Thoughts on building agentic AI systems that work in production.
Maintaining stable operation of the ARKONA ecosystem – particularly the compute-intensive workloads on our dual Tesla P40 GPUs – has been a significant engi
April 06, 2026For the past year, building ARKONA – an autonomous multi-agent AI ecosystem – has forced some incredibly practical decisions about where and how we
April 06, 2026At ARKONA, we’ve spent the last year building an autonomous multi-agent AI ecosystem. It’s currently managing 47 services across 23 Tailscale HTTPS ports, f
April 06, 2026Building ARKONA – my autonomous multi-agent ecosystem – has fundamentally shifted my thinking on AI system design. We’ve moved past simply *making* things
April 06, 2026At ARKONA, we've built an autonomous multi-agent AI ecosystem currently operating with 26 agents, covering domains from cyber-physical reverse engineering (Core
April 06, 2026At ARKONA, we're building an autonomous multi-agent AI ecosystem, currently comprising 47 services running across 23 ports on Tailscale HTTPS. This generates a
April 06, 2026The ARKONA ecosystem, a multi-agent AI system for cyber-physical reverse engineering and beyond, currently comprises 47 services spanning 23 Tailscale HTTPS por
April 05, 2026Maintaining situational awareness across ARKONA – my autonomous multi-agent AI ecosystem – demands more than just knowing its components are *running*. It r
April 05, 2026The core challenge in scaling an autonomous multi-agent system like ARKONA isn’t just building the agents themselves, but defining the boundaries of their aut
April 05, 2026Maintaining consistent performance in autonomous agent systems over extended periods is a significant challenge. It's not simply about building agents that *can
April 05, 2026For the past quarter century, I’ve been building and operating cyber-physical systems for defense. Over that time, I’ve seen a *lot* of compliance documenta
April 05, 2026Building ARKONA, my autonomous multi-agent AI ecosystem, presented a unique challenge: managing six distinct operational domains – CoreOps, BizOps, COMET, Dev
April 05, 2026Maintaining code quality at scale, especially within a rapidly evolving ecosystem like ARKONA, demands automation beyond traditional CI/CD. We’ve moved past s
April 05, 2026ARKONA, my autonomous multi-agent AI ecosystem, currently operates 21 of 23 services across 23 distinct ports. Initially, I anticipated a significant networkin
April 05, 2026I’ve spent the last 25 years building and securing complex cyber-physical systems for the government. Now, with ARKONA, my focus has shifted to building an au
April 05, 2026When your personal project crosses 47 services on 23 ports, "security" stops being a feature you bolt on and becomes a structural problem you solve at the archi
April 05, 2026When you're running 26 autonomous agents across six operational domains, the question of who does what — and how agents discover each other — becomes a real
April 05, 2026lsof Calls with a Single ss SnapshotMy ecosystem health check script was taking 14 seconds to run. That's 14 seconds of wall-clock time every time I wanted to know whether all 47 services were up
April 05, 2026Running 26 autonomous agents on a single ecosystem without them stepping on each other is a scheduling problem — and like most scheduling problems, the naive
April 05, 2026I run 47 services across 23 ports. Every one of them sits behind Tailscale, every one of them speaks HTTPS, and until recently, every one of them that required
April 05, 2026Publishing technical content at scale without sacrificing accuracy is a hard problem. I built a 5-agent newsroom inside ARKONA that now produces, edits, fact-ch
April 05, 2026Eighteen months ago, ARKONA was a single Python script that scraped firmware headers and dumped them into a SQLite file. Today it is 47 services across 23 ports
April 05, 2026The gap between an AI demo and a production agent system is enormous. When you chain LLM calls across long-horizon tasks, failure modes compound in ways that prompt engineering alone can't solve. What's missing is the discipline that systems engineers have applied to safety-critical infrastructure for decades.
Read more →Most agent demos show short-horizon tasks that complete in seconds. Real production agents need to maintain coherence across hours, days, or longer. This is a fundamentally different design problem that demands careful attention to memory architecture, context compression, and communication protocols.
Read more →Not every task needs the most capable model. In production multi-agent systems, you're making hundreds of LLM calls per workflow. Cost and latency add up fast. A hybrid routing approach that dynamically matches model capability to task requirements is essential for sustainable production AI.
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