloudflare CASB now supports automatic remediation policies that respond to newly detected SaaS security findings without requiring an administrator to act on each instance. Teams can match policies by vendor, integration or tenant, and finding type, then revoke file-sharing access, send finding details to webhooks, or do both; first-party remediation currently covers Microsoft and Google Workspace file and folder findings. The backend enqueues findings to Cloudflare Queues, uses a Worker to match policy configurations, and runs matched jobs through Cloudflare Workflows, which provides durable execution, retries, and rate-limit backoff. Policies generate administrator-activity and runtime execution logs, while the stated target from detection to completed remediation is five minutes or less; support for Custom Findings is planned for the coming weeks.
Maryland has announced a partnership with Anthropic to deploy Claude across state agencies, aiming to improve government operations and serve more than six million residents. The partnership includes a Claude-powered virtual assistant for SNAP, Medicaid, temporary cash assistance, and WIC; it will also identify other programs residents may qualify for. Caseworkers will use Claude to process and verify more than 150,000 documents manually handled each month, validate eligibility, and receive policy guidance for complex cases. Maryland will explore an AI upskilling pilot for early-career professionals in “lighthouse” industries, while a Governor’s Innovation Team tool will use Claude to identify unmet local needs such as fresh food and childcare. The effort builds on a bilingual chatbot launched in June for more than 600,000 SUN Bucks recipients, which simplified information access and reduced call center volume.
Cursor is launching Projects, a beta feature for taking on larger bodies of work such as features, migrations, and full applications. Each Project preserves context over months, runs on its own cloud computer through Cloud Agents, and coordinates parallel subagents rather than having the coordinator write code itself. Shared files synchronize across agents’ cloud and local machines, storing research, artifacts, codebase knowledge, and preferred working practices so later agents can use them. Projects can also subscribe to Slack channels, schedules, or pull requests and delegate work when signals arrive, while local agents handle testing that requires the user’s machine. Projects are accessible from Cursor’s left-hand navigation and are rolling out to all users in beta starting today.
Anthropic and PwC are expanding their strategic alliance to use Claude for technology delivery, deal execution, and AI-led redesign of enterprise functions. PwC will roll out Claude Code and Cowork from U.S. teams toward a global workforce of hundreds of thousands, establish a joint Center of Excellence, and train and certify 30,000 professionals. The collaboration targets agentic technology builds, AI-native deal-making, and production operating models for finance, supply chain, HR, and engineering, alongside a new Claude-based Office of the CFO for regulated industries. Reported deployments include insurance underwriting reduced from ten weeks to ten days, an HR application delivered in under two months, and cybersecurity response accelerated from hours to minutes, with improvements of up to 70%.
Automation roadmaps that prioritize ticket volume and low complexity can improve containment while lowering customer satisfaction when customers did not want a task automated. The post proposes four screening questions: how reversible a mistake is, whether the task touches money, health, or identity, whether the customer needs a transaction or to be heard, and whether the AI can access the necessary context. It cites a Twilio survey of 7,652 consumers, including 80% comfortable with appointment or reservation scheduling and 60% unwilling to have AI handle a medical inquiry. Tasks that fail the test can be split between AI and people: AI gathers information, verifies identity, retrieves history, and drafts a recommendation before a human decides, with Twilio Conversation Orchestrator handing conversation history and profile to Twilio Flex. The recommendation is to start with tasks where errors are relatively cheap and the data is available.
Anthropic and HHMI Janelia Research Campus are opening a research preview of the Model Hardware Standard (MHS), a shared specification for AI agents to operate programmable laboratory and manufacturing devices safely. MHS replaces bespoke integrations with standardized drivers built around read and write primitives, device discovery, natural-language tags, generated reference files, and control through MCP, a command-line interface, or code files. The model-agnostic standard lets agents coordinate instruments, monitor operating data, adjust parameters in real time, and turn exploratory work into deterministic scripts; early projects covered protein assays, qPCR, microscopy, robotics, dose-response experiments, and quantum-laser stabilization. Reported results include serial dilution experiments running about three times faster and a QuEra laser-lock controller recovering 99.3% of the time, while the preview will inform safety evaluations and guidance before an open-source release.
Anthropic is launching Claude for Small Business, a package of connectors and ready-to-run workflows intended to put Claude inside small businesses’ existing tools. Through Claude Cowork, owners can connect Intuit QuickBooks, PayPal, HubSpot, Canva, Docusign, Google Workspace, and Microsoft 365, choose a job, and approve actions before anything sends, posts, or pays. The package includes 15 agentic workflows and 15 skills covering finance, operations, sales, marketing, HR, and customer service, including payroll planning, month-end close, invoice chasing, campaign preparation, and contract review. Trust controls retain existing permissions, keep users in the approval loop, and state that Team and Enterprise Plans do not train on customer data by default. The launch also includes a free AI Fluency for Small Business course, a multi-city Claude SMB Tour beginning May 14 in Chicago, and partnerships supporting solopreneurs and community development financial institutions.
Cursor’s release expands cloud agents and the Cursor harness for longer-running, event-driven software work. Cloud agents can subscribe to pull requests, Slack threads, or scheduled tasks, wake on events, keep goals until completion, and automatically address CI failures and bot comments on pull requests they create. Custom modes pin a selected skill in chat, while /goal supplies a long-lived objective that can be paired with a custom mode or /loop. Subagents can run on separate virtual machines with isolated project copies and clean context, supporting fresh testing or parallel fixes without collisions. Steering improvements let users send follow-ups that wait for the next tool call instead of interrupting an action.
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.
This Notion HQ page collects product announcements, company updates, and stories about extending Notion’s workspace. It introduces a Developer Platform, Custom Agents that handle entire workflows autonomously, Notion 3.0 with Agents, Notion Calendar, and Notion AI for Work, described as an all-in-one AI toolkit built into the workspace. The updates also cover connectivity and data consolidation, including the acquisition of Automate.io, which built integrations for more than 200 software tools, and the welcome of Sequin, whose tools brought data from across software stacks together. Additional announcements report ZeroEntropy joining Notion, a new board of directors, a security update dated July ’26, and a private tender offer involving GIC, Sequoia Capital, and Index Ventures. The page also includes Custom Agents beta learnings, conference highlights, and a profile of a Notion consultancy.
The team formerly known as Developer Advocacy, within Developer Success, has been renamed Builder Advocacy within Builder Experience (BX). The change responds to an expanded definition of who builds with technology: alongside traditional developers, AI builders and no-code or low-code automators are creating production-level experiences and mission-critical infrastructure. The organization says its advocacy will serve developers, prompt engineers, LLM integrators, AI-agent creators, ML practitioners, and automation-focused builders, while continuing support for traditional coders, engineers, and architects. The announcement cites GitHub’s 180 million developers, 36 million new developers in 2025, and Stack Overflow’s finding that 84% use or plan to use AI tools. The rename is effective immediately and begins broader investments in resources, content, and community programs for this wider builder community.
Boost+ is an updated promotion for delivery drivers that adds pay to eligible trips during periods and locations of high demand. The promotion applies automatically when a trip’s pickup is in a designated blue zone during listed hours, and the extra amount appears in the fare shown on the trip request. Its calculation uses estimated active time—from accepting the trip through delivery—rather than time spent online waiting; for example, a 30-minute trip at $10 per active hour receives $5 in Boost+ pay. Drivers can find upcoming opportunities in the Discover tab and on the map, view total Boost+ earnings in the trip receipt, and combine the promotion with Quests and other promotions. Availability varies by city, and time beyond the estimated active time does not qualify for Boost+ pay.
Resend reports reaching 3,000,000 users six months after reaching 1 million, while its team grew from 25 to 43 members and weekly npm downloads increased from 1.4M to 6.8M. The company says it has been profitable since last year without reducing cloud or Datadog spending or slowing hiring, treating profitability as flexibility for product decisions rather than the goal. Its support model gives AI agents the product’s tools for routine questions while keeping engineer support available when answers are not found. For developers and agents, Resend has made every page render as Markdown, expanded MCP coverage from about 8% to about 90%, shipped a 53-command CLI, and planned remote MCP and OAuth, while positioning Automations alongside sending, receiving, and broadcasting in one email platform.
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