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81 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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Intercom has practiced continuous deployment since its early days, deploying code automatically after each merge to master. The pipeline uses GitHub, Codeship for the test suite, and an internal tool called Muster to push code to production EC2 instances, typically in under ten minutes. The company now deploys over 80 times a day, aiming for 100 by end of 2015, and a dashboard tracks this history over three years. This rapid feedback loop allows engineers to iterate features through internal, beta, and public stages, learning how people use the feature and how code performs in production. The automated system helps new engineers ship code on their first day and a feature in their first week by removing deployment barriers, and they demo their work at Friday sessions. Additionally, a fast and reliable deployment process discourages engineers from bypassing it during critical bug fixes, reducing risky behavior.
Stripe Workflows now supports recurring schedule triggers, enabling automated tasks like notifying teams about past-due subscriptions without external cron servers. Users set schedules in the Dashboard, and Stripe runs the workflow at each scheduled time, following the local clock and handling daylight saving time. A walkthrough lists past-due subscriptions, uses a metadata key condition to skip already-notified ones, and sends Slack notifications via the installed Slack app. The workflow handles pagination; if an action fails, the loop stops and remaining items are picked up on the next run, with details showing which step failed. This eliminates the need for a separate server, secret key storage, and daylight saving time logic, putting calendar-based operations under the finance team’s control.
Intercom launched a suite of new features including Custom Reports, Conversation Topics, and Macros to help teams balance personal and efficient customer communication at scale. Custom Reports lets teams tailor dashboards in just a few simple steps by building custom charts from user and conversation data attributes and exporting them. Conversation Topics provides a machine-learning powered engine that visually represents what customers are talking about and how often, giving teams an at-a-glance view of top concerns. Macros automate common inbox actions like tagging, assigning, snoozing, and closing conversations with a single click, boosting efficiency by up to 43%. Additional updates include Series targeting and split testing, enhanced Articles, mobile app tagging, permissions, and custom return paths for email deliverability.
Intercom announced powerful updates to Series, Banners, Resolution Bot, and other tools to help businesses scale personalized customer interactions. Series now enables split testing of up to five message variants, team annotations, company-level goals from company data, and tag removal to streamline workflows. Banners can be non-dismissible to ensure customers take action and trigger Product Tours from the banner for guided walkthroughs. Resolution Bot introduces looping mode to answer multiple questions, auto-closes conversations after three minutes of inactivity, includes a bot intro, and offers bot-only self-service with a configurable link. Additional updates include granular sidebar permissions to restrict inbox access, no-reply post messages, mobile carousel and article triggers from in-app buttons, iOS universal links, and API version 2.3 for exporting conversation data.
Intercom announced eight new features aimed at helping sales, support, and marketing teams manage volume and capacity more efficiently to increase conversions and grow their business. Custom Bots can qualify leads automatically, book meetings, and work 24/7 without code, connecting to the tech stack and accelerating sales cycles by delivering qualified leads when prospects are live. The new Intercom App Store offers over 100 apps for acquiring, engaging, and retaining customers, including Zoom Webinars, Content Showcase, Coda, and PieSync, along with a new Messenger framework and Developer Hub for building custom apps. Updated webhooks and APIs provide more flexibility to trigger actions based on conversation tags, check teammate availability with the Admins API, and run assignment rules at any point in a conversation. Other additions include downloadable conversation transcripts for both teammates and customers, and free developer workspaces for safe building and testing.
Intercom released the biggest update to its Messenger, completely rethinking how a business messenger should work beyond chat. With over 500 million conversations per month, the company observed that in 1 in 7 conversations people shared links to other tools, prompting the redesign. The update introduces a customizable Home screen that acts as a front desk, directing prospective and existing customers to the best outcome, whether talking to a person or self-serving. Messenger apps integrate third-party tools like Stripe, Shopify, and Google Calendar directly into conversations, and businesses can build custom apps for their own workflows via a new App Store. Intercom believes messengers will become the primary communication channel for internet businesses, combining personal conversation with intelligent automation to scale communication, diverging from personal messengers in important ways.
A Harvard Business Review study of 1.25 million sales leads found that responding within an hour makes a deal seven times more likely to close than a two-hour response. Live chat on a website is the fastest way to achieve that speed, bypassing the delays of contact forms and email follow-ups. The first tactic is to offer live chat only to prospects matching an ideal customer profile, using tools like Clearbit to filter visitors so SDRs focus on qualified leads. The second is to combine a bot for collecting contact information with human sales development representatives for personalized conversations, avoiding over-automation that annoys prospects. The third is to set proper expectations by stating office hours, displaying average response times when unavailable, and hiding the chat outside business hours, which allows reps to be more thoughtful in their responses.
Intercom’s second Customer Support Trends Report, based on a survey of over 1,200 global support leaders across tech, e-commerce, and finance, outlines five trends reshaping the industry in 2022. Customer expectations continue to rise, with 75% of leaders reporting a widening gap; experts advise blending proactive messaging, self-service, and easy access to human agents. Support teams face increasing pressure: 59% report higher incoming volume, 52% have reduced headcount, and 48% say those cuts hurt the customer experience, forcing leaders to find efficiencies without sacrificing quality. Agent burnout is prevalent, exacerbated by longer hours and disconnected tools that slow down nearly half of teams; leaders are redesigning workflows and rethinking traditional KPIs to better support their staff. Empathy has become a top priority for 73% of teams, but 21% feel they lack the enabling tools; building a sustainable empathetic culture requires a holistic investment in strategy, processes, technology, and people.
Intercom released Engineer Chats, an internal podcast about engineering topics, making its episodes publicly available. The podcast explores disambiguation, described by Senior Principal Engineer Mike Stewart as narrowing a wide solution space based on evidence, applied throughout product development from roadmap planning to coding. Another episode recounts Intercom’s worst outage, where a premature automation command triggered a cascading failure that queued SMS messages incorrectly for hours, revealing risks of automation. The podcast also discusses the importance of speed for startups, advising teams to run and do things this week instead of next quarter. Additionally, a conversation between engineering and legal teams emphasizes that legal is not there to slow R&D but to ensure responsible growth within legal confines through compliance by design.
Human support remains critical for customer satisfaction even as automation improves, especially for complex or emotionally charged issues that bots cannot handle. The Conversational Support Funnel framework helps companies balance proactive, self-serve, and human support layers to route only the most impactful queries to live agents. Automating repetitive tasks and integrating support tools frees team members to focus on meaningful problem-solving. Support leaders should rethink traditional metrics like CSAT to account for the difficulty and emotional toll of complex work, and provide clear escalation paths for difficult situations. Investing in team development and well-being through these practices improves retention, morale, and long-term customer loyalty.
Stuart, a logistics platform owned by GeoPost, faced the challenge of scaling support for its delivery partners without slowing response times or losing the personal touch. After analyzing their support journey, Head of LiveOps Andrew Baylis and Program Manager Marina Alejo identified that manual handling of every conversation was inefficient and implemented an Intercom Custom Bot, the courier bot. The bot operates across four delivery stages, resolving common queries automatically and collecting context before routing complex issues to agents, reducing manual intervention to 55%. As a result, the bot saves over 2,500 support team hours and automatically resolves 55,000 chats per week, with response times under 30 seconds and resolutions four times faster. Additionally, handling time per case is two minutes faster and delivery partner satisfaction has increased by 4%, with plans to further expand self-service capabilities.
Customer support is shifting from a reactive cost center to a proactive value driver, with leaders adopting conversational, messenger-based approaches to meet rising customer expectations. Proactive support uses targeted messages, banners, and custom bots to address known issues before customers ask for help, reducing inbound volume. Tanda doubled its client base while keeping its support team the same size, achieving better first-response times through proactive communication. TrueCommerce cut contact rates for temporary issues by nearly 80% by sending outbound alerts to affected users. These tactics help companies scale support, improve efficiency, and maintain customer satisfaction scores above 90%.
Companies scaling their customer service should first ask whether their tools are scalable, as legacy channels like phone and email are labor-intensive and difficult to scale. A next-generation messenger-based support solution can improve efficiency by collecting information upfront, enabling reps to handle multiple chats, and using bots for simple queries. Second, teams must maintain high standards as conversation volumes rise by using automation to resolve repetitive questions, freeing agents for complex issues. Third, businesses should adapt to evolving customer needs by incorporating self-serve and proactive support layers, reducing inbox volume and allowing focus on high-value VIP customers. Laying this infrastructure early can result in significant time and cost savings while improving customer satisfaction.
Self-serve support combines automation and human expertise to deliver scalable yet personal customer service. The Conversational Support Funnel framework integrates proactive, self-serve, and human support layers to balance efficiency and personalization. Case studies demonstrate significant gains: Stuart reduced support workload by over 2,500 hours weekly using Custom Bots, and in a test with a pharmacy client, 70% of inbound queries were resolved through self-serve. SMARTY saved 7,000 support team hours annually with Resolution Bot, achieving a 99% reduction in median first-response time. Livestorm maintained a 94% customer satisfaction score while automatically resolving one in four queries, and Rugs.com increased support efficiency tenfold by switching from phone to chat. The post notes that 57% of support teams now measure revenue impact, and self-serve support lowers response times, frees agents for complex issues, and drives retention and expansion.
Conversational support is a digital-first, messenger-based approach to resolving customer queries, enabling real-time or asynchronous communication and preserving context across interactions. By combining custom chatbots and automation, it streamlines support workflows and reduces repetitive tasks. A 2021 Forrester study commissioned by Intercom found messaging-based support became the second most used channel, with 60% of leaders reporting improved customer retention and 58% reporting increased efficiency. The approach benefits multiple teams: sales and marketing can capture qualifying customer data upfront, such as company size and plan tier, while product teams identify trends from support topics for better development decisions, and customer success teams deliver personalized experiences. For agents, automating low-value tasks like password resets reduces burnout, improves job satisfaction, and lowers turnover costs from hiring and training replacements.
NVIDIA’s Seattle Robotics Lab automated GB300 tester tray assembly, focusing on busbar insertion with 16 screws and multi-connector insertion into tight-clearance sockets. The team adopted a “right tool at the right time” strategy, implementing a classical modular pipeline with multi-view FoundationPose for 6D pose estimation, Lissajous-curve manipulation primitives, and high-performance impedance control, later transitioning to DOPER. Work was divided across three robot arms to reduce cycle time, and the system approached the 99.5% success rate and 124-second cycle time targets set by NVIDIA Operations and Foxconn. The key challenges were uncertainty in part geometry and dynamics, and the need for high performance beyond typical research success rates. The project demonstrated that modular systems with robust control can outperform end-to-end learning in constrained manufacturing tasks, challenging assumptions about where learning is most powerful.
HubSpot’s Director of Customer Support for EMEA, Noel O’Reilly, discusses the impact of AI on customer service in an Intercom podcast. He argues that AI should automate repetitive tasks, allowing support reps to focus on complex issues and build deeper customer connections, while gaining skills like helping customers optimize automation. O’Reilly emphasizes involving frontline staff in AI implementation to create a robust knowledge base and intuitive automation flows. He notes that maintaining high-quality customer experience requires new evaluation methods, as bot-influenced CSAT may be less relevant for high-performing teams. For small support teams, O’Reilly recommends starting with small AI projects, feeding bots from documentation, and iterating, as AI creates space for high-value work.
Cloudflare introduces Issues, a built-in error monitoring feature for the Workers platform now in open beta. It automatically groups repeated exceptions, HTTP 5xx responses, and error logs into a single issue, eliminating manual telemetry linking. Developers enable it by setting observability.issues.enabled to true in wrangler.jsonc, with no SDK required. Issues captures stack traces, console logs, distributed traces, and Worker version, and via automations sends failure context—including user-provided identifiers—directly to a configured coding agent like Claude Code, Cursor, or Devin. The agent receives a structured summary and can further query logs through the Cloudflare MCP to propose code changes and open a pull request. Internally, the Workflows team uncovered and fixed two production bugs within a day using Issues (a migration retry loop and an incomplete deletion process).
Stripe offers over 125 different payment methods globally, each requiring integrations that previously took up to six months because teams started from scratch. They hypothesized that reusable prompts with hardening could reduce effort, and a bake-off showed vanilla Claude Code took 20 days versus 4 days with their prompts. The resulting factory has created three new payment method integrations and migrated ten existing ones to a modern stack, reducing timelines from six months to two to six weeks. The factory also uses code-based orchestration, continuous knowledge loops where agents suggest prompt improvements, and agents testing their own code against sandbox environments. The factory’s approach has since been extended to Stripe’s tax and verification teams for building their own connectors.
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.
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