Chris Johnson

Today marks Six months in as SOCPAC CTO, and it somehow feels like both yesterday and six years ago. When I stepped into this role, we were behind the curve. Legacy systems, siloed data, and slow processes held us back. In just half a year, we’ve laid a Foundational Technical Architecture that gave us a modern backbone for data, AI, and autonomy. Since then, we’ve: 1. Unified our digital foundation for secure, scalable operations 2. Embedded AI and LLMs into daily workflows to speed decisions 3. Deployed containerized AI agents across all networks 4. Integrated autonomy at the edge, moving toward true man–machine teaming 5. Shifted our culture from cautious adoption to championing innovation The result? We’re no longer catching up, we’re setting the pace. What once felt like a distant goal is now part of our daily reality. All of this happened in six months, which is why it feels so surreal. It took countless late nights, cross-functional sprints, and the relentless dedication of an amazing team. I'm humbled by the trust the organization placed in me to drive this transformation, and I'm grateful to every person who rolled up their sleeves to make it real. This was truly a team effort, from the junior analysts using our new AI tools to the senior leaders who empowered us to take bold risks. And we’re just getting started. The foundation is in place, but now we get to build on it. In the next chapter, we'll be refining and scaling these capabilities, drilling deeper into AI-enhanced decision-making, and pushing the envelope on autonomous operations. The technology will keep evolving, and so will we. Our goal remains the same: give our operators a decisive edge, making it an extension of the warfighter and keep innovating responsibly. I’m humbled by what the team accomplished in such a short time, and excited for what’s next. The foundation is in place. Now we build on it. Six months down, and the journey is just beginning.

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Adding Document Understanding to Claude Code 📑🤖 Coding agents are awesome but they don’t natively understand docs (they kind of do, but it sucks). This limits their ability to build business applications - think about anything from a financial copilot to contract reviewer to rfp synthesizer to synthesizing PRDs. I wrote a blog to capture core patterns for adding document understanding to Claude Code - applicable to coding agents in general. This both helps Claude Code 1) get enterprise context when building apps, and 2) allows it to build more agentic, robust apps in general. 1️⃣ Add an MCP server that preindexes your file knowledge base 2️⃣ Let Claude Code loose on your filesystem with the right CLI tools for understanding files. 3️⃣ Give Claude Code context on core modules for document understanding and agentic workflows - this last step is important if you want to build “better apps in general” and not just “rule-based apps”. Blog: https://lnkd.in/g4EEFftk The blog offers general principles regardless of whether you’re using our stuff, because we fundamentally think that coding agents + docs leads to a ton of useful applications. Of course we are deeply investing in tooling in this space too (some referenced in the blog), a lot of it dependent on LlamaCloud. Check it out: https://lnkd.in/g9Wpqn7w

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📅 Today at revAlation Chicago, we’re unveiling Alation Agent Builder— enabling you to build accurate, configurable, governed, metadata-aware agents. Prototypes are easy. Accuracy and trust are hard. Especially with structured data, where even a small error in a query or filter can break trust. Agent Builder helps teams: 🚀 Reach 90%+ accuracy before deployment with built-in evaluation tools 🛠️ Build agents with no code and prebuilt templates 🤝 Connect to 100+ data sources and apps 🏢 JLL is already using it to power intelligent lease decisions with trusted data (and they're speaking at revAlation today!). https://lnkd.in/g58AeF7u

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So which models produce HTML that has the least accessibility errors, and at what cost? Mapped it out here (bottom left is the best). This is helpful for understanding which models are most compliant for those with disabilities and do it efficiently. Qwen and Gemini 2.5 Flash lead the pack. Hat tip to Ben Ogilvie for pointing me to this: aimac.ai

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Most software deployments feel like marathons. Six months of dragging things across the finish line, endless check-ins, and waning momentum. That’s not how we approached Kalamazoo. As Jeff Troyer of Kalamazoo County County Consolidated Dispatch put it in our chat, it was a sprint: Dedicated teams on both sides, clear two-week window to get it done, and rapid iteration after go-live. The result was faster implementation, stronger adoption from the team, and Aurelian providing customer support that actually feels like support. Aurelian is proving that with the right focus, public safety agencies don’t need to settle for six-month rollouts.

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Uptime isn’t a goal. It’s a side effect of doing everything else right. If your leadership only cares about uptime after an outage, you don’t have an SRE function, you have scapegoats. Reliability and quality should be at the beginning of every product development conversation. Relying on post-incident heroics is one of the least efficient ways to effectively achieve reliability, especially at scale. Every outage costs more to resolve than it would have cost to prevent. But that should be obvious and a statement that goes without saying. It drains time, energy, and focus that could have been spent improving systems and building better product instead of repairing them. Everyone needs to be part of the reliability conversation before incidents happen, when initial investment and prevention can make the biggest impact. If executives and people only show up after the fact, the temptation is to find someone to blame rather than address the systemic gaps that caused the problem in the first place. Strategic investment in resilience upfront is not just good engineering, it’s sound business. If your reliability work begins when the incident starts, you’re not building for the future. You’re just cleaning up the past.

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Today I am excited to announce Digger’s BIGGEST update ever Project OpenTaco - The Open Standard for Terraform automation. With OpenTofu, where Digger is a founding member, the community already has a credible open-source CLI alternative to BSL-licensed Terraform. But as we all kno that only solves half the problem. The CLI helps teams avoid lock-in, but it doesn’t address governance or collaboration challenges at scale. Project OpenTaco builds on this foundation, without taking an opinion on which IaC language is used, giving teams a path to adopt more Infrastructure-as-Code with a TACOs that is open, self-hostable, and free from licensing traps or per-resource pricing. We believe that it this is “Terraform’s backstage moment”. Backstage became the open standard for Internal Developer Portals, we believe Project OpenTaco will do the same for IaC automation, setting the standard, driving adoption, and making closed platforms unnecessary. The future of IaC is open, and we’re building it brick by brick. Check out our roadmap and sign up for updates, we’d love any and all feedback! Link in the first comment ->

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Validating Risk Scores at the Source Our HCC-In-FHIR package keeps getting more robust every day. Though I'll admit - the name's a bit misleading now since we've expanded beyond just FHIR (see the figure below). The package now handles three primary formats: X12 837, FHIR, and custom formats. But here's the key: it calculates CMS HCC risk scores directly from your source data. Why does this matter? Every data transformation step between your source data and final risk scores creates two problems: - Delays in getting actionable insights - Information loss along the way Anyone who's tracked a single claim from practice to CMS encounter data knows exactly what I'm talking about. The amount of data that gets lost from source to destination is staggering. By calculating risk scores directly from the data you receive from clearinghouses and submit to CMS, you get true visibility into what's actually happening - and what the real delta is. The package remains open source (https://lnkd.in/ebEjSqAc), but if you need help implementing it, we're here. At falcon health, we're already helping many MAPDs gain full visibility into their claim journey and HCC performance (feat. Prince Baawuah, DrPH, MPH, CSM®) Interested in learning more? Let's connect!

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Maxim Fateev's blog is a must read ... It's direct talk from his experience. "After spending over two decades building orchestration solutions. I’ve seen the patterns, I’ve tried the different approaches, and I’ve watched the industry rediscover the same lessons over and over" So many parallels to why Infrastructure as Code won over the gazillions of visual infra layout tools ... https://lnkd.in/gB_G3ijA Temporal Technologies

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🚨 Buying a CMS is hard. Implementing it well is even harder. At Atlassian, I helped lead the selection and rollout of a new CMS. We ran a 90-day pilot, secured funding, and did some things that aren’t as common as they should be: ✅ Created a triad leadership model (PM, dev lead, content design) ✅ Did user research with internal stakeholders ✅ Evaluated vendors on both tech specs and team dynamics ✅ Ran a proof-of-concept before signing any contracts It taught me how noisy and overwhelming the CMS landscape can be—even for well-resourced teams. ⛔️ Vendor-created comparisons? Biased. ⛔️ Analyst reports? Expensive and often missing the practical stuff. So I launched something new: 👉 Choose Your CMS (link in comments ... sigh) It’s a free service to help CMS buyers: 🎁 Get personalized, expert recommendations 🎁 Assess their implementation readiness 🎁 Avoid common pitfalls If you’re about to choose a CMS—or trying to make the one you picked actually work—I’d love to help. Check it out or share with someone who might need it. #CMS #ContentStrategy #ContentOperations #ContentDesign #DigitalExperience

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We recently released lifecycle reporting in DX, and I’ve loved seeing it help Platform teams build a concrete business case for funding important Platform and DevEx initiatives. While traditional metrics—like build times, pull request merge times, and issue cycle times—offer useful insights, they cover only a narrow slice of developer workflows. They also often fail to distinguish between non-blocking delays (where developers can switch context and stay productive) and true friction points that drain valuable engineering time. Lifecycle reporting helps pinpoint where in the development process teams are experiencing the most friction and delays—and ultimately costing organizations the most. Just like in the screenshot below, I’m seeing companies lose thousands of engineering hours each year to challenges understanding existing code, lengthy CI processes, dev environment issues, and excessive meeting schedules.

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