Jhon Arango | Agentic AI Systems Engineer

ARKONA

Agentic AI System of Systems

Organizations are 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?

ARKONA was built to solve 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

1096

Commits

View Portfolio Get In Touch

226K+

Lines of Code

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

Domains

Each with dashboard, API, agents

Standards Cited

NIST, ISO, OWASP & more

47%

API Cost Savings

Hybrid LLM routing

Inside the Ecosystem

Dozens of services, autonomous agents, and MCP servers — how they connect.

What We 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

Autonomous agents on coordinated schedules. The ecosystem manages itself.

            
            Always Running
          

GPU Thermal Guard

Real-time monitoring · Auto-throttle · Multi-GPU

Service Watchdog

All services · Auto-restart · Circuit breaker

Reboot Monitor

Detects restarts · Re-launches services

Auto-Commit

Preserves work every hour across repos

Code Health

TODO drift · Test failures · Unpushed commits

Activity Logger

Git activity + service state snapshot

Article Agent

Draft → Edit → Publish pipeline

Feed Agent

Ecosystem status posts · 12 categories

            
            Daily Schedule
          

Research Agent

State-of-the-art scan · Multi-layer analysis · Brief

Night Build

Snapshot → plan → build → test → deploy

R&D Publisher

Multi-agent editorial → Knowledge base articles

Daily Summary

Operations report · Git activity · Service health

Podcast Agent

Script → Voice synthesis → MP3 → Published

Study Agent

Generates flashcards from live system data

Midday Checkpoint

Schedule transition · Resume operations

Cloud Backup

Databases · Reports · Configs → Encrypted sync

Metrics Watchdog

Cross-validates counts across all domains

Metrics Sync

Sync stats across portfolio + backup + alerts

Stats Updater

Update live numbers → Deploy to CDN

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

COMET Works Across Any Domain

Aircraft Maintenance

NDI Inspection L2 Human

Parts Requisition L5 AI

Engine Trending L4 AI-Led

Business Operations

Invoice Processing L5 AI

Budget Approval L1 Human

Expense Reporting L4 AI-Led

Business Development

Lead Generation L5 AI

Proposal Drafting L3 Hybrid

Client Negotiations L1 Human

Cybersecurity Operations

Log Analysis L5 AI

Incident Response L3 Hybrid

Threat Escalation L1 Human

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.

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Let's Build Something

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

Get In Touch About

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

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Request Ecosystem Access

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