Every post, summarised

Oct 5, 2026

• Salesforce

  AI Agents Need Memory: Building Persistent Memory for Agentforce

  Enterprise AI agents without persistent memory suffer from the “Groundhog Day” problem, repeatedly asking users to re-explain themselves. Agentforce Memory addresses this by giving agents persistent memory that retains user preferences, conversation history, and context across sessions. The team first established a shared terminology document to define concepts like semantic and episodic memory before architectural discussions could proceed. The architecture evolved incrementally, starting with conversation history injection and later adding semantic retrieval to find relevant past information regardless of recency. Memory retrieval uses keyword search during pre-orchestration to balance context quality with low latency, and moving memory into the Reasoner simplified configuration for administrators. Privacy and user trust were treated as architectural requirements from the start, enabling individual user controls over memory creation and management.

• Databricks

  How Genie Ontology powers product development at Databricks

  General-purpose AI agents fail on business questions because they lack enterprise context such as authoritative tables, business rules, and trusted experts; the missing institutional knowledge is scattered across dashboards, documents, and people. Databricks’ Genie Ontology addresses this by continuously updating institutional knowledge from certified Unity Catalog concepts and automatically learned snippets from dashboards, notebooks, SQL queries, and connected apps. OntoRank ranks sources by authority using certification, usage, and authorship signals, so Genie grounds answers in the most trusted and relevant data. In a weekly adoption review, a product manager prompts Genie, which uses MCP to search Google Docs and Jira while running live SQL for fresh analysis. The resulting report includes citations, forecasting, scheduling, and even conversion into a shareable Genie Agent, all within permission boundaries.

• AWS

  Announcing the AWS Digital Sovereignty Well-Architected Lens

  The new AWS Digital Sovereignty Well-Architected Lens offers extended guidance for designing, building, and operating sovereign workloads, addressing challenges not fully covered by existing guidance. It includes procedures to deploy Oracle Database on Amazon EVS with FSx for ONTAP, covering provisioning, NFS mounting, and SnapMirror replication. It details multi-day AZ evacuation drills with ARC Zonal Shift across ECS, EKS, RDS for PostgreSQL, and Aurora PostgreSQL, providing CLI commands and observability metrics. MHK built the SmartProminence AI Orchestrator on Amazon Bedrock, a HIPAA-eligible framework that reduced manual medical review by 90% and cut deployment from months to weeks. CSIRO developed Serverless Beacon for genomic variant querying on AWS, implementing the GA4GH standard with S3, Lambda, DynamoDB, and Athena.

• Clickhouse

  Executable UDFs are now generally available on ClickHouse Cloud

  Executable user-defined functions (UDFs) have moved from public beta to general availability on ClickHouse Cloud across AWS, GCP, and Azure. The GA release adds a Native runtime for precompiled statically linked binaries in Rust, Go, C++, and JavaScript, requiring both amd64 and arm64 architectures, alongside the existing Python support. UDFs now have network access to public endpoints, a per-process memory limit, a deterministic flag that enables query caching, and are not billed separately, running inside service pods. ClickHouse 26.6+ provides eight ProfileEvents counters and two asynchronous metrics for per-query UDF cost monitoring, and the Cloud API and Terraform provider support UDF management with per-service version pinning. The post demonstrates counting LLM tokens and grading completions using these UDFs, showing how to put tokenization libraries next to the data in ClickHouse.

• Jetbrains

  dotInsights | October 2026

  The dotInsights newsletter from JetBrains collects recent .NET and software development articles for developers. Topics include C# 15 collection expression arguments, union serialization with System.Text.Json, and control flow obfuscation for .NET and Java teams. Several posts focus on ASP.NET Core, covering middleware, RAG integration, and using passkeys for second factor authentication. The newsletter also features discussions on event-driven architecture versus CRUD over pub/sub and the impact of AI on database schema design. JetBrains announces the Rider 2026.2.3 release and explains how Microsoft helped fix slow startup times for Rider and ReSharper.

• Intercom

  Making the best of a bad situation: Lessons from an Intercom outage - The Intercom Blog

  Intercom’s outage began with a page about elevated exceptions, leading engineers to dive into Datadog and uncover a lurking 32-bit integer limit in one of the most critical parts of the app’s data model. The chaotic five-hour incident marathon required monkey patches, data migrations, and feature flag gymnastics to restore normal operations. After stabilizing the system, the team applied important technical changes to the Rails application to prevent integer overflow and similar failures. Miles McGuire presented the postmortem at Rails World 2024, sharing the debugging process and the key lessons learned from the unusual incident. The talk covers monitoring data model limits, using feature flags for safe rollouts, and enhancing application resilience to avoid future recurrence.

• Intercom

  How we scaled engineering enablement and infrastructure at Intercom - The Intercom Blog

  Intercom’s engineering enablement team transformed the company from a patchwork of legacy systems and recurring incidents into a stable infrastructure that empowers fast-moving product teams. The company learned that selecting core technologies is not enough without a clearly defined strategy for understanding, guiding their use, and investing in the teams behind them, as lacking this leads to persistent friction. They defined a dedicated core technology team, learned and standardized their tooling, and implemented approaches to hire and grow engineers who own the infrastructure. These efforts reduced operational friction and incident frequency, allowing the business to move more confidently. As a result, product teams run faster and the infrastructure is now a reliable foundation for growth.

• Intercom

  Intercom on Product: How we became an AI-first company - The Intercom Blog

  Intercom’s Chief Product Officer and Co-founder discuss how generative AI reshaped their strategy, product development, and internal rituals. They describe moving from isolated AI tasks to AI-native products built from the ground up with AI at their core. A key move was launching Fin, their AI agent, as a standalone product on competitor platforms like Zendesk, prioritizing AI leadership over platform exclusivity. They draw parallels to Facebook’s mobile pivot and the iPhone’s initial skepticism, arguing generative AI will face similar resistance but have profound disruptive potential. The conversation emphasizes that second- and third-order effects of AI will create entirely new workflows and behaviors, making full impact hard to predict.

• Intercom

  Start Good Trouble: Mastering SaaS metrics with Equals' Bobby Pinero - The Intercom Blog

  Equals co-founder and CEO Bobby Pinero joined the Start Good Trouble webinar to discuss how SaaS metrics unlock business potential and tell a compelling story. He argued that spreadsheets remain central despite the explosion of new BI tools, because everything eventually ends up back in a spreadsheet connected to live data. Drawing on seven years as Intercom’s senior director of finance, where he scaled ARR from under $1 million to over $150 million, Pinero highlighted the complexity of early-stage metrics like CAC, LTV, and retention. Equals recently published The Guide to SaaS Metrics to help leaders understand, optimize, and report on key drivers, and Pinero emphasized the need to tailor metrics to the business model and start with the story. He also recommended writing consistently and authentically to clarify ideas and connect with audiences, and noted that horizontal products require narrowing down use cases to scale effectively.

• Intercom

  This is getting real: Welcome to the AI revolution - The Intercom Blog

  The talk traces the tech industry’s journey from the late stages of SaaS in 2019, through pandemic virtual offices and NFTs, to the 2022 downturn and ChatGPT’s launch in November 2022. 2023 was a year of prototypes and ‘thin wrappers’ on ChatGPT, but also real learning about building with LLMs, including non-deterministic approaches. The author argues the AI transformation is now real, moving beyond superficial features to fundamentally rethinking products, exemplified by the shift from ‘Software as a Service’ to ‘Service as Software.’ The future of customer service includes proactive, personalized bots, video and voice bots, and the ability to analyze all conversations to deeply understand customers. The author cites Bloom’s 2 sigma effect as an example of AI’s potential in education and urges optimism, noting that learners will inherit the earth.

• Intercom

  The Ticket: Stacy Sherman on how to design customer experiences that drive loyalty - The Intercom Blog

  Customer expectations have never been higher, and the experience a business delivers can determine whether it wins or loses customers. Stacy Sherman, a CX author and speaker, joined The Ticket podcast to discuss her Doing CX Right framework for designing standout customer experiences. She emphasizes that companies must move from merely planning to taking action, and that walking in customers’ shoes to get the basics right is essential. Sherman advises validating every approach with real customers and ensuring internal teams feel valued to create a culture of customer excellence. She also recommends mapping the customer journey to identify where AI can enhance interactions and where the human touch should be preserved.

• Intercom

  The Ticket: Fueling The AI machine with the knowledge it needs

  The performance of an AI-driven customer support engine depends directly on the quality of the knowledge that feeds it, as accurate, up-to-date content is critical for giving customers instant and trustworthy answers. Investing time and care into help content can significantly boost automated resolution rates, as Intercom has experienced firsthand. Many busy support teams struggle to prioritize maintaining their help centers due to limited resources, but filling content gaps and updating content after launching new products is essential for AI success. Knowledge content compounds in value over time; Intercom’s transition from its Resolution Bot to the Fin AI chatbot resulted in resolution rates skyrocketing from the start. Knowledge management should be cultivated as a company-wide responsibility, with input from product, engineering, sales, and marketing teams, and in the near future, internal knowledge will also be fed to AI to help agents answer complex queries.

• Intercom

  Intercom on Product: The evolution of product management in the age of AI - The Intercom Blog

  Product management is at a critical juncture as generative AI reshapes how products are conceived, built, and used. Indiscriminately adding AI is risky; product managers must ensure the technology solves real user problems rather than being an end in itself. Curiosity is essential for driving exploration of generative AI’s new possibilities. A foundational understanding of AI is important, but the ability to collaborate with AI and ML experts is even more critical for bridging technical possibilities with customer needs. The future of product management will be shaped by those willing to learn, adapt, and explore the art of the possible.

• Intercom

  How AI is Transforming Customer Service Tech Stacks

  Customer service tech stacks have become fragmented and outdated, comprising many disconnected point solutions that slow teams and prevent fast, personal experiences. Recent advances in AI now enable a consolidated AI-first platform with AI at the core, empowering teams to analyze conversation data, spot trends, and deliver personalized support at scale using business and customer data. AI bots can first resolve repetitive queries 24/7, then build contextual experiences from historical data, and eventually take actions like setting up capabilities for customers. AI makes proactive engagement possible by detecting customer actions to preemptively unblock pain points, onboard users, and prevent friction. This shift improves metrics like customer satisfaction and operational efficiency and supports data-driven programs like Intercom’s Beyond CSAT to measure success.

• Intercom

  The Ticket: Amplitude’s Jenna Elliott on navigating the future of customer success - The Intercom Blog

  On The Ticket podcast, Intercom’s Declan Ivory interviews Amplitude’s Jenna Elliott about navigating AI-driven customer experience. Elliott argues a data-driven culture requires both qualitative and quantitative data to gauge team efficacy and identify areas for improvement, enabling targeted strategies that drive quicker value for customers. She highlights the balance between operational excellence and customer centricity, requiring a focus on KPIs and OKR frameworks as well as agile responsiveness to customer needs. AI should be implemented thoughtfully to complement rather than replace human interaction, boosting productivity and creating real value when integrated intentionally into the customer journey. New roles such as technical writers, customer journey designers, and strategists will emerge to ensure data is used in the right way to inform the customer journey.

• Intercom

  Understanding the Economics of Customer Service in the Age of AI

  Customer service has long been perceived as a cost center rather than a value driver, but the transformative arrival of generative AI, including tools such as Fin, is rapidly changing that equation. Understanding the economics of customer service involves balancing the financial costs of providing support against the value it creates, aiming for sustainability and alignment with overall business goals. AI automates routine tasks, enabling support teams to focus on more complex and value-adding activities that require stronger troubleshooting skills, subject matter expertise, and customer empathy. Integrating AI requires investment beyond just purchasing the technology, including knowledge management, content creation, and building an ecosystem that supports continuous learning by support teams. This alters the traditional linear scaling model, enabling companies to absorb increased workload without proportional expansion, leading to cost savings and more rewarding roles.

• Intercom

  The Ticket: Simon T. Bailey on the platinum service principles that create lifelong customers - The Intercom Blog

  The blog post recounts a podcast episode from The Ticket featuring Simon T. Bailey, author of Be the Spark, who discusses platinum service principles to create lifelong customers with Intercom VP of Customer Support Declan Ivory. Bailey’s pivotal realization came while sweeping streets at Disney, where he understood his true purpose was to create magical moments for families seeking a once-in-a-lifetime experience. He argues the platinum standard for customer service supersedes the gold standard by emphasizing personal connection, engagement, and making people feel valued rather than merely completing transactions. Key enablers include employee empowerment through recognition and sharing success stories, and leadership that avoids pitfalls like micromanagement while modeling the behaviors that enhance experience. Cultivating resilience and a genuine customer-love mindset drives loyalty, advocacy, and positive business outcomes, enabling teams to maintain high-quality service despite challenges.

• Intercom

  Intercom on Product: How to design standout products in an AI world - The Intercom Blog

  Intercom’s VP of Design Emmet Connolly and Staff Product Designer Molly Mahar discuss how product designers must adapt their methods for AI, drawing from their experiences building Intercom’s products. They explain that designing for AI means moving from deterministic to probabilistic systems, where unpredictability is the norm and designers must design flexibly around surprises. Data quality is foundational for reliable AI, so designers should prioritize it; realistic prototyping using actual data helps capture essential user feedback and evaluate output effectiveness. Extensive testing is necessary to anticipate possible system failures; if live versions catch designers off guard, testing must be more robust. Building user trust requires consistent positive experiences and transparency about how the system works and its data, enabling designers to create AI experiences that make users feel comfortable and confident.

• Intercom

  Crossing the threshold: Exploring Intercom's Customer Service Trends Report for 2024 - The Intercom Blog

  Intercom’s 2024 Customer Service Trends Report surveyed over 2,000 professionals, and in a podcast episode of The Ticket, three Intercom support leaders discussed the findings. The report found that customer expectations have hit an all-time high, with 87% of support teams reporting increased demands, driven by the availability of high-quality AI chatbots. AI adoption is accelerating: almost half of support teams already use AI, and 70% of C-level executives plan to invest further in 2024. Support roles are evolving, with new positions such as conversation designers and analysts emerging, as humans complement AI for complex issues. The report also reveals that only 18% of professionals believe their existing tech stack covers all needs, leading to demand for integrated AI-centric platforms and a shift in metrics like average handle time.

• Intercom

  Intercom on Product: Riding the AI wave in 2024 - The Intercom Blog

  Intercom leaders reflect on 2023’s generative AI dominance in a podcast, predicting deeper investments, AI-first companies, and hardware transformations like wearable tech and AI-native products in 2024. Standout moments include Paul’s AI holiday using phone camera translation for travel and Emmet’s praise for Nintendo’s Tears of the Kingdom physics innovation. Optimal AI deployment requires careful use-case selection, avoiding misapplication on structured tasks needing human oversight, while wearable tech success depends on seamless lifestyle integration over status. Customer support will evolve from ticket-resolution to a continuous relationship-building model, emphasizing ongoing engagement over finite tasks, with design playing a key role. A key concern is that over-reliance on AI tools as magic wands may erode critical thinking and judgment in product management and complex troubleshooting, where details matter.