Conduct
Live 24/7Research agents run on a schedule
- Daily scan: papers, tools, CVEs — 02:00
- Weekly domain fleets file scoped findings
ARKONA Research — AI governance connected to technical implementation, down to the task level
Ideas|Innovation|Evidence|Impact
Agentic AI System of Systems
An on-premise agentic artificial intelligence platform for real-world systems
To understand why it exists, consider where organizations stand today.
Some organizations are racing to deploy AI. Others are still deciding where to start. Both reach the same question: which decisions should AI own, and who stays accountable for them?
80%
of people using AI say it made them more productive.
37%
of their organizations can point to any effect on earnings.
6%
call that effect significant.
Same survey. Same year. Same organizations.
— McKinsey, The state of AI in 2026: On the road to ROI (n=1,719)
And this is not new.
According to MIT’s 2025 State of AI in Business report (Project NANDA):
“Organizations are rushing to deploy AI; however, 95% of enterprise GenAI pilots fail to deliver measurable business impact.”
Broader studies put AI project failure at more than twice the rate of ordinary IT. — RAND, 2024
What separates that 6% is not more AI. Nearly three-quarters of them fundamentally redesigned the work itself — against about one-quarter of everyone else.
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.
Read the analysis: why the value gap is a traceability problem →
Understand the domain
Map the work
Define the task
Establish accountability
Build and evaluate
Measure the outcome
AI Governance.
Engineered for measurable impact.
Governed autonomy. Provable ROI. — AI should augment human capability, not replace human judgment.
1
COMET identifies where AI belongs
Works on any domain or subdomain that breaks down to role, task, and subtask levels: cybersecurity operations, business development and operations, GOVCON proposal management, research and development. Decomposes each into organizational structures, job roles, and tasks, then classifies every task across 5 delegation levels from fully human to fully autonomous, grounded in 20 industry standards. The output: an auditable RACI matrix that names the AI agent alongside the humans accountable for the work.
2
ARKONA builds and operates the agents
A production software factory that builds, deploys, and monitors AI agents with the discipline of mission-critical defense systems. Tamper-evident logging, circuit breakers, signed provenance, and a human in the loop at every escalation. Not a demo — a system that runs at 3 AM and can be explained to regulators.
61
Services
19
AI Agents
6029
Commits
198K+
Lines of Production Code
Across 6,300+ source files — Python, JavaScript, TypeScript, Shell, YAML
8
Domains
Each with dashboard, API, agents
20
Standards Cited
NIST, ISO, OWASP & more
47%
API Cost Savings
Hybrid LLM routing
From one commit to sixty-one services
First commit on 26 March. Every line below is scaled to its own peak — 100% is today — so commits, lines of code, services, and domains compare on the same axis.
Snapshot date
Thursday, 26 March 2026
Day 1 of 33
Cycle 1 / 6
Inside the Ecosystem
A layered architecture designed for autonomous operations with human oversight at every level.
Capture
Evidence retained with provenance
- VAULT stores the source, not the summary
- CHRONICLE keeps facts, episodes, foresights
Report
Findings written up, cited and kept
- Every claim cited, to a source you can open
- Research notes, open to read
8 domainsIndependent apps, dashboards & APIs
18 agentsAutonomous, live 24/7
Zero-trustAuth & encryption on every hop
Hybrid routingLocal inference where it fits
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.
Agent Skill Builder
A closed-loop pipeline from governance to local inference. COMET RACI output feeds into Anthropic’s Agent SDK to construct task-specific agents. Training data accumulates from live execution, then QLoRA fine-tunes capable local models — on-premise agents at a fraction of cloud cost.
Tested With Foundation Clients
ARKONA has been run end to end with foundation clients in aviation operations and regulated hiring — separate tenants, separate data, separate compliance obligations, on one platform. Two domains that share nothing operationally, both running on the same governance model.
Early by design. These deployments exist to harden the system against real work before it is sold widely — which is the point of a foundation client. Organisations are not named.
Battle Rhythm
Twenty-three jobs across the day. Mean time to fault detection: under sixty seconds via the trust watchdog. 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 — doneEach agent is a single file. Testing is bash agent.sh. Adding an agent is 5 lines of YAML. The same reason no SRE wraps PostgreSQL in a Python event loop — operational systems live in the OS, not the application runtime.
We do use Anthropic’s Agent SDK — for what it’s good at: structured prompt construction. Runtime orchestration stays in cron, systemd, and bash.
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
Missing SOPs or written job descriptions? No problem — the facilitated workshop builds the role and task taxonomy from scratch, with your experts in the room.
COMET Works Across Any Domain
Intelligence Analysis
Collection Triage
L4 AI-Led
Report Drafting
L3 Hybrid
Analytic Judgment
L1 Human
RMF Compliance
Evidence Collection
L5 AI
POA&M Updates
L4 AI-Led
Authorization Decision
L1 Human
Capture & Proposals
Opportunity Monitoring
L5 AI
Proposal Drafting
L3 Hybrid
Bid / No-Bid
L1 Human
Cyber Defense Ops
Log Analysis
L5 AI
Alert Triage
L4 AI-Led
Threat Escalation
L1 Human
RACI Output Preview
| Task | ISSM | ISSO | Auth Official | AI Agent |
|---|---|---|---|---|
| Evidence Collection | C | A | I | R |
| POA&M Updates | A | R | I | C |
| Authorization Decision | C | R | A | I |
R=Responsible
A=Accountable
C=Consulted
I=Informed
Every cell cites the governing standard
Facilitated Workshop
COMET's initial assessment maps the organization first — the org chart, every job role, and the tasks each role performs. The facilitated workshop then puts those findings in front of your experts: validating the assessment live, surfacing disagreements, and tailoring every task's delegation level to how the work is actually done.
No documented job roles down to the task level? No problem. The facilitated workshop creates them — COMET drafts a starting taxonomy from whatever documentation exists, and your experts refine it into the real thing, in the room.
1 · ASSESSMENT
Org chart → job roles → tasks per role, mapped by COMET
2 · WORKSHOP
Experts validate findings live, resolve disagreements, tailor every task
3 · DELIVERABLE
Standards-grounded RACI matrix
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.
You walk out with a standards-grounded RACI matrix.
That is not a demo. That is a consulting deliverable.
Latest Articles
Sixty-six published articles on agentic AI, governance frameworks, OT security, and local model fine-tuning. New posts land daily from the R&D Publisher agent.
View BlogAnd Then Someone Has To Build It
COMET decomposes the work — field of work, job roles, the tasks inside each role — and classifies
every task across five delegation levels against industry standards. Voyager is where that assessment is run,
scored and exported. What comes out is not a slide. It is a specific, ordered list of work.
A prioritised list is still only a list.
Most organisations stop here, holding a good answer and no function to act on it — because standing up
research and development has always meant standing up a department.
01 · CONDUCT
The research actually runs
Agents work the prioritised list on a schedule — papers, tools, vendors, disclosures — instead of whenever someone finds an afternoon.
02 · CAPTURE
The evidence is kept, not summarised away
Sources are retained with their provenance, so a finding can be re-examined months later against what it was actually based on.
03 · REPORT
Findings written up and cited
Every claim points at a source you can open and check. That is what makes the answer auditable rather than merely confident.
Conduct, capture, report — that is the loop an R&D department runs. ARKONA runs it continuously against the priorities the decomposition produced, which closes the line of sight the whole method depends on: strategy down to a task, and a measured result back up to the objective that asked for it.
Let's Build Something
Open to senior roles across two tracks — AI/ML research & applied engineering, and cybersecurity / systems-engineering leadership — on teams that ship agentic systems to production. If your team has a hard problem in agent orchestration, AI governance, secure AI systems engineering, or local-model fine-tuning — let’s talk.
Request Ecosystem Access
The ARKONA ecosystem is invite-only. Request an invite code to explore the platform.
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