During the discussion with Span, we talked about a challenge many eng leaders are navigating: AI is increasing coding throughput, but overall delivery impact can feel flat as code review, rework, and quality bottlenecks emerge. We also discussed what it would take to measure AI impact day-to-day: going beyond vanity metrics and surveys to instrument the full SDLC, surface actionable insights, and tie adoption to quality and cost.

D2 Product Preview - Span: From AI Adoption to Engineering Impact

During the discussion with Ren, we chatted about a challenge many engineering leaders are feeling: real people development keeps getting squeezed by meetings and delivery pressure, even as expectations for feedback and growth rise. We also discussed what it would take for AI-assisted coaching to work day-to-day: low-friction usage, integration with existing workflow artifacts, clear privacy boundaries, and nudges that improve the quality of human conversations.

D2 Product Preview - Ren: Building an AI Coach for Eng Managers — A Non-Technical Founder's Journey

D2 Roundtable: Adopting AI DevTools in your organization

In this demo, we explore how Superlink is addressing the challenges language models face when processing structured data. By creating custom vector embedding models, they improve the efficiency of encoding and retrieving information from complex data sets. Their approach simplifies the process with a pipeline for data ingestion, embedding, and query matching, making it easier to extract valuable insights from varied data types.

Superlink’s platform provides flexible deployment options, including personalized recommendations based on user behavior, and is built to integrate seamlessly with major cloud providers. Their vendor lock-in strategy focuses on compute rather than storage, offering a powerful solution for AI engineers working with large datasets.

This demo gives us a closer look at Superlink’s innovations and how they could shape future advancements in machine learning and data management.

D2 Portfolio Demo - Superlinked

As more teams move beyond chat over data and start giving agentic systems the ability to&nbsp;take real actions—triggering workflows, changing state, even touching money—the old safety patterns start to crack. Role-based access control on APIs and a few regex-based guardrails on model output aren’t enough when you have autonomous or tool-using agents making decisions inside complex, ambiguous business processes. Agentic systems today are substantially limited by hallucination, adherence problems, poor context length, increasingly high token use, and model attention.In this roundtable, we’ll explore how to make agents significantly more effective, using emerging patterns for&nbsp;agentic safety: how we direct and constrain what agents are allowed to do, on whose behalf, and under what conditions. We’ll focus on&nbsp;semantic authorization—authorizing “what this action means in the business context” rather than just “which endpoint is called”—and on practical, layered&nbsp;guardrails&nbsp;that go beyond prompt engineering or AI red-teaming.Chad Wathington is the CEO of Manual Software. His career has spanned product, strategy, and engineering leadership roles. Some of you may have attended his session @ ELC Annual 2024. He will give a brief overview and demo of a semantic authorization control plane, and then the group will spend most of the time comparing notes:<ol><li data-list="bullet">How are you preventing unsafe or unintended agent actions today?</li><li data-list="bullet">Where do current tools and patterns fall short for your domain?</li><li data-list="bullet">What would a credible safety, governance, and audit story look like for agents in your organization?</li></ol>

D2 Product Preview: Agentic Safety, Semantic Authorization & Guardrails for Real-World AI Actions

As AI applications increasingly combine vision, audio, video, and LLMs, teams are drowning in fragmented infrastructure. A typical production AI workflow requires stitching together 5+ services: cloud storage for raw files, a separate metadata database, an orchestrator like Airflow, embedding services, vector databases for similarity search, and hundreds of lines of glue code to keep everything in sync. Every time a model improves or business requirements change, you're rewriting pipelines and re-processing entire datasets. This isn't building AI - it's maintaining infrastructure.In this session, you'll see how a declarative approach to AI data infrastructure eliminates the glue code and maintenance burden. Instead of writing pipelines, you define transformations as computed columns and tablesAnd we'll walk through a real multimodal workflow, e.g. starting with raw video, extracting audio, transcribing with Whisper, generating embeddings with CLIP, and building semantic search. Then we'll show what happens when you update upstream data and/or add new transformations.This session is most relevant for ML Engineers, AI App Developers, and technical leaders who are:<ol><li data-list="bullet">Building production AI applications that combine images, video, audio, or documents with LLMs</li><li data-list="bullet">Currently managing multiple services (S3 + Postgres + Airflow + Vector DB + custom scripts)</li><li data-list="bullet">Hitting scaling pain points: slow iteration, complex model updates, or lack of reproducibility</li><li data-list="bullet">Looking to reduce infrastructure complexity without sacrificing capability</li></ol>

D2 Product Preview: The Multimodal Shift - Rethinking Data Infra for the Age of Vision, A/V & LLMs

AI coding tools are rapidly becoming part of everyday engineering work. Adoption is rising, teams are experimenting, and executive expectations are high. But many organizations are only beginning to ask the harder leadership question: how do we turn AI coding activity into measurable impact on how software actually gets built?Join fellow engineering leaders for a practical, open roundtable hosted by Stephen Poletto, Field CTO at Span, where we’ll compare notes on:<ol><li data-list="bullet">The decisions leaders face once AI tools are in active use across teams</li><li data-list="bullet">Early signals to evaluate impact at the org level, even without fully mature metrics</li><li data-list="bullet">Patterns emerging around speed, friction, quality, and developer experience</li><li data-list="bullet">Where AI is creating real leverage today — and where it’s introducing new challenges and risks</li><li data-list="bullet">How senior leaders are grounding AI conversations with executives and boards in evidence, not hype</li></ol>Stephen will share observations from across engineering organizations, with a focus on what’s working (and what’s not) as teams move from pilots to scaled, sustainable impact. This is a peer discussion for leaders navigating the shift from AI adoption to understanding impact.Come exchange perspectives with peers, pressure-test assumptions, and leave with clearer ways to think and talk about AI’s role in your engineering strategy.

D2 Product Preview: From AI Adoption to Engineering Impact

For the last decade, I've worked with engineering leaders on a problem that keeps getting worse: helping their teams grow. Not just skill up—actually grow as people through their work. I got obsessed with this because it changed my life: realizing personal and professional growth aren't separate things.We had some success, but kept hitting the same wall: managers would learn new coaching approaches, get excited, then get buried. Sprint planning, incidents, roadmap pressure. Within weeks, people development was back on the back burner. The urgent always beat out the important.Then the last few years made it worse. Remote work, higher stress, more isolation—and somehow managers are expected to get&nbsp;more&nbsp;from people while having less time to actually develop them.Why I'm Building This (as a non-technical founder):In 2023, I realized AI might finally make it possible to put a tool in managers' hands&nbsp;in the moment—not another dashboard to check, but something that actually helps them coach better without adding work.The Hard Technical Problems We're Solving:<ol><li data-list="bullet">Making AI that challenges, not just answers:&nbsp;Getting the AI to push managers to think deeper (not just give them scripts) has been brutally hard</li><li data-list="bullet">Data without surveillance:&nbsp;We're figuring out how to pull useful context—Slack, meetings, PRs—without becoming a monitoring tool. "Ren is not a narc" is our design principle</li><li data-list="bullet">Zero friction:&nbsp;If it takes more than 10 seconds or requires leaving their workflow, managers won't use it</li></ol>What's Working So Far:<ol><li data-list="bullet">We're clearly solving a real problem—validation has been strong</li><li data-list="bullet">The AI finally works well enough to be useful (took longer than expected)</li><li data-list="bullet">Early users are actually changing how they manage, not just collecting insights</li></ol>What I'm Still Wrestling With:<ol><li data-list="bullet">How much automation is helpful vs. making leadership feel mechanical?</li><li data-list="bullet">Getting the data integration right without creeping into surveillance territory</li><li data-list="bullet">Building AI that helps managers be more human, not less</li></ol>This is a conversation about building AI tools for the messiest part of engineering&nbsp;leadership, the&nbsp;human part. Happy to dig into technical architecture, product decisions, or just the weird experience of being a non-technical founder building AI tooling for devs.

D2 Product Preview: Building an AI Coach for Eng Managers — A Non-Technical Founder's Journey

Tech debt—it piles up faster than we can pay it down. Keeping systems updated while shipping new features often feels like running on a hamster wheel. But does AI and automation help break this cycle? Join fellow engineering leaders for a lively roundtable where we’ll swap war stories and insights on: <ol><li data-list="bullet">How AI-powered tools are speeding up tech debt resolution </li><li data-list="bullet">Strategies to keep systems updated without derailing roadmaps </li><li data-list="bullet">What’s hype vs. what’s actually working in AI-driven maintenance </li><li data-list="bullet">Lessons learned—successes, struggles, and unexpected surprises </li></ol>Come share, learn, and maybe even walk away with a new AI trick to finally get ahead of that never-ending backlog!

D2 Roundtable: Taming the Tech Debt Beast - How AI is Changing the Game

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