Jhon Arango | Agentic AI Systems Engineer

ARKONA

Agentic AI Systems Engineer

Every organization is rushing to deploy AI.
Nearly 80% of AI projects fail.

— RAND Corporation, 2024: "More than twice the failure rate of IT projects that don't involve AI"

The reason? Organizations buy AI tools without a methodology for integration. No structured analysis of which tasks AI should own. No accountability framework. No delegation governance. They skip the hardest question: who is responsible when the AI makes the wrong call at 3 AM?

I built the system that solves this.

COMET identifies where AI belongs

Decomposes any domain — aircraft maintenance, healthcare, finance — into organizational structures, job roles, and tasks. Then classifies each task across 5 delegation levels from fully human to fully autonomous, grounded in 20 industry standards. The output: an auditable RACI matrix with the AI agent as a first-class participant.

ARKONA builds and operates the agents

A production software factory that builds, deploys, and monitors AI agents with the same rigor as mission-critical defense systems. Rigorous logging, circuit breakers, provenance signing, and human oversight at every level. Not a demo — a system that runs at 3 AM and can be explained to regulators.

Services

AI Agents

928

Commits

View My Work Contact Me

226K+

Lines of Code

Python, JavaScript, JSX, Shell, CSS, HTML, YAML

Domains

Each with dashboard, API, agents

HTTPS Ports

Tailscale zero-trust

47%

MuXD Token Savings

Hybrid LLM routing

MCP Servers

Local LLM Models

Standards Cited

COMET Views

Inside the Ecosystem

47 services, 18 agents, 6 MCP servers — how they connect.

What I Build

Multi-Agent Orchestration

Purpose-built agent harnesses where specialized AI agents collaborate on complex tasks — with structured communication, shared memory, and human-in-the-loop governance at every stage.

Hybrid LLM Routing

Intelligent model selection that dynamically routes between cloud and local models based on task complexity, context requirements, and cost constraints — optimizing for both capability and efficiency.

AI Governance & Evaluation

Real-time monitoring, evaluation, and control systems for autonomous AI operations — tracking agent decisions, resource usage, and performance across multi-agent workflows.

Battle Rhythm

18 autonomous agents on coordinated cron schedules. The ecosystem manages itself.

            
            Always Running
          

*/2 min — GPU Thermal Guard

Throttle 75°C · Kill 80°C · Dual P40

*/5 min — Watchdog

47 services · Auto-restart · Circuit breaker

*/5 min — Reboot Monitor

Detects restarts · Re-launches services

Hourly — Auto-Commit

Preserves work every hour across repos

Hourly — Code Health

TODO drift · Test failures · Unpushed commits

Hourly — Activity Logger

Git activity + service state snapshot

Hourly — Article Agent

MuXD draft → Claude polish → Cloudflare deploy

*/2 hr — Feed Agent

Ecosystem status posts · 12 categories

            
            Daily Schedule
          

02:00 — Research Agent

State-of-the-art scan · 8 FS-RE layers · Brief

03:00 — Night Build

Snapshot → plan → build → test → deploy

06:15 — R&D Publisher

5-agent editorial → Wiki.js articles

06:30 — Daily Summary

Operations report · Git activity · Service health

06:45 — Podcast (Heart)

Script → Kokoro TTS → MP3 → Published

4x daily — Interview Prep

Flashcards from live ecosystem data → Scholar

12:00 — Noon Checkpoint

End protected hours · Resume operations

*/6 hr — Nextcloud Sync

Databases · Reports · Configs → Cloud backup

*/6 hr — Metrics Watchdog

Cross-validates counts across all domains

20:00 — Metrics Sync

Sync to WEBMASTER + Nextcloud + Broadcast

23:00 — Stats Updater

Update live numbers → Deploy to Cloudflare

Infrastructure Over Frameworks

When an agent fails at 3 AM, you want to tail a log file — not trace through a callback chain.

            ✗ LangChain / CrewAI
          

• Python classes, chains, graphs

• Framework runtime, event loops

• In-memory state management

• Framework-internal communication

• Step through chain logic to debug

            ✓ ARKONA
          

• Bash scripts + claude --print

• Cron schedules, OS process mgmt

• Filesystem + SQLite (survives crashes)

• Cron pipeline + message broker + MCP

• tail /tmp/agent.log — done

Each agent is a single file. Testing is bash agent.sh. Adding an agent is 5 lines of YAML. The same reason production databases use systemd, not a Python wrapper.

COMET — AI Governance Framework

Cognitive Operations & Mission Effectiveness Taxonomy

The reason ARKONA exists.

Upload Docs

SOPs · Org Charts

→

AI Analysis

Extract Roles/Tasks

→

Classify

5 Delegation Levels

→

Workshop

Facilitated Session

→

RACI Matrix

Human + AI Agent

Real Example: Aircraft Maintenance

NDI Inspection

Non-destructive testing per TO 33B-1-1

Level 2

Human sign-off · NAS 410

Parts Requisitioning

DIFM assets via SBSS/D035K

Level 5

Fully AI · ISO 9001 §8.4

Engine Trending

JOAP oil analysis + vibration data

Level 4

AI-led · Human oversight

RACI Output Preview

Task	Supervisor	Inspector	Mechanic	AI Agent
NDI Inspection	A	R	I	C
Parts Requisition	I	I	C	R
Engine Trending	A	C	I	R

R=Responsible A=Accountable C=Consulted I=Informed Every cell cites the governing standard

Facilitated Workshop

Admin View

Real-time consensus dashboard. See who answered, agreement levels, and disagreements flagged for discussion.

Client View

One question at a time. Large touch targets. Framework citations shown per task. No distractions.

Within 16 to 24 business hours, you walk out with a standards-grounded RACI matrix.

That is not a demo. That is a consulting deliverable.

Latest Articles

View All Articles

Let's Build Something

Currently exploring opportunities in agentic AI systems, AI safety, and applied AI research.

Get In Touch About Me

jhon.arango@IntrepidCyberSecEng.com
(667) 355-4069

Scan to save contact

Request Ecosystem Access

The ARKONA ecosystem is invite-only. Request an invite code to explore the platform.