The first decision in any agentic project isn't which model to use. It's which framework will orchestrate it. Get that wrong and you inherit a stack you can't run locally, can't afford to scale, and can't escape when the vendor changes the API.

This is a JigsawFlux project. JigsawFlux builds open-source tools for health tech, humanitarian response, and crisis management — in places where "cloud-native" is not an option and IT budgets are measured in grants, not headcount. That context imposes hard constraints on every architecture decision: portability, cost, and freedom from vendor lock-in.

The frameworks here — LangGraph, CrewAI, and AutoGen — were chosen because they meet those constraints. They are open source, actively maintained, and run entirely on hardware you own. Alternatives like Microsoft Semantic Kernel or Amazon Bedrock Agents are capable, but they introduce hard dependencies on specific cloud ecosystems. That trade-off doesn't fit the JigsawFlux model.

The name cuts two ways. It's a clinic — for patients. And the clinic runs on agents.

The problem this POC targets is specific: small and charity hospitals where doctor time is genuinely scarce and IT budgets are measured in hundreds of dollars, not thousands. A consultation isn't just a diagnosis — it's intake, medical history retrieval, triage sorting, prescription recording, pharmacy stock checking. The typical workflow hands all of that to a doctor anyway, because there's no other option. The result: a clinician spending 40% of their time on work that doesn't require clinical judgment.

The premise here is simple. AI handles everything that doesn't require a clinician. The doctor steps in exactly once — to read the AI-produced intake summary and give a diagnosis. That's it. The prescription agent takes over from there.

This is a JigsawFlux project. JigsawFlux builds open-source tools for health tech, things that matters — tools that have to work in the real world, not the well-funded one. That means two hard constraints shaped every architecture decision here: cost and deployability. Viable on a shoestring budget. Runnable in places where "cloud-native" isn't an option — a clinic with a single server, unreliable internet, and an IT team of one.

Built on AWS Bedrock (Claude Haiku 4.5), LangGraph for orchestration, LangChain @tool wrappers for data access, and Streamlit for the UI. Total cost: < $0.01 per consultation. Deployable on a £25/month VPS or a clinic's own hardware, with the option to go fully on-premises as models improve.

Four months into 2026, Uber's AI budget for the year was already gone — thousands of engineers with un-gated access to Claude Code, bills reportedly running $500–$2,000 per person per month, and leadership asking very loud questions about what exactly all those tokens were buying. Around the same time, an unnamed "mystery company" reportedly burned $500 million on Claude credits in a single month — not from a runaway model or a billing bug, but because nobody had thought to put a usage cap on employee licences.

Neither story is about bad engineering. Both are about one broken default: when developers get unrestricted access to frontier models, they use frontier models for everything.

I've been working through a framework to fix this — not by restricting access, but by routing the right task to the right model. The goal is frictionless development that doesn't quietly drain your budget. Here's how it works.

In Part 1 I ran Ollama directly on a Linux machine and wired it up through an MCP layer to a small web app. It worked. But bare-metal has friction — if the process crashes, it stays down. Adding Open-WebUI means managing another process. Resource limits are manual. There's no clean internal networking between services.

This post moves the whole thing into Kubernetes. The goal isn't enterprise-grade infrastructure — it's a home lab setup that's reliable, easy to extend, and honest about its limitations.

Manifests are in the ollama-mcp-starter repo under backend/k8s-deployment/.

AI Tools for Architects: Beyond Code Generation​

Practical use cases for Solution, Infrastructure & Enterprise Architects Featuring Claude™, GitHub Copilot™, and Gemini™ + Antigravity™

The rapid explosion of LLMs and AI tools to assist software engineers and architects is mind-boggling. Selecting the right tools for each use case can be a daunting task. I have been experimenting with three superpowers: Claude Code™, GitHub Copilot™ + VS Code, and Antigravity™ + Gemini™. Variations such as using Cursor with Claude™ can easily be derived from this.

This article also shares a GitHub repo that demonstrates some of the architecture use cases.